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 |
|---|---|---|---|---|---|---|---|---|---|---|
MultiHeadAttention | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data.distributed
class ScaledDotProductAttention(nn.Module):
""" Scaled Dot-Product Attention """
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature = temperature
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.... | QiuhongAnnaWei/IBRNet | MultiHeadAttention | false | 14,276 | [
"Apache-2.0"
] | 254 | 6c8b68e6d95eae04535ff0906387ec7899f5d5ce | https://github.com/QiuhongAnnaWei/IBRNet/tree/6c8b68e6d95eae04535ff0906387ec7899f5d5ce |
MixerBlock | import torch
import torch.nn.functional as F
from torch import nn
class FeedForward(nn.Module):
def __init__(self, num_features, expansion_factor, dropout):
super().__init__()
num_hidden = expansion_factor * num_features
self.fc1 = nn.Linear(num_features, num_hidden)
self.fc2 = nn... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.fun... | RAYTRAC3R/mlp-singer | MixerBlock | false | 14,277 | [
"MIT"
] | 82 | a68299b943815353fcc177e4873d24d1d0937cfb | https://github.com/RAYTRAC3R/mlp-singer/tree/a68299b943815353fcc177e4873d24d1d0937cfb |
GPool | from torch.nn import Module
import torch
from torch.nn import Sequential
from torch.nn import Linear
class FullyConnected(torch.nn.Module):
def __init__(self, in_features, out_features, bias=True, activation=None):
super().__init__()
self.linear = torch.nn.Linear(in_features, out_features, bias=b... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | RRemixx/DMRDenoise | GPool | false | 14,278 | [
"MIT"
] | 79 | 026d25f9eaf98fdfd85a67caeb9b49cab71148e9 | https://github.com/RRemixx/DMRDenoise/tree/026d25f9eaf98fdfd85a67caeb9b49cab71148e9 |
Attention | import torch
from typing import Tuple
from torch import nn
class Attention(nn.Module):
"""
Attention network
Parameters
----------
rnn_size : int
Size of Bi-LSTM
"""
def __init__(self, rnn_size: 'int') ->None:
super(Attention, self).__init__()
self.w = nn.Linear(r... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Renovamen/Text-Classification | Attention | false | 14,279 | [
"MIT"
] | 72 | 4a4aa4001c402ed4371ebaabe1393b27794e5992 | https://github.com/Renovamen/Text-Classification/tree/4a4aa4001c402ed4371ebaabe1393b27794e5992 |
Downsampling | from torch.nn import Module
import torch
from torch.nn import Sequential
from torch.nn import Linear
class FullyConnected(torch.nn.Module):
def __init__(self, in_features, out_features, bias=True, activation=None):
super().__init__()
self.linear = torch.nn.Linear(in_features, out_features, bias=b... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | RRemixx/DMRDenoise | Downsampling | false | 14,280 | [
"MIT"
] | 79 | 026d25f9eaf98fdfd85a67caeb9b49cab71148e9 | https://github.com/RRemixx/DMRDenoise/tree/026d25f9eaf98fdfd85a67caeb9b49cab71148e9 |
PositionWiseFeedForward | import torch
from torch import nn
class PositionWiseFeedForward(nn.Module):
"""
Position-Wise Feed-Forward Network
Parameters
----------
d_model : int
Size of word embeddings
hidden_size : int
Size of position-wise feed forward network
dropout : float
Dropout
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Renovamen/Text-Classification | PositionWiseFeedForward | false | 14,281 | [
"MIT"
] | 72 | 4a4aa4001c402ed4371ebaabe1393b27794e5992 | https://github.com/Renovamen/Text-Classification/tree/4a4aa4001c402ed4371ebaabe1393b27794e5992 |
CLSTMCell | import torch
import torch.nn as nn
from torch.autograd import Variable
class CLSTMCell(nn.Module):
def __init__(self, input_channels, hidden_channels, kernel_size, bias=True
):
super(CLSTMCell, self).__init__()
assert hidden_channels % 2 == 0
self.input_channels = input_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.triton_helpers import libdevice
import torch.nn as ... | Rehan-Ahmar/UNet-Zoo | CLSTMCell | false | 14,282 | [
"MIT"
] | 345 | 630f9290d487fda828e7118a3d953575b27a2686 | https://github.com/Rehan-Ahmar/UNet-Zoo/tree/630f9290d487fda828e7118a3d953575b27a2686 |
TorchClampOptionMaxMin | import torch
class TorchClampOptionMaxMin(torch.nn.Module):
def forward(self, x):
return torch.clamp(x, min=-0.1, max=0.1)
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... | PogChamper/torch2trt | TorchClampOptionMaxMin | false | 14,283 | [
"MIT"
] | 3,363 | 43b12627ec0de4d212efb6d02b07570205085ccc | https://github.com/PogChamper/torch2trt/tree/43b12627ec0de4d212efb6d02b07570205085ccc |
RMSEFeaturesLoss | import torch
import torch.nn as nn
import torch.utils.data
def rmseOnFeatures(feature_difference):
gt = torch.zeros_like(feature_difference)
return torch.nn.functional.mse_loss(feature_difference, gt,
size_average=False)
class RMSEFeaturesLoss(nn.Module):
def __init__(self):
super(RMSEF... | 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... | RerRayne/learn3d | RMSEFeaturesLoss | false | 14,284 | [
"MIT"
] | 335 | 83e4ac657c6538fb4cbed6e00b2e3ed6cbf43555 | https://github.com/RerRayne/learn3d/tree/83e4ac657c6538fb4cbed6e00b2e3ed6cbf43555 |
PositionwiseFeedForward | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class PositionwiseFeedForward(nn.Module):
"""Implements FFN equation."""
def __init__(self, d_model, d_ff, dropout=0.1):
super(PositionwiseFeedForward, self).__init__()
self.w_1 = nn.Linear(d_model, d_f... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | RerRayne/learn3d | PositionwiseFeedForward | false | 14,285 | [
"MIT"
] | 335 | 83e4ac657c6538fb4cbed6e00b2e3ed6cbf43555 | https://github.com/RerRayne/learn3d/tree/83e4ac657c6538fb4cbed6e00b2e3ed6cbf43555 |
DecoderLayer | import torch
import torch.nn as nn
import torch.nn.functional as F
class ScaledDotProductAttention(nn.Module):
""" Scaled Dot-Product Attention """
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature = temperature
self.dropout = nn.Dropout(attn_dropo... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Rajathbharadwaj/algorithmic-efficiency | DecoderLayer | false | 14,286 | [
"Apache-2.0"
] | 49 | 47d2928836e0574bc54cc3ad58860dd4daf86cce | https://github.com/Rajathbharadwaj/algorithmic-efficiency/tree/47d2928836e0574bc54cc3ad58860dd4daf86cce |
ProjectionLoss | import math
import torch
import torch.nn as nn
def get_knn_idx_dist(pos: 'torch.FloatTensor', query: 'torch.FloatTensor',
k, offset=0):
"""
:param pos: (B, N, F)
:param query: (B, M, F)
:return knn_idx: (B, M, k)
"""
B, N, F = tuple(pos.size())
M = query.size(1)
pos = pos.u... | 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 math
import tor... | RRemixx/DMRDenoise | ProjectionLoss | false | 14,287 | [
"MIT"
] | 79 | 026d25f9eaf98fdfd85a67caeb9b49cab71148e9 | https://github.com/RRemixx/DMRDenoise/tree/026d25f9eaf98fdfd85a67caeb9b49cab71148e9 |
Attn | import torch
import torch.nn as nn
import torch.nn.functional as F
class Attn(nn.Module):
def __init__(self, hidden_size):
super().__init__()
self.hidden_size = hidden_size
def forward(self, hidden, encoder_output):
attn_energies = torch.sum(hidden * encoder_output, dim=2)
at... | 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
... | RedisAI/redisai-examples | Attn | false | 14,288 | [
"MIT"
] | 51 | c85c755781d4c45443aee0d7d52c306bfda87121 | https://github.com/RedisAI/redisai-examples/tree/c85c755781d4c45443aee0d7d52c306bfda87121 |
EncoderLayer | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data.distributed
class ScaledDotProductAttention(nn.Module):
""" Scaled Dot-Product Attention """
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature = temperature
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.... | QiuhongAnnaWei/IBRNet | EncoderLayer | false | 14,289 | [
"Apache-2.0"
] | 254 | 6c8b68e6d95eae04535ff0906387ec7899f5d5ce | https://github.com/QiuhongAnnaWei/IBRNet/tree/6c8b68e6d95eae04535ff0906387ec7899f5d5ce |
AsymmetricLoss | import torch
import torch.nn as nn
class AsymmetricLoss(nn.Module):
def __init__(self, gamma_neg=4, gamma_pos=1, clip=0.05, eps=1e-08,
disable_torch_grad_focal_loss=True):
super(AsymmetricLoss, self).__init__()
self.gamma_neg = gamma_neg
self.gamma_pos = gamma_pos
self.cli... | 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... | RetroCirce/Zero_Shot_Audio_Source_Separation | AsymmetricLoss | false | 14,290 | [
"MIT"
] | 66 | 16b5c2cc9f263c6d17894d433a2da31b07788f4d | https://github.com/RetroCirce/Zero_Shot_Audio_Source_Separation/tree/16b5c2cc9f263c6d17894d433a2da31b07788f4d |
LayerNorm | import torch
from torch import nn
import torch.utils.data
import torch.optim
import torch.distributions
class LayerNorm(nn.Module):
def __init__(self, channels, eps=0.0001):
super().__init__()
self.channels = channels
self.eps = eps
self.gamma = nn.Parameter(torch.ones(channels))
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
import torch.utils.data
import torch.optim
import torch.di... | Rexiome/NATSpeech | LayerNorm | false | 14,291 | [
"MIT"
] | 561 | 238165e8cd430531b69c484cabb032c1313ee73b | https://github.com/Rexiome/NATSpeech/tree/238165e8cd430531b69c484cabb032c1313ee73b |
MultiLayeredConv1d | import torch
import torch.utils.data
import torch.optim
import torch.distributions
class MultiLayeredConv1d(torch.nn.Module):
"""Multi-layered conv1d for Transformer block.
This is a module of multi-leyered conv1d designed
to replace positionwise feed-forward network
in Transforner block, which is int... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
impor... | Rexiome/NATSpeech | MultiLayeredConv1d | false | 14,293 | [
"MIT"
] | 561 | 238165e8cd430531b69c484cabb032c1313ee73b | https://github.com/Rexiome/NATSpeech/tree/238165e8cd430531b69c484cabb032c1313ee73b |
HighwayNetwork | import torch
from torch import nn
import torch.nn.functional as F
import torch.utils.data
import torch.optim
import torch.distributions
class HighwayNetwork(nn.Module):
def __init__(self, size):
super().__init__()
self.W1 = nn.Linear(size, size)
self.W2 = nn.Linear(size, size)
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 import nn
import t... | Rexiome/NATSpeech | HighwayNetwork | false | 14,294 | [
"MIT"
] | 561 | 238165e8cd430531b69c484cabb032c1313ee73b | https://github.com/Rexiome/NATSpeech/tree/238165e8cd430531b69c484cabb032c1313ee73b |
GramMatrix | import torch
import torch.nn as nn
import torch.utils.data
class GramMatrix(nn.Module):
def forward(self, input):
b, c, h, w = input.size()
F = input.view(b, c, h * w)
G = torch.bmm(F, F.transpose(1, 2))
G.div_(h * w)
return G
def get_inputs():
return [torch.rand([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
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | Reytuag/non-stationary_texture_syn | GramMatrix | false | 14,295 | [
"MIT"
] | 351 | 005d3e4ead3dfa2164b14c5b3bf41cdc15fd3b0b | https://github.com/Reytuag/non-stationary_texture_syn/tree/005d3e4ead3dfa2164b14c5b3bf41cdc15fd3b0b |
PreNet | import torch
from torch import nn
import torch.nn.functional as F
import torch.utils.data
import torch.optim
import torch.distributions
class PreNet(nn.Module):
def __init__(self, in_dims, fc1_dims=256, fc2_dims=128, dropout=0.5):
super().__init__()
self.fc1 = nn.Linear(in_dims, fc1_dims)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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
import t... | Rexiome/NATSpeech | PreNet | false | 14,296 | [
"MIT"
] | 561 | 238165e8cd430531b69c484cabb032c1313ee73b | https://github.com/Rexiome/NATSpeech/tree/238165e8cd430531b69c484cabb032c1313ee73b |
SinusoidalPosEmb | import math
import torch
from torch import nn
import torch.utils.data
import torch.optim
import torch.distributions
class SinusoidalPosEmb(nn.Module):
def __init__(self, dim):
super().__init__()
self.dim = dim
def forward(self, x):
"""
:param x: [B, T]
:return: [B, T... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
import torch.utils.data
import torch.optim
import to... | Rexiome/NATSpeech | SinusoidalPosEmb | false | 14,297 | [
"MIT"
] | 561 | 238165e8cd430531b69c484cabb032c1313ee73b | https://github.com/Rexiome/NATSpeech/tree/238165e8cd430531b69c484cabb032c1313ee73b |
ScaledDotProductAttention | import torch
from torch import nn
from typing import Optional
class ScaledDotProductAttention(nn.Module):
"""
Scaled Dot-Product Attention
Parameters
----------
scale : float
Scale factor (sqrt(d_k))
dropout : float
Dropout
"""
def __init__(self, scale: 'float', drop... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Renovamen/Text-Classification | ScaledDotProductAttention | false | 14,298 | [
"MIT"
] | 72 | 4a4aa4001c402ed4371ebaabe1393b27794e5992 | https://github.com/Renovamen/Text-Classification/tree/4a4aa4001c402ed4371ebaabe1393b27794e5992 |
DICELossMultiClass | import torch
import torch.nn as nn
class DICELossMultiClass(nn.Module):
def __init__(self):
super(DICELossMultiClass, self).__init__()
def forward(self, output, input_mask):
num_classes = output.size(1) - 1
dice_eso = 0
for i in range(num_classes):
probs = torch.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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Rehan-Ahmar/UNet-Zoo | DICELossMultiClass | false | 14,299 | [
"MIT"
] | 345 | 630f9290d487fda828e7118a3d953575b27a2686 | https://github.com/Rehan-Ahmar/UNet-Zoo/tree/630f9290d487fda828e7118a3d953575b27a2686 |
DICELoss | import torch
import torch.nn as nn
class DICELoss(nn.Module):
def __init__(self):
super(DICELoss, self).__init__()
def forward(self, output, mask):
probs = torch.squeeze(output, 1)
mask = torch.squeeze(mask, 1)
intersection = probs * mask
intersection = torch.sum(inte... | 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... | Rehan-Ahmar/UNet-Zoo | DICELoss | false | 14,300 | [
"MIT"
] | 345 | 630f9290d487fda828e7118a3d953575b27a2686 | https://github.com/Rehan-Ahmar/UNet-Zoo/tree/630f9290d487fda828e7118a3d953575b27a2686 |
ClipGlobalAvgPool2d | import torch
import torch.nn as nn
import torch.utils.data
class FastGlobalAvgPool2d(nn.Module):
def __init__(self, flatten=False):
super(FastGlobalAvgPool2d, self).__init__()
self.flatten = flatten
def forward(self, x):
if self.flatten:
in_size = x.size()
ret... | 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... | RichardDominik/AIC21-MTMC | ClipGlobalAvgPool2d | false | 14,301 | [
"MIT"
] | 63 | f69f63f9c40e2dc98e98c7af1cebe3d5605307ee | https://github.com/RichardDominik/AIC21-MTMC/tree/f69f63f9c40e2dc98e98c7af1cebe3d5605307ee |
MultiHeadAttention | import torch
from typing import Tuple
from torch import nn
from typing import Optional
class ScaledDotProductAttention(nn.Module):
"""
Scaled Dot-Product Attention
Parameters
----------
scale : float
Scale factor (sqrt(d_k))
dropout : float
Dropout
"""
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
from torch._inductor.runtime.... | Renovamen/Text-Classification | MultiHeadAttention | false | 14,302 | [
"MIT"
] | 72 | 4a4aa4001c402ed4371ebaabe1393b27794e5992 | https://github.com/Renovamen/Text-Classification/tree/4a4aa4001c402ed4371ebaabe1393b27794e5992 |
GramMSELoss | import torch
import torch.nn as nn
import torch.utils.data
class GramMatrix(nn.Module):
def forward(self, input):
b, c, h, w = input.size()
F = input.view(b, c, h * w)
G = torch.bmm(F, F.transpose(1, 2))
G.div_(h * w)
return G
class GramMSELoss(nn.Module):
def forwa... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | Reytuag/non-stationary_texture_syn | GramMSELoss | false | 14,303 | [
"MIT"
] | 351 | 005d3e4ead3dfa2164b14c5b3bf41cdc15fd3b0b | https://github.com/Reytuag/non-stationary_texture_syn/tree/005d3e4ead3dfa2164b14c5b3bf41cdc15fd3b0b |
EncoderLayer | import torch
from typing import Tuple
from torch import nn
from typing import Optional
class ScaledDotProductAttention(nn.Module):
"""
Scaled Dot-Product Attention
Parameters
----------
scale : float
Scale factor (sqrt(d_k))
dropout : float
Dropout
"""
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
from torch._inductor.runtime.... | Renovamen/Text-Classification | EncoderLayer | false | 14,304 | [
"MIT"
] | 72 | 4a4aa4001c402ed4371ebaabe1393b27794e5992 | https://github.com/Renovamen/Text-Classification/tree/4a4aa4001c402ed4371ebaabe1393b27794e5992 |
LocalSnrLoss | import torch
from torch import Tensor
from torch import nn
from torch.nn import functional as F
class LocalSnrLoss(nn.Module):
def __init__(self, factor: 'float'=1):
super().__init__()
self.factor = factor
def forward(self, input: 'Tensor', target_lsnr: 'Tensor'):
input = input.squee... | 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... | Rikorose/DeepFilterNet | LocalSnrLoss | false | 14,305 | [
"ECL-2.0",
"Apache-2.0",
"MIT"
] | 54 | afe6bfb53efae70207e18df7ed372c2cfe337fee | https://github.com/Rikorose/DeepFilterNet/tree/afe6bfb53efae70207e18df7ed372c2cfe337fee |
FreqUpsample | import torch
from torch import Tensor
from torch import nn
from torch.nn import functional as F
class FreqUpsample(nn.Module):
def __init__(self, factor: 'int', mode='nearest'):
super().__init__()
self.f = float(factor)
self.mode = mode
def forward(self, x: 'Tensor') ->Tensor:
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | Rikorose/DeepFilterNet | FreqUpsample | false | 14,306 | [
"ECL-2.0",
"Apache-2.0",
"MIT"
] | 54 | afe6bfb53efae70207e18df7ed372c2cfe337fee | https://github.com/Rikorose/DeepFilterNet/tree/afe6bfb53efae70207e18df7ed372c2cfe337fee |
TransformerFFNLayer | import torch
from torch import nn
import torch.nn.functional as F
from torch.nn import Linear
import torch.utils.data
import torch.optim
import torch.distributions
def _get_full_incremental_state_key(module_instance, key):
module_name = module_instance.__class__.__name__
if not hasattr(module_instance, '_inst... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | Rexiome/NATSpeech | TransformerFFNLayer | false | 14,307 | [
"MIT"
] | 561 | 238165e8cd430531b69c484cabb032c1313ee73b | https://github.com/Rexiome/NATSpeech/tree/238165e8cd430531b69c484cabb032c1313ee73b |
SiSdr | import torch
from torch import Tensor
from torch import nn
class SiSdr(nn.Module):
def __init__(self):
super().__init__()
def forward(self, input: 'Tensor', target: 'Tensor'):
eps = torch.finfo(input.dtype).eps
t = input.shape[-1]
target = target.reshape(-1, t)
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.triton_helpers import libdevice
from torch import n... | Rikorose/DeepFilterNet | SiSdr | false | 14,308 | [
"ECL-2.0",
"Apache-2.0",
"MIT"
] | 54 | afe6bfb53efae70207e18df7ed372c2cfe337fee | https://github.com/Rikorose/DeepFilterNet/tree/afe6bfb53efae70207e18df7ed372c2cfe337fee |
GeneralizedMeanPooling | import torch
import torch.nn as nn
import torch.utils.data
class GeneralizedMeanPooling(nn.Module):
"""Applies a 2D power-average adaptive pooling over an input signal composed of several input planes.
The function computed is: :math:`f(X) = pow(sum(pow(X, p)), 1/p)`
- At p = infinity, one gets Max Po... | 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... | RichardDominik/AIC21-MTMC | GeneralizedMeanPooling | false | 14,309 | [
"MIT"
] | 63 | f69f63f9c40e2dc98e98c7af1cebe3d5605307ee | https://github.com/RichardDominik/AIC21-MTMC/tree/f69f63f9c40e2dc98e98c7af1cebe3d5605307ee |
ResidualBlock | import torch
from torch import nn
from torch.nn import Linear
from math import sqrt
from torch.nn import Conv1d
import torch.utils.data
import torch.optim
import torch.distributions
class ResidualBlock(nn.Module):
def __init__(self, encoder_hidden, residual_channels, dilation):
super().__init__()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | Rexiome/NATSpeech | ResidualBlock | false | 14,310 | [
"MIT"
] | 561 | 238165e8cd430531b69c484cabb032c1313ee73b | https://github.com/Rexiome/NATSpeech/tree/238165e8cd430531b69c484cabb032c1313ee73b |
GeM | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class GeM(nn.Module):
def __init__(self, p=3.0, eps=1e-06, freeze_p=True):
super(GeM, self).__init__()
self.p = p if freeze_p else Parameter(torch.ones(1) * p)
self.eps = eps
def forward(self, ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import... | RichardDominik/AIC21-MTMC | GeM | false | 14,311 | [
"MIT"
] | 63 | f69f63f9c40e2dc98e98c7af1cebe3d5605307ee | https://github.com/RichardDominik/AIC21-MTMC/tree/f69f63f9c40e2dc98e98c7af1cebe3d5605307ee |
LocallyConnected | import math
import torch
from torch import nn
class LocallyConnected(nn.Module):
"""
Local linear layer, i.e. Conv1dLocal() with filter size 1.
"""
def __init__(self, num_linear: 'int', input_features: 'int',
output_features: 'int', bias: 'bool'=True):
"""
Create local linear ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.as... | Rishab26/causalnex | LocallyConnected | false | 14,312 | [
"Apache-2.0"
] | 1,523 | 127d9324a3d68c1795299c7522f22cdea880f344 | https://github.com/Rishab26/causalnex/tree/127d9324a3d68c1795299c7522f22cdea880f344 |
ComplexMul | import torch
from torch import nn
class ComplexMul(nn.Module):
def forward(self, a, b):
re = a[:, :1] * b[:, :1] - a[:, 1:] * b[:, 1:]
im = a[:, :1] * b[:, 1:] + a[:, :1] * b[:, 1:]
return torch.cat((re, im), dim=1)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | Rikorose/DeepFilterNet | ComplexMul | false | 14,313 | [
"ECL-2.0",
"Apache-2.0",
"MIT"
] | 54 | afe6bfb53efae70207e18df7ed372c2cfe337fee | https://github.com/Rikorose/DeepFilterNet/tree/afe6bfb53efae70207e18df7ed372c2cfe337fee |
LocalLinearCF | import math
import torch
from torch import Tensor
from typing import Optional
from torch import nn
from torch.nn import init
from torch.nn.parameter import Parameter
class LocalLinearCF(nn.Module):
def __init__(self, in_ch: 'int', out_ch: 'int', n_freqs: 'int', bias:
'bool'=True):
super().__init_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
from torch import Tensor
from typing import Optional
from torch impo... | Rikorose/DeepFilterNet | LocalLinearCF | false | 14,314 | [
"ECL-2.0",
"Apache-2.0",
"MIT"
] | 54 | afe6bfb53efae70207e18df7ed372c2cfe337fee | https://github.com/Rikorose/DeepFilterNet/tree/afe6bfb53efae70207e18df7ed372c2cfe337fee |
CrossEntropyLoss | import torch
import torch.nn.functional as F
import torch.nn as nn
def _is_long(x):
return isinstance(x, torch.LongTensor) or isinstance(x, torch.LongTensor)
def onehot(indexes, N=None, ignore_index=None):
"""
Creates a one-representation of indexes with N possible entries
if N is not specified, it ... | 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... | RicJM/weighted_c2d | CrossEntropyLoss | false | 14,315 | [
"MIT"
] | 49 | 38053869b77c1544349c53ba6f3c1325254aa413 | https://github.com/RicJM/weighted_c2d/tree/38053869b77c1544349c53ba6f3c1325254aa413 |
GroupedLinearCF | import math
import torch
from torch import Tensor
from typing import Optional
from torch import nn
from torch.nn import init
from torch.nn.parameter import Parameter
class GroupedLinearCF(nn.Module):
def __init__(self, in_ch: 'int', out_ch: 'int', n_freqs: 'int',
n_groups: 'int', bias: 'bool'=True):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
from torch import Tensor
from typing import Optional
from torch impo... | Rikorose/DeepFilterNet | GroupedLinearCF | false | 14,316 | [
"ECL-2.0",
"Apache-2.0",
"MIT"
] | 54 | afe6bfb53efae70207e18df7ed372c2cfe337fee | https://github.com/Rikorose/DeepFilterNet/tree/afe6bfb53efae70207e18df7ed372c2cfe337fee |
MagCompression | import torch
from torch import Tensor
from torch import nn
from torch.nn.parameter import Parameter
class MagCompression(nn.Module):
def __init__(self, n_freqs: 'int', init_value: 'float'=0.3):
super().__init__()
self.c: 'Tensor'
self.register_parameter('c', Parameter(torch.full((n_freqs,... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import Tensor
from torch import nn
from torch.nn.parameter import Pa... | Rikorose/DeepFilterNet | MagCompression | false | 14,317 | [
"ECL-2.0",
"Apache-2.0",
"MIT"
] | 54 | afe6bfb53efae70207e18df7ed372c2cfe337fee | https://github.com/Rikorose/DeepFilterNet/tree/afe6bfb53efae70207e18df7ed372c2cfe337fee |
DfAlphaLoss | import torch
from torch import Tensor
from typing import Optional
from torch import nn
from typing import Final
class DfAlphaLoss(nn.Module):
"""Add a penalty to use DF for very noisy segments.
Starting from lsnr_thresh, the penalty is increased and has its maximum at lsnr_min.
"""
factor: 'Final[flo... | 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 Tens... | Rikorose/DeepFilterNet | DfAlphaLoss | false | 14,319 | [
"ECL-2.0",
"Apache-2.0",
"MIT"
] | 54 | afe6bfb53efae70207e18df7ed372c2cfe337fee | https://github.com/Rikorose/DeepFilterNet/tree/afe6bfb53efae70207e18df7ed372c2cfe337fee |
AdaptiveAvgMaxPool2d | import torch
import torch.nn as nn
import torch.utils.data
class FastGlobalAvgPool2d(nn.Module):
def __init__(self, flatten=False):
super(FastGlobalAvgPool2d, self).__init__()
self.flatten = flatten
def forward(self, x):
if self.flatten:
in_size = x.size()
ret... | 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... | RichardDominik/AIC21-MTMC | AdaptiveAvgMaxPool2d | false | 14,320 | [
"MIT"
] | 63 | f69f63f9c40e2dc98e98c7af1cebe3d5605307ee | https://github.com/RichardDominik/AIC21-MTMC/tree/f69f63f9c40e2dc98e98c7af1cebe3d5605307ee |
MultiHeadAttention | import math
import torch
from torch import nn
import torch.nn.functional as F
import torch.utils.data
import torch.optim
import torch.distributions
def convert_pad_shape(pad_shape):
l = pad_shape[::-1]
pad_shape = [item for sublist in l for item in sublist]
return pad_shape
class MultiHeadAttention(nn.M... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Rexiome/NATSpeech | MultiHeadAttention | false | 14,321 | [
"MIT"
] | 561 | 238165e8cd430531b69c484cabb032c1313ee73b | https://github.com/Rexiome/NATSpeech/tree/238165e8cd430531b69c484cabb032c1313ee73b |
WeightedFeatureFusion | import torch
import torch.nn as nn
class WeightedFeatureFusion(nn.Module):
def __init__(self, layers, weight=False):
super(WeightedFeatureFusion, self).__init__()
self.layers = layers
self.weight = weight
self.n = len(layers) + 1
if weight:
self.w = nn.Paramete... | 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... | Royzon/YOLOV4_MCMOT | WeightedFeatureFusion | false | 14,322 | [
"MIT"
] | 94 | cd4c8b1b60f9cf809579609caa29d408432845ba | https://github.com/Royzon/YOLOV4_MCMOT/tree/cd4c8b1b60f9cf809579609caa29d408432845ba |
ComplexCompression | from torch.autograd import Function
import torch
from torch import Tensor
from typing import Tuple
from torch import nn
from torch.nn.parameter import Parameter
class angle_re_im(Function):
"""Similar to torch.angle but robustify the gradient for zero magnitude."""
@staticmethod
def forward(ctx, re: 'Ten... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch.... | Rikorose/DeepFilterNet | ComplexCompression | false | 14,324 | [
"ECL-2.0",
"Apache-2.0",
"MIT"
] | 54 | afe6bfb53efae70207e18df7ed372c2cfe337fee | https://github.com/Rikorose/DeepFilterNet/tree/afe6bfb53efae70207e18df7ed372c2cfe337fee |
C3D | import logging
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
class C3D(nn.Module):
def __init__(self, pretrained=None, modality='RGB'):
super(C3D, self).__init__()
self.pretrained = pretrained
self.modality = modality
inplace = True
assert ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 logging
import torch.n... | Lill98/mmaction_custom_data | C3D | false | 14,325 | [
"Apache-2.0"
] | 1,929 | a174e995b78a936a7c80a1feb884cbfa801af740 | https://github.com/Lill98/mmaction_custom_data/tree/a174e995b78a936a7c80a1feb884cbfa801af740 |
sobel_net | import torch
import numpy as np
from torch import nn
import torch.nn.functional as F
class sobel_net(nn.Module):
def __init__(self):
super().__init__()
self.conv_opx = nn.Conv2d(1, 1, 3, bias=False)
self.conv_opy = nn.Conv2d(1, 1, 3, bias=False)
sobel_kernelx = np.array([[-1, 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.... | Rming/DocTr | sobel_net | false | 14,326 | [
"MIT"
] | 111 | e61e3d34f65d1bd70997f2e2e583f640b8779a3c | https://github.com/Rming/DocTr/tree/e61e3d34f65d1bd70997f2e2e583f640b8779a3c |
Head | import torch
from torch import nn
class ResBlock(nn.Module):
def __init__(self, channels):
super(ResBlock, self).__init__()
self.conv1 = nn.Conv2d(channels, channels, kernel_size=5, stride=1,
padding=2, bias=False)
self.bn1 = nn.InstanceNorm2d(channels)
self.relu = nn.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Rming/DocTr | Head | false | 14,327 | [
"MIT"
] | 111 | e61e3d34f65d1bd70997f2e2e583f640b8779a3c | https://github.com/Rming/DocTr/tree/e61e3d34f65d1bd70997f2e2e583f640b8779a3c |
DistanceNetwork | import torch
import torch.nn as nn
import torch.utils.checkpoint
class DistanceNetwork(nn.Module):
def __init__(self, n_feat, p_drop=0.1):
super(DistanceNetwork, self).__init__()
self.proj_symm = nn.Linear(n_feat, 37 * 2)
self.proj_asymm = nn.Linear(n_feat, 37 + 19)
self.reset_par... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.checkpoint
assert_size_stride = torch._... | RosettaCommons/RFDesign | DistanceNetwork | false | 14,328 | [
"MIT"
] | 45 | b404b8b2c57f89c047529c30259aeeb8f6012b61 | https://github.com/RosettaCommons/RFDesign/tree/b404b8b2c57f89c047529c30259aeeb8f6012b61 |
L1_Charbonnier_loss | import torch
from torch.nn import init as init
from torch.nn.modules.loss import _Loss
class L1_Charbonnier_loss(_Loss):
"""
L1 Charbonnierloss
"""
def __init__(self, para):
super(L1_Charbonnier_loss, self).__init__()
self.eps = 0.001
def forward(self, X, Y):
diff = 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
from torch._inductor.runtime.triton_helpers import libdevice
from torch.nn import init as... | RunqiuBao/Event_ESTRNN | L1_Charbonnier_loss | false | 14,329 | [
"MIT"
] | 180 | 6d156cc42a3a33bd0b4b7c4c4be98f943ff53acb | https://github.com/RunqiuBao/Event_ESTRNN/tree/6d156cc42a3a33bd0b4b7c4c4be98f943ff53acb |
ResBlock | import torch
from torch import nn
class ResBlock(nn.Module):
def __init__(self, channels):
super(ResBlock, self).__init__()
self.conv1 = nn.Conv2d(channels, channels, kernel_size=5, stride=1,
padding=2, bias=False)
self.bn1 = nn.InstanceNorm2d(channels)
self.relu = nn.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Rming/DocTr | ResBlock | false | 14,330 | [
"MIT"
] | 111 | e61e3d34f65d1bd70997f2e2e583f640b8779a3c | https://github.com/Rming/DocTr/tree/e61e3d34f65d1bd70997f2e2e583f640b8779a3c |
EncoderLayer | import torch
import torch.nn as nn
import torch.nn.functional as F
class ScaledDotProductAttention(nn.Module):
""" Scaled Dot-Product Attention """
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature = temperature
self.dropout = nn.Dropout(attn_dropo... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Rajathbharadwaj/algorithmic-efficiency | EncoderLayer | false | 14,331 | [
"Apache-2.0"
] | 49 | 47d2928836e0574bc54cc3ad58860dd4daf86cce | https://github.com/Rajathbharadwaj/algorithmic-efficiency/tree/47d2928836e0574bc54cc3ad58860dd4daf86cce |
PSNR | import torch
from torch.nn import init as init
from torch.nn.modules.loss import _Loss
def normalize_reverse(x, centralize=False, normalize=False, val_range=255.0):
if normalize:
x = x * val_range
if centralize:
x = x + val_range / 2
return x
class PSNR(_Loss):
def __init__(self, ce... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch.nn import init as... | RunqiuBao/Event_ESTRNN | PSNR | false | 14,332 | [
"MIT"
] | 180 | 6d156cc42a3a33bd0b4b7c4c4be98f943ff53acb | https://github.com/RunqiuBao/Event_ESTRNN/tree/6d156cc42a3a33bd0b4b7c4c4be98f943ff53acb |
ZeroPad1d | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import optim as optim
import torchvision.transforms.functional as F
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
import torch.utils.checkpoint
class ZeroPad1d(nn.Module):
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
import torch.nn as nn
from torch import optim as optim
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.o... | Maria-philna/unilm | ZeroPad1d | false | 14,333 | [
"MIT"
] | 5,129 | 5550a335c6d2ae5838b1a90e50cb46f81edcd50f | https://github.com/Maria-philna/unilm/tree/5550a335c6d2ae5838b1a90e50cb46f81edcd50f |
UPChannelBAN | import torch
import torch.nn.functional as F
import torch.nn as nn
def xcorr_fast(x, kernel):
"""group conv2d to calculate cross correlation, fast version
"""
batch = kernel.size()[0]
pk = kernel.view(-1, x.size()[1], kernel.size()[2], kernel.size()[3])
px = x.view(1, -1, x.size()[2], x.size()[3])... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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
assert_size_stride = torch... | QiangliangHuang/siamban | UPChannelBAN | false | 14,334 | [
"Apache-2.0"
] | 216 | 940208cb26f8146f87f7534d1674791dcb62468a | https://github.com/QiangliangHuang/siamban/tree/940208cb26f8146f87f7534d1674791dcb62468a |
L1_Charbonnier_loss_color | import torch
from torch.nn import init as init
from torch.nn.modules.loss import _Loss
class L1_Charbonnier_loss_color(_Loss):
"""
L1 Charbonnierloss color
"""
def __init__(self, para):
super(L1_Charbonnier_loss_color, self).__init__()
self.eps = 0.001
def forward(self, X, Y):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch.nn import init as init
from torch.nn.modules.loss import _Loss
asser... | RunqiuBao/Event_ESTRNN | L1_Charbonnier_loss_color | false | 14,335 | [
"MIT"
] | 180 | 6d156cc42a3a33bd0b4b7c4c4be98f943ff53acb | https://github.com/RunqiuBao/Event_ESTRNN/tree/6d156cc42a3a33bd0b4b7c4c4be98f943ff53acb |
TVLoss | import torch
from torch import nn
import torch.utils.data
from torchvision.transforms import *
class TVLoss(nn.Module):
def __init__(self, tv_loss_weight=1):
super(TVLoss, self).__init__()
self.tv_loss_weight = tv_loss_weight
def forward(self, x):
batch_size = x.size()[0]
h_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 import nn
import torch.utils.data
from torchvision.transforms import *
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | RyanMoussouni/iSeeBetter | TVLoss | false | 14,336 | [
"MIT"
] | 327 | af193ae0852f8e477fcd6875dce874eb5092a24a | https://github.com/RyanMoussouni/iSeeBetter/tree/af193ae0852f8e477fcd6875dce874eb5092a24a |
GCN | import torch
import torch.nn as nn
class SwishImplementation(torch.autograd.Function):
@staticmethod
def forward(ctx, i):
result = i * torch.sigmoid(i)
ctx.save_for_backward(i)
return result
@staticmethod
def backward(ctx, grad_output):
i = ctx.saved_variables[0]
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | RuijieJ/pren | GCN | false | 14,337 | [
"Apache-2.0"
] | 64 | 529d4d3366eb1885001200491d3d171d58028f6c | https://github.com/RuijieJ/pren/tree/529d4d3366eb1885001200491d3d171d58028f6c |
PositionalEncoding2D | import torch
import torch.nn as nn
import torch.utils.checkpoint
class PositionalEncoding2D(nn.Module):
def __init__(self, d_model, minpos=-32, maxpos=32, p_drop=0.1):
super(PositionalEncoding2D, self).__init__()
self.minpos = minpos
self.maxpos = maxpos
self.nbin = abs(minpos) + ... | 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... | RosettaCommons/RFDesign | PositionalEncoding2D | false | 14,338 | [
"MIT"
] | 45 | b404b8b2c57f89c047529c30259aeeb8f6012b61 | https://github.com/RosettaCommons/RFDesign/tree/b404b8b2c57f89c047529c30259aeeb8f6012b61 |
Gradient | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init as init
class Gradient(nn.Module):
def __init__(self):
super(Gradient, self).__init__()
kernel_v = [[0, -1, 0], [0, 0, 0], [0, 1, 0]]
kernel_h = [[0, 0, 0], [-1, 0, 1], [0, 0, 0]]
kernel_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.triton_helpers import libdevice
import torch.nn as ... | RunqiuBao/Event_ESTRNN | Gradient | false | 14,339 | [
"MIT"
] | 180 | 6d156cc42a3a33bd0b4b7c4c4be98f943ff53acb | https://github.com/RunqiuBao/Event_ESTRNN/tree/6d156cc42a3a33bd0b4b7c4c4be98f943ff53acb |
GELayerv1 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.utils.data.distributed
class GELayerv1(nn.Module):
def __init__(self):
super(GELayerv1, self).__init__()
self.avg_pool = nn.AvgPool2d(kernel_size=(15, 15), stride=8)
self.sigmod = nn.S... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data.distributed
assert... | SSusantAchary/OctaveConv_pytorch | GELayerv1 | false | 14,340 | [
"MIT"
] | 633 | 079f7da29d55c2eeed8985d33f0b2f765d7a469e | https://github.com/SSusantAchary/OctaveConv_pytorch/tree/079f7da29d55c2eeed8985d33f0b2f765d7a469e |
MatrixArgMax | import torch
import torch.nn as nn
import torch.autograd
class MatrixArgMax(nn.Module):
def __init__(self):
super(MatrixArgMax, self).__init__()
def forward(self, x):
z = torch.argmax(x)
return z
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
r... | 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.autograd
assert_size_stride = torch._C._dynamo.guards.... | RyusukeYamano/nngen | MatrixArgMax | false | 14,341 | [
"Apache-2.0"
] | 207 | 9ed1f7fb83908794aa94d70287d89545d45fe875 | https://github.com/RyusukeYamano/nngen/tree/9ed1f7fb83908794aa94d70287d89545d45fe875 |
FeedForwardLayer | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint
class FeedForwardLayer(nn.Module):
def __init__(self, d_model, r_ff, p_drop=0.1):
super(FeedForwardLayer, self).__init__()
self.norm = nn.LayerNorm(d_model)
self.linear1 = nn.Linear(d_model, 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.... | RosettaCommons/RFDesign | FeedForwardLayer | false | 14,342 | [
"MIT"
] | 45 | b404b8b2c57f89c047529c30259aeeb8f6012b61 | https://github.com/RosettaCommons/RFDesign/tree/b404b8b2c57f89c047529c30259aeeb8f6012b61 |
MeanSquared | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.nn.parallel
def mean_squared(y, target, mask=None):
y = y.softmax(1)
loss = F.mse_loss(y, target, reduction='none').mean(1)
if mask is not None:
loss = mask * loss
return loss.mean()
class MeanSquared(nn.Module):... | 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... | SHI-Labs/Semi-Supervised-Transfer-Learning | MeanSquared | false | 14,343 | [
"MIT"
] | 81 | f206750824ffe10f88a2b418b2b671da61b999f6 | https://github.com/SHI-Labs/Semi-Supervised-Transfer-Learning/tree/f206750824ffe10f88a2b418b2b671da61b999f6 |
MatrixReduceMin | import torch
import torch.nn as nn
import torch.autograd
class MatrixReduceMin(nn.Module):
def __init__(self):
super(MatrixReduceMin, self).__init__()
def forward(self, x):
z = torch.min(x)
return z
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
import torch.nn as nn
import torch.autograd
assert_size_stride = torch._C._dynamo.guards.... | RyusukeYamano/nngen | MatrixReduceMin | false | 14,344 | [
"Apache-2.0"
] | 207 | 9ed1f7fb83908794aa94d70287d89545d45fe875 | https://github.com/RyusukeYamano/nngen/tree/9ed1f7fb83908794aa94d70287d89545d45fe875 |
Upsampler | import math
import torch
import torch.utils.data
from torchvision.transforms import *
class ConvBlock(torch.nn.Module):
def __init__(self, input_size, output_size, kernel_size=3, stride=1,
padding=1, bias=True, activation='prelu', norm=None):
super(ConvBlock, self).__init__()
self.conv = ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.utils.data
from torchvision.transforms import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | RyanMoussouni/iSeeBetter | Upsampler | false | 14,345 | [
"MIT"
] | 327 | af193ae0852f8e477fcd6875dce874eb5092a24a | https://github.com/RyanMoussouni/iSeeBetter/tree/af193ae0852f8e477fcd6875dce874eb5092a24a |
UpBlock | import torch
import torch.utils.data
from torchvision.transforms import *
class ConvBlock(torch.nn.Module):
def __init__(self, input_size, output_size, kernel_size=3, stride=1,
padding=1, bias=True, activation='prelu', norm=None):
super(ConvBlock, self).__init__()
self.conv = torch.nn.Con... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
from torchvision.transforms import *
assert_size_stride ... | RyanMoussouni/iSeeBetter | UpBlock | false | 14,346 | [
"MIT"
] | 327 | af193ae0852f8e477fcd6875dce874eb5092a24a | https://github.com/RyanMoussouni/iSeeBetter/tree/af193ae0852f8e477fcd6875dce874eb5092a24a |
AmdimNCELoss | import torch
from torch import nn as nn
from torch import optim as optim
from math import *
def tanh_clip(x, clip_val=10.0):
"""
soft clip values to the range [-clip_val, +clip_val]
"""
if clip_val is not None:
x_clip = clip_val * torch.tanh(1.0 / clip_val * x)
else:
x_clip = 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.... | SNUHDR2018/ConSSL | AmdimNCELoss | false | 14,347 | [
"MIT"
] | 78 | c7d406d0224e38895986c8fb7281a189e493c982 | https://github.com/SNUHDR2018/ConSSL/tree/c7d406d0224e38895986c8fb7281a189e493c982 |
GELayerv2 | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data.distributed
class GELayerv2(nn.Module):
def __init__(self):
super(GELayerv2, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.sigmod = nn.Sigmoid()
def forward(self, x):
_b, _c, _... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data.distributed
assert_size_stride = torch._C._dynamo.guards.assert_size_... | SSusantAchary/OctaveConv_pytorch | GELayerv2 | false | 14,348 | [
"MIT"
] | 633 | 079f7da29d55c2eeed8985d33f0b2f765d7a469e | https://github.com/SSusantAchary/OctaveConv_pytorch/tree/079f7da29d55c2eeed8985d33f0b2f765d7a469e |
CrossEntropy | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.nn.parallel
def cross_entropy(y, target, mask=None):
if len(target.shape) < 2:
loss = F.cross_entropy(y, target, reduction='none')
else:
loss = -(target * F.log_softmax(y, 1)).sum(1)
if mask is not 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 math as tl_math
import torch.nn.functi... | SHI-Labs/Semi-Supervised-Transfer-Learning | CrossEntropy | false | 14,349 | [
"MIT"
] | 81 | f206750824ffe10f88a2b418b2b671da61b999f6 | https://github.com/SHI-Labs/Semi-Supervised-Transfer-Learning/tree/f206750824ffe10f88a2b418b2b671da61b999f6 |
MatrixConv2dMultiResblock | import torch
import torch.nn as nn
import torch.autograd
class MatrixConv2dMultiResblock(nn.Module):
def __init__(self, weight_shape, stride=1, padding=0, with_batchnorm=
False, act_func='ReLU'):
super(MatrixConv2dMultiResblock, self).__init__()
self.conv1 = nn.Conv2d(weight_shape[3], wei... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | RyusukeYamano/nngen | MatrixConv2dMultiResblock | false | 14,350 | [
"Apache-2.0"
] | 207 | 9ed1f7fb83908794aa94d70287d89545d45fe875 | https://github.com/RyusukeYamano/nngen/tree/9ed1f7fb83908794aa94d70287d89545d45fe875 |
L1GradientLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init as init
from torch.nn.modules.loss import _Loss
class Gradient(nn.Module):
def __init__(self):
super(Gradient, self).__init__()
kernel_v = [[0, -1, 0], [0, 0, 0], [0, 1, 0]]
kernel_h = [[0, 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.... | RunqiuBao/Event_ESTRNN | L1GradientLoss | false | 14,351 | [
"MIT"
] | 180 | 6d156cc42a3a33bd0b4b7c4c4be98f943ff53acb | https://github.com/RunqiuBao/Event_ESTRNN/tree/6d156cc42a3a33bd0b4b7c4c4be98f943ff53acb |
MatrixConv2dResblock | import torch
import torch.nn as nn
import torch.autograd
class MatrixConv2dResblock(nn.Module):
def __init__(self, weight_shape, stride=1, padding=0, with_batchnorm=
False, act_func='ReLU'):
super(MatrixConv2dResblock, self).__init__()
self.conv = nn.Conv2d(weight_shape[3], weight_shape[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
import torch.nn as nn
import ... | RyusukeYamano/nngen | MatrixConv2dResblock | false | 14,352 | [
"Apache-2.0"
] | 207 | 9ed1f7fb83908794aa94d70287d89545d45fe875 | https://github.com/RyusukeYamano/nngen/tree/9ed1f7fb83908794aa94d70287d89545d45fe875 |
MatrixReduceSum | import torch
import torch.nn as nn
import torch.autograd
class MatrixReduceSum(nn.Module):
def __init__(self):
super(MatrixReduceSum, self).__init__()
def forward(self, x):
z = torch.sum(x)
return z
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
import torch.nn as nn
import torch.autograd
assert_size_stride = torch._C._dynamo.guards.... | RyusukeYamano/nngen | MatrixReduceSum | false | 14,353 | [
"Apache-2.0"
] | 207 | 9ed1f7fb83908794aa94d70287d89545d45fe875 | https://github.com/RyusukeYamano/nngen/tree/9ed1f7fb83908794aa94d70287d89545d45fe875 |
GroupedGRUMS | import torch
from torch import Tensor
from typing import List
from typing import Tuple
from torch import nn
from functools import partial
from torch.nn.parameter import Parameter
class GroupedGRULayerMS(nn.Module):
def __init__(self, in_ch: 'int', out_ch: 'int', n_freqs: 'int',
n_groups: 'int', bias: 'bo... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import T... | Rikorose/DeepFilterNet | GroupedGRUMS | false | 14,354 | [
"ECL-2.0",
"Apache-2.0",
"MIT"
] | 54 | afe6bfb53efae70207e18df7ed372c2cfe337fee | https://github.com/Rikorose/DeepFilterNet/tree/afe6bfb53efae70207e18df7ed372c2cfe337fee |
eca_layer | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data.distributed
class eca_layer(nn.Module):
"""Constructs a ECA module.
Args:
channel: Number of channels of the input feature map
k_size: Adaptive selection of kernel size
"""
def __init__(self, channel, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data.distribut... | SSusantAchary/OctaveConv_pytorch | eca_layer | false | 14,355 | [
"MIT"
] | 633 | 079f7da29d55c2eeed8985d33f0b2f765d7a469e | https://github.com/SSusantAchary/OctaveConv_pytorch/tree/079f7da29d55c2eeed8985d33f0b2f765d7a469e |
CustomLoss | import torch
import torch.nn as nn
class CustomLoss(nn.Module):
def __init__(self, weight=None, size_average=True):
super(CustomLoss, self).__init__()
def forward(self, outputs, targets):
gamma = 0.5
C4 = 10
gb_hat = outputs[:, :, :34]
rb_hat = outputs[:, :, 34:68]
... | 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... | Ryuk17/PercepNet | CustomLoss | false | 14,356 | [
"BSD-3-Clause"
] | 170 | 94e91f1db242447593098afc1a844b822e154e09 | https://github.com/Ryuk17/PercepNet/tree/94e91f1db242447593098afc1a844b822e154e09 |
Distribution_Loss | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.nn.parallel
def compute_kernel(x, y):
x_size = x.size(0)
y_size = y.size(0)
dim = x.size(1)
x = x.unsqueeze(1)
y = y.unsqueeze(0)
tiled_x = x.expand(x_size, y_size, dim)
tiled_y = y.expand(x_size, y_size, 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 math as tl_math
import torch.nn.functi... | SHI-Labs/Semi-Supervised-Transfer-Learning | Distribution_Loss | false | 14,357 | [
"MIT"
] | 81 | f206750824ffe10f88a2b418b2b671da61b999f6 | https://github.com/SHI-Labs/Semi-Supervised-Transfer-Learning/tree/f206750824ffe10f88a2b418b2b671da61b999f6 |
D_DownBlock | import torch
import torch.utils.data
from torchvision.transforms import *
class ConvBlock(torch.nn.Module):
def __init__(self, input_size, output_size, kernel_size=3, stride=1,
padding=1, bias=True, activation='prelu', norm=None):
super(ConvBlock, self).__init__()
self.conv = torch.nn.Con... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
from torchvision.transforms import *
assert_size_stride ... | RyanMoussouni/iSeeBetter | D_DownBlock | false | 14,358 | [
"MIT"
] | 327 | af193ae0852f8e477fcd6875dce874eb5092a24a | https://github.com/RyanMoussouni/iSeeBetter/tree/af193ae0852f8e477fcd6875dce874eb5092a24a |
FakeRKHSConvNet | import math
import torch
import numpy as np
from torch import nn as nn
from torch import optim as optim
from math import *
class MaybeBatchNorm2d(nn.Module):
def __init__(self, n_ftr, affine, use_bn):
super(MaybeBatchNorm2d, self).__init__()
self.bn = nn.BatchNorm2d(n_ftr, affine=affine)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | SNUHDR2018/ConSSL | FakeRKHSConvNet | false | 14,359 | [
"MIT"
] | 78 | c7d406d0224e38895986c8fb7281a189e493c982 | https://github.com/SNUHDR2018/ConSSL/tree/c7d406d0224e38895986c8fb7281a189e493c982 |
DownBlock | import torch
import torch.utils.data
from torchvision.transforms import *
class ConvBlock(torch.nn.Module):
def __init__(self, input_size, output_size, kernel_size=3, stride=1,
padding=1, bias=True, activation='prelu', norm=None):
super(ConvBlock, self).__init__()
self.conv = torch.nn.Con... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
from torchvision.transforms import *
assert_size_stride ... | RyanMoussouni/iSeeBetter | DownBlock | false | 14,360 | [
"MIT"
] | 327 | af193ae0852f8e477fcd6875dce874eb5092a24a | https://github.com/RyanMoussouni/iSeeBetter/tree/af193ae0852f8e477fcd6875dce874eb5092a24a |
DecoderLayer | import torch
from torch import nn
class Ffn(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
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.... | Rming/DocTr | DecoderLayer | false | 14,361 | [
"MIT"
] | 111 | e61e3d34f65d1bd70997f2e2e583f640b8779a3c | https://github.com/Rming/DocTr/tree/e61e3d34f65d1bd70997f2e2e583f640b8779a3c |
FirstOctaveConv | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data.distributed
class FirstOctaveConv(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, alpha=0.5,
stride=1, padding=1, dilation=1, groups=1, bias=False):
super(FirstOctaveConv, 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
import torch.nn.parallel
import torch.utils.data.distribut... | SSusantAchary/OctaveConv_pytorch | FirstOctaveConv | false | 14,362 | [
"MIT"
] | 633 | 079f7da29d55c2eeed8985d33f0b2f765d7a469e | https://github.com/SSusantAchary/OctaveConv_pytorch/tree/079f7da29d55c2eeed8985d33f0b2f765d7a469e |
DeNormalize | import torch
import torch.nn as nn
import torch.utils.cpp_extension
class DeNormalize(nn.Module):
def __init__(self, mean, std):
super().__init__()
self.mean = mean
self.std = std
def forward(self, x):
return x.mul(self.std).add(self.mean)
def get_inputs():
return [torc... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.cpp_extension
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = ... | STomoya/animeface | DeNormalize | false | 14,363 | [
"MIT"
] | 61 | 37b3cd26097d7874559d4c152e41e5712b7a1a42 | https://github.com/STomoya/animeface/tree/37b3cd26097d7874559d4c152e41e5712b7a1a42 |
AppendClsToken | import torch
import torch.nn as nn
from functools import partial
import torch.utils.cpp_extension
class AppendClsToken(nn.Module):
def __init__(self, embed_dim, init_func=partial(nn.init.normal_, std=0.02)
):
super().__init__()
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_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
import torch.nn as nn
from functools import partial
import torch.utils.cpp_extension
assert_size_stride = torch._C._dynamo.guards.assert_siz... | STomoya/animeface | AppendClsToken | false | 14,364 | [
"MIT"
] | 61 | 37b3cd26097d7874559d4c152e41e5712b7a1a42 | https://github.com/STomoya/animeface/tree/37b3cd26097d7874559d4c152e41e5712b7a1a42 |
UnBlock | import torch
import torch.nn as nn
import torch.utils.cpp_extension
def unblock(tensor):
"""blocked tensor back to normal"""
B, M, N, C = tensor.size()
H = W = int(M ** 0.5)
patch_size = int(N ** 0.5)
tensor = tensor.reshape(B, H, W, patch_size, patch_size, C)
tensor = tensor.permute(0, 5, 3, ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.cpp_extension
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = ... | STomoya/animeface | UnBlock | false | 14,365 | [
"MIT"
] | 61 | 37b3cd26097d7874559d4c152e41e5712b7a1a42 | https://github.com/STomoya/animeface/tree/37b3cd26097d7874559d4c152e41e5712b7a1a42 |
MiniBatchStd | import torch
import torch.nn as nn
import torch.utils.cpp_extension
class MiniBatchStd(nn.Module):
"""
minibatch standard deviation
"""
def forward(self, x):
std = torch.std(x).expand(x.shape[0], 1, *x.shape[2:])
return torch.cat([x, std], dim=1)
def get_inputs():
return [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
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import... | STomoya/animeface | MiniBatchStd | false | 14,366 | [
"MIT"
] | 61 | 37b3cd26097d7874559d4c152e41e5712b7a1a42 | https://github.com/STomoya/animeface/tree/37b3cd26097d7874559d4c152e41e5712b7a1a42 |
AddPositionEmbed | import torch
import torch.nn as nn
from functools import partial
import torch.utils.cpp_extension
class AddPositionEmbed(nn.Module):
def __init__(self, size, init_func=partial(nn.init.normal_, std=0.02)):
super().__init__()
self.pe = nn.Parameter(torch.zeros(size))
init_func(self.pe)
... | 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 functools import partial
import torch.utils.cpp_extension
assert_size_stride = torch._C._dynamo.guards.assert_siz... | STomoya/animeface | AddPositionEmbed | false | 14,367 | [
"MIT"
] | 61 | 37b3cd26097d7874559d4c152e41e5712b7a1a42 | https://github.com/STomoya/animeface/tree/37b3cd26097d7874559d4c152e41e5712b7a1a42 |
MiniBatchStdDev | import torch
import torch.nn as nn
import torch.utils.cpp_extension
class MiniBatchStdDev(nn.Module):
"""Mini-Batch Standard Deviation"""
def __init__(self, group_size: 'int'=4, eps: 'float'=0.0001) ->None:
super().__init__()
self.group_size = group_size
self.eps = eps
def forwar... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.utils.cpp_extension
assert_size_stride = tor... | STomoya/animeface | MiniBatchStdDev | false | 14,368 | [
"MIT"
] | 61 | 37b3cd26097d7874559d4c152e41e5712b7a1a42 | https://github.com/STomoya/animeface/tree/37b3cd26097d7874559d4c152e41e5712b7a1a42 |
Subspace | import torch
import torch.nn as nn
import torch.utils.cpp_extension
class Subspace(nn.Module):
def __init__(self, latent_dim, channels, resolution):
super().__init__()
self.U = nn.Parameter(torch.empty(latent_dim, channels, resolution,
resolution))
nn.init.orthogonal_(self.U)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.cpp_extension
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = ... | STomoya/animeface | Subspace | false | 14,369 | [
"MIT"
] | 61 | 37b3cd26097d7874559d4c152e41e5712b7a1a42 | https://github.com/STomoya/animeface/tree/37b3cd26097d7874559d4c152e41e5712b7a1a42 |
ChannelPool | import torch
import torch.nn as nn
import torch.nn.functional as F
class ChannelPool(nn.MaxPool1d):
def forward(self, x):
n, c, w, h = x.size()
x = x.view(n, c, w * h).permute(0, 2, 1)
x = x.contiguous()
pooled = F.max_pool1d(x, c, 1)
_, _, c = pooled.size()
pooled... | 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... | Sapio-S/Neural-SLAM | ChannelPool | false | 14,370 | [
"MIT"
] | 171 | 3a1e429fc54fe5682833bfe541512c8d62c2e2f7 | https://github.com/Sapio-S/Neural-SLAM/tree/3a1e429fc54fe5682833bfe541512c8d62c2e2f7 |
MultiQueryAttention | import torch
import torch.nn as nn
import torch.utils.cpp_extension
class MultiQueryAttention(nn.Module):
def __init__(self, dim, latent_dim, num_heads):
super().__init__()
self.dim = dim
self.num_heads = num_heads
self.q = nn.Linear(dim, dim, bias=False)
self.kv = nn.Line... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | STomoya/animeface | MultiQueryAttention | false | 14,371 | [
"MIT"
] | 61 | 37b3cd26097d7874559d4c152e41e5712b7a1a42 | https://github.com/STomoya/animeface/tree/37b3cd26097d7874559d4c152e41e5712b7a1a42 |
PSNR | import torch
import torch.nn as nn
import torch.nn.functional as F
class PSNR(nn.Module):
def __init__(self, max_val=1.0, mode='Y'):
super(PSNR, self).__init__()
self.max_val = max_val
self.mode = mode
def forward(self, x, y):
if self.mode == 'Y' and x.shape[1] == 3 and y.sha... | 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... | S-aiueo32/srntt-pytorch | PSNR | false | 14,372 | [
"Apache-2.0"
] | 88 | 4ea0aa22a54a2d1b1f19c4a43596a693b9e7c067 | https://github.com/S-aiueo32/srntt-pytorch/tree/4ea0aa22a54a2d1b1f19c4a43596a693b9e7c067 |
AdaptiveInstanceNorm | import math
import torch
import torch.nn as nn
import torch.utils.cpp_extension
@torch.no_grad()
def scaling_init(tensor, scale=1, dist='u'):
fan_in, fan_out = nn.init._calculate_fan_in_and_fan_out(tensor)
scale /= (fan_in + fan_out) / 2
if dist == 'n':
std = math.sqrt(scale)
return tensor... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import ... | STomoya/animeface | AdaptiveInstanceNorm | false | 14,373 | [
"MIT"
] | 61 | 37b3cd26097d7874559d4c152e41e5712b7a1a42 | https://github.com/STomoya/animeface/tree/37b3cd26097d7874559d4c152e41e5712b7a1a42 |
DBLoss | import torch
import numpy as np
from torch import nn
class DBLoss(nn.Module):
def __init__(self, alpha=1.0, beta=10.0, ohem_ratio=3):
"""
Implement DB Loss.
:param alpha: loss binary_map 前面的系数
:param beta: loss threshold 前面的系数
:param ohem_ratio: OHEM的比例
"""
... | 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 numpy as np
fro... | SURFZJY/Real-time-Text-Detection | DBLoss | false | 14,374 | [
"Apache-2.0"
] | 65 | b76ee8d840b1fcebf7b9545402907416c7daf24e | https://github.com/SURFZJY/Real-time-Text-Detection/tree/b76ee8d840b1fcebf7b9545402907416c7daf24e |
GeM | import torch
import torch.nn as nn
from torch.nn import functional as F
from torch.nn.parameter import Parameter
def gem(x, p=3, eps=1e-06):
return F.avg_pool2d(x.clamp(min=eps).pow(p), (x.size(-2), x.size(-1))).pow(
1.0 / p)
class GeM(nn.Module):
def __init__(self, p=3, eps=1e-06, p_trainable=True... | 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... | SamYuen101234/Masked_Face_Recognition | GeM | false | 14,375 | [
"MIT"
] | 60 | 2dc572573ebd9ac208314690b529ed69addf0913 | https://github.com/SamYuen101234/Masked_Face_Recognition/tree/2dc572573ebd9ac208314690b529ed69addf0913 |
AdaptiveConv | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.utils.data.distributed
class AdaptiveConv(nn.Module):
def __init__(self, in_channels, out_channels, stride=1, padding=1,
dilation=1, groups=1, bias=False, size=(256, 256)):
super(AdaptiveConv,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | SSusantAchary/OctaveConv_pytorch | AdaptiveConv | false | 14,376 | [
"MIT"
] | 633 | 079f7da29d55c2eeed8985d33f0b2f765d7a469e | https://github.com/SSusantAchary/OctaveConv_pytorch/tree/079f7da29d55c2eeed8985d33f0b2f765d7a469e |
L1Loss | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class L1Loss(nn.Module):
""" A simple mean absolute error (MAE) implementation.
"""
def __init__(self, reduction='mean', **kwargs):
super().__init__()
self.reduction = reduction
def forward(sel... | 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
... | SanghyukChun/rebias | L1Loss | false | 14,377 | [
"MIT"
] | 129 | 6a4f6abdd68e080a08737d93a3c4b43e0f0ce055 | https://github.com/SanghyukChun/rebias/tree/6a4f6abdd68e080a08737d93a3c4b43e0f0ce055 |
EqualizedLinear | import math
import torch
import torch.nn as nn
import torch.utils.cpp_extension
@torch.no_grad()
def scaling_init(tensor, scale=1, dist='u'):
fan_in, fan_out = nn.init._calculate_fan_in_and_fan_out(tensor)
scale /= (fan_in + fan_out) / 2
if dist == 'n':
std = math.sqrt(scale)
return tensor... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.nn as nn
import torch.utils.cpp_extension
assert_size_s... | STomoya/animeface | EqualizedLinear | false | 14,378 | [
"MIT"
] | 61 | 37b3cd26097d7874559d4c152e41e5712b7a1a42 | https://github.com/STomoya/animeface/tree/37b3cd26097d7874559d4c152e41e5712b7a1a42 |
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