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 |
|---|---|---|---|---|---|---|---|---|---|---|
TemperatureHolder | import torch
import torch.nn as nn
class TemperatureHolder(nn.Module):
"""Module that holds a temperature as a learnable value.
Args:
initial_log_temperature (float): Initial value of log(temperature).
"""
def __init__(self, initial_log_temperature=0):
super().__init__()
self... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | imatge-upc/pixelcoordEDL | TemperatureHolder | false | 6,868 | [
"MIT"
] | 1 | 353632feed6ac8c93758c1a2a1b7a477e7ff053c | https://github.com/imatge-upc/pixelcoordEDL/tree/353632feed6ac8c93758c1a2a1b7a477e7ff053c |
AsymmetricLossOptimized | import torch
import torch.nn as nn
class AsymmetricLossOptimized(nn.Module):
""" Notice - optimized version, minimizes memory allocation and gpu uploading,
favors inplace operations"""
def __init__(self, gamma_neg=4, gamma_pos=1, clip=0.05, eps=1e-08,
disable_torch_grad_focal_loss=False):
... | 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... | imvladikon/pytorch-loss | AsymmetricLossOptimized | false | 6,869 | [
"MIT"
] | 1 | 6cfaabe1be898e1ff000b3dffb46d0ef09096f6b | https://github.com/imvladikon/pytorch-loss/tree/6cfaabe1be898e1ff000b3dffb46d0ef09096f6b |
Self_Attentive_Pooling | import torch
import torch.nn as nn
import torch.nn.functional as F
class Self_Attentive_Pooling(nn.Module):
def __init__(self, dim):
"""SAP
Paper: Self-Attentive Speaker Embeddings for Text-Independent Speaker Verification
Link： https://danielpovey.com/files/2018_interspeech_xvector_atten... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | ikunsaikou/lhy_ML2021Spring | Self_Attentive_Pooling | false | 6,870 | [
"WTFPL"
] | 1 | 80d8922077e2f5abba6a440c17654a143ebc8c9c | https://github.com/ikunsaikou/lhy_ML2021Spring/tree/80d8922077e2f5abba6a440c17654a143ebc8c9c |
FCLateActionSAQFunction | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from abc import ABCMeta
from abc import abstractmethod
def init_lecun_normal(tensor, scale=1.0):
"""Initializes the tensor with LeCunNormal."""
fan_in = torch.nn.init._calculate_correct_fan(tensor, 'fan_in')
std = scale ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | imatge-upc/pixelcoordEDL | FCLateActionSAQFunction | false | 6,871 | [
"MIT"
] | 1 | 353632feed6ac8c93758c1a2a1b7a477e7ff053c | https://github.com/imatge-upc/pixelcoordEDL/tree/353632feed6ac8c93758c1a2a1b7a477e7ff053c |
TransformerEncoderLayer | import torch
import torch.nn as nn
class TransformerEncoderLayer(nn.Module):
def __init__(self, d_model, nhead, dim_feedforward=16, dropout=0):
super(TransformerEncoderLayer, self).__init__()
self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
self.linear1 = nn.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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | imperial-qore/CAROL | TransformerEncoderLayer | false | 6,872 | [
"BSD-3-Clause"
] | 1 | 57dc42c4ddeb9e75eed43a91ceb336a1ecc9c8b9 | https://github.com/imperial-qore/CAROL/tree/57dc42c4ddeb9e75eed43a91ceb336a1ecc9c8b9 |
WNConv2d | import torch
import torch.nn as nn
class WNConv2d(nn.Module):
def __init__(self, in_channel, out_channel, kernel_size, stride=1,
padding=0, bias=True, activation=None):
super().__init__()
self.conv = nn.utils.weight_norm(nn.Conv2d(in_channel, out_channel,
kernel_size, stride=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.triton_helpers import libdevice
import torch.nn as ... | imatge-upc/pixelcoordEDL | WNConv2d | false | 6,873 | [
"MIT"
] | 1 | 353632feed6ac8c93758c1a2a1b7a477e7ff053c | https://github.com/imatge-upc/pixelcoordEDL/tree/353632feed6ac8c93758c1a2a1b7a477e7ff053c |
TransformerDecoderLayer | import torch
import torch.nn as nn
class TransformerDecoderLayer(nn.Module):
def __init__(self, d_model, nhead, dim_feedforward=16, dropout=0):
super(TransformerDecoderLayer, self).__init__()
self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
self.multihead_attn = 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.... | imperial-qore/CAROL | TransformerDecoderLayer | false | 6,874 | [
"BSD-3-Clause"
] | 1 | 57dc42c4ddeb9e75eed43a91ceb336a1ecc9c8b9 | https://github.com/imperial-qore/CAROL/tree/57dc42c4ddeb9e75eed43a91ceb336a1ecc9c8b9 |
FocalLossV1 | import torch
import torch.nn as nn
class FocalLossV1(nn.Module):
def __init__(self, alpha=0.25, gamma=2, reduction='mean'):
super(FocalLossV1, self).__init__()
self.alpha = alpha
self.gamma = gamma
self.reduction = reduction
self.crit = nn.BCEWithLogitsLoss(reduction='none... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | imvladikon/pytorch-loss | FocalLossV1 | false | 6,875 | [
"MIT"
] | 1 | 6cfaabe1be898e1ff000b3dffb46d0ef09096f6b | https://github.com/imvladikon/pytorch-loss/tree/6cfaabe1be898e1ff000b3dffb46d0ef09096f6b |
SoftDiceLossV1 | import torch
import torch.nn as nn
class SoftDiceLossV1(nn.Module):
"""
soft-dice loss, useful in binary segmentation
"""
def __init__(self, p=1, smooth=1, reduction='mean'):
super(SoftDiceLossV1, self).__init__()
self.p = p
self.smooth = smooth
self.reduction = reduct... | 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... | imvladikon/pytorch-loss | SoftDiceLossV1 | false | 6,876 | [
"MIT"
] | 1 | 6cfaabe1be898e1ff000b3dffb46d0ef09096f6b | https://github.com/imvladikon/pytorch-loss/tree/6cfaabe1be898e1ff000b3dffb46d0ef09096f6b |
CoordConv2d | import torch
import torch.nn as nn
import torch.nn.functional as F
class CoordConv2d(nn.Conv2d):
def __init__(self, in_chan, out_chan, kernel_size=3, stride=1, padding=
1, dilation=1, groups=1, bias=True):
super(CoordConv2d, self).__init__(in_chan + 2, out_chan,
kernel_size, stride=st... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | imvladikon/pytorch-loss | CoordConv2d | false | 6,877 | [
"MIT"
] | 1 | 6cfaabe1be898e1ff000b3dffb46d0ef09096f6b | https://github.com/imvladikon/pytorch-loss/tree/6cfaabe1be898e1ff000b3dffb46d0ef09096f6b |
EncoderLayer | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class AffineLayer(nn.Module):
def __init__(self, dropout, d_model, d_ff):
super(AffineLayer, self).__init__()
self.w_1 = nn.Linear(d_model, d_ff)
self.w_2 = nn.Linear(d_ff, d_model)
self.dropout = nn.Dr... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | iamxpy/pointer_summarizer | EncoderLayer | false | 6,878 | [
"Apache-2.0"
] | 1 | ebeb2ad32a45162c0da14dac0b6241b0b0d00fa0 | https://github.com/iamxpy/pointer_summarizer/tree/ebeb2ad32a45162c0da14dac0b6241b0b0d00fa0 |
SoftDiceLossV2 | import torch
import torch.nn as nn
import torch.cuda.amp as amp
class SoftDiceLossV2Func(torch.autograd.Function):
"""
compute backward directly for better numeric stability
"""
@staticmethod
@amp.custom_fwd
def forward(ctx, logits, labels, p, smooth):
logits = logits.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
import torch.nn as nn
import torch.cuda.amp as amp
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | imvladikon/pytorch-loss | SoftDiceLossV2 | false | 6,879 | [
"MIT"
] | 1 | 6cfaabe1be898e1ff000b3dffb46d0ef09096f6b | https://github.com/imvladikon/pytorch-loss/tree/6cfaabe1be898e1ff000b3dffb46d0ef09096f6b |
DilConv1dWithGLU | import torch
import torch.nn as nn
import torch.nn.functional as F
class DilConv1dWithGLU(nn.Module):
def __init__(self, num_channels, dilation, lenght=100, kernel_size=2,
activation=F.leaky_relu, residual_connection=True, dropout=0.2):
super(DilConv1dWithGLU, self).__init__()
self.dilati... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | icyray/proGENTRL | DilConv1dWithGLU | false | 6,880 | [
"MIT"
] | 1 | c48305c3411ecb604c4f26f5e6b62f285e42e696 | https://github.com/icyray/proGENTRL/tree/c48305c3411ecb604c4f26f5e6b62f285e42e696 |
CausalConv2d | import torch
import torch.nn as nn
class WNConv2d(nn.Module):
def __init__(self, in_channel, out_channel, kernel_size, stride=1,
padding=0, bias=True, activation=None):
super().__init__()
self.conv = nn.utils.weight_norm(nn.Conv2d(in_channel, out_channel,
kernel_size, stride=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.triton_helpers import libdevice
import torch.nn as ... | imatge-upc/pixelcoordEDL | CausalConv2d | false | 6,881 | [
"MIT"
] | 1 | 353632feed6ac8c93758c1a2a1b7a477e7ff053c | https://github.com/imatge-upc/pixelcoordEDL/tree/353632feed6ac8c93758c1a2a1b7a477e7ff053c |
DY_Conv2d | import torch
import torch.nn as nn
import torch.nn.functional as F
class DY_Conv2d(nn.Conv2d):
def __init__(self, in_chan, out_chan, kernel_size=3, stride=1, padding=
1, dilation=1, groups=1, bias=False, act=nn.ReLU(inplace=True), K=4,
temperature=30, temp_anneal_steps=3000):
super(DY_Con... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | imvladikon/pytorch-loss | DY_Conv2d | false | 6,882 | [
"MIT"
] | 1 | 6cfaabe1be898e1ff000b3dffb46d0ef09096f6b | https://github.com/imvladikon/pytorch-loss/tree/6cfaabe1be898e1ff000b3dffb46d0ef09096f6b |
Conv2dSame | import math
import torch
import torch.nn.functional as F
import torch.nn as nn
from typing import List
from typing import Optional
from typing import Tuple
from torch.jit.annotations import List
def get_same_padding(x: 'int', k: 'int', s: 'int', d: 'int'):
return max((math.ceil(x / s) - 1) * s + (k - 1) * d + 1 -... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.nn.functional as F
import torch.nn as nn
from typing im... | infomon/meta_nas | Conv2dSame | false | 6,883 | [
"Apache-2.0"
] | 1 | b81b7de86d26ae1ec0d6646b4277f3c918e5e35d | https://github.com/infomon/meta_nas/tree/b81b7de86d26ae1ec0d6646b4277f3c918e5e35d |
SelfAttention_naive | import math
import torch
from torch import nn
import torch.nn.functional as F
class SelfAttention_naive(nn.Module):
def __init__(self, dim_emb, dim_internal, heads=8, mask=False, dropout=
0.0, dtype=torch.float32):
"""
A single self attention block
:param dim_emb: embedding dimen... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | insop/transformer_simple | SelfAttention_naive | false | 6,884 | [
"Apache-2.0"
] | 1 | d07e6c3b9ddc9687d332ac3a980bbce22880ad46 | https://github.com/insop/transformer_simple/tree/d07e6c3b9ddc9687d332ac3a980bbce22880ad46 |
MultiHead | import torch
import torch.nn as nn
class Attention(nn.Module):
def __init__(self):
super().__init__()
self.softmax = nn.Softmax(dim=-1)
def forward(self, Q, K, V, mask=None, dk=64):
w = torch.bmm(Q, K.transpose(1, 2))
if mask is not None:
assert w.size() == mask.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.... | iml1111/machine-translation | MultiHead | false | 6,885 | [
"MIT"
] | 1 | a7dd673efbe8a172c1df49e0d50482dc84008c37 | https://github.com/iml1111/machine-translation/tree/a7dd673efbe8a172c1df49e0d50482dc84008c37 |
IOU | import torch
import torch.multiprocessing
def _iou(pred, target, size_average=True):
b = pred.shape[0]
IoU = 0.0
for i in range(0, b):
Iand1 = torch.sum(target[i, :, :, :] * pred[i, :, :, :])
Ior1 = torch.sum(target[i, :, :, :]) + torch.sum(pred[i, :, :, :]
) - Iand1
Io... | 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.multiprocessing
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._e... | intchous/SmartText | IOU | false | 6,886 | [
"MIT"
] | 1 | 81abb84ce135a3859c32257d861c9e87b51f8c3f | https://github.com/intchous/SmartText/tree/81abb84ce135a3859c32257d861c9e87b51f8c3f |
CecaModule | import math
import torch
import torch.nn.functional as F
import torch.nn as nn
class CecaModule(nn.Module):
"""Constructs a circular ECA module.
ECA module where the conv uses circular padding rather than zero padding.
Unlike the spatial dimension, the channels do not have inherent ordering nor
local... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | infomon/meta_nas | CecaModule | false | 6,887 | [
"Apache-2.0"
] | 1 | b81b7de86d26ae1ec0d6646b4277f3c918e5e35d | https://github.com/infomon/meta_nas/tree/b81b7de86d26ae1ec0d6646b4277f3c918e5e35d |
Binarizer | import torch
from abc import ABC
from sklearn.preprocessing import Binarizer
class BaseOperator(ABC):
"""
Abstract class defining the basic structure for operator implementations in Hummingbird.
"""
def __init__(self, regression=False, classification=False, transformer=
False, anomaly_detecti... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from abc import ABC
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_stri... | hannahaih/hummingbird | Binarizer | false | 6,888 | [
"MIT"
] | 1 | b8ec670b3c90ec7e87d3ae4a2b268075bd5eae65 | https://github.com/hannahaih/hummingbird/tree/b8ec670b3c90ec7e87d3ae4a2b268075bd5eae65 |
ActorNetwork | import torch
import torch as T
import torch.nn as nn
import torch.optim as optim
class ActorNetwork(nn.Module):
def __init__(self, alpha, state_dim, action_dim, fc1_dim, fc2_dim):
super(ActorNetwork, self).__init__()
self.fc1 = nn.Linear(state_dim, fc1_dim)
self.ln1 = nn.LayerNorm(fc1_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.... | indigoLovee/TD3 | ActorNetwork | false | 6,889 | [
"MIT"
] | 1 | 0e86a40c27ec376b52e9f8e0e70db28e7411276b | https://github.com/indigoLovee/TD3/tree/0e86a40c27ec376b52e9f8e0e70db28e7411276b |
CriticNetwork | import torch
import torch as T
import torch.nn as nn
import torch.optim as optim
class CriticNetwork(nn.Module):
def __init__(self, beta, state_dim, action_dim, fc1_dim, fc2_dim):
super(CriticNetwork, self).__init__()
self.fc1 = nn.Linear(state_dim + action_dim, fc1_dim)
self.ln1 = nn.Lay... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | indigoLovee/TD3 | CriticNetwork | false | 6,890 | [
"MIT"
] | 1 | 0e86a40c27ec376b52e9f8e0e70db28e7411276b | https://github.com/indigoLovee/TD3/tree/0e86a40c27ec376b52e9f8e0e70db28e7411276b |
ChannelSqueeze | import torch
import torch.nn as nn
def channel_squeeze(x, groups):
"""
Channel squeeze operation.
Parameters:
----------
x : Tensor
Input tensor.
groups : int
Number of groups.
Returns
-------
Tensor
Resulted tensor.
"""
batch, channels, height, wi... | 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... | iofthetiger/pkuad | ChannelSqueeze | false | 6,891 | [
"Apache-2.0"
] | 1 | 07496d108c614c84be028f344830becc9cac8fe5 | https://github.com/iofthetiger/pkuad/tree/07496d108c614c84be028f344830becc9cac8fe5 |
MP | from torch.nn import Module
import torch
import torch.utils.data
from torch.nn import MaxPool2d
class MP(Module):
def __init__(self, k=2):
super().__init__()
self.m = MaxPool2d(kernel_size=k, stride=k)
def forward(self, x):
return self.m(x)
def get_inputs():
return [torch.rand(... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn import Module
import torch.utils.data
from torch.nn import MaxPool2d
assert... | ioangatop/yolo | MP | false | 6,892 | [
"MIT"
] | 1 | c65a72337369572bc07090f39123e2bf6ff5f4a3 | https://github.com/ioangatop/yolo/tree/c65a72337369572bc07090f39123e2bf6ff5f4a3 |
BasicBlockWN | import torch
import torch as t
import torch.nn as nn
from abc import ABC
from torch.nn.utils.weight_norm import weight_norm
def conv1x1(in_planes, out_planes, stride=1):
"""
Create a 1x1 2d convolution block
"""
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride,
bias=False)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ikamensh/machin | BasicBlockWN | false | 6,893 | [
"MIT"
] | 1 | af7b423c47bc1412530cf6c96c11bd3af9b3e239 | https://github.com/ikamensh/machin/tree/af7b423c47bc1412530cf6c96c11bd3af9b3e239 |
Gated_Conv_1d | import torch
import torch.nn as nn
class Gated_Conv_1d(nn.Module):
def __init__(self, channels, kernel_size, stride=1, padding=0, dilation
=1, groups=1, bias=True):
super(Gated_Conv_1d, self).__init__()
self.dilation = dilation
self.channels = channels
self.conv_dil = nn.C... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | ioanvl/wavenet_classifier_torch | Gated_Conv_1d | false | 6,894 | [
"MIT"
] | 1 | de29bfce59d52ae46143f62c4d7a6158a04edf00 | https://github.com/ioanvl/wavenet_classifier_torch/tree/de29bfce59d52ae46143f62c4d7a6158a04edf00 |
IRevInjectivePad | import torch
import torch.nn as nn
class IRevInjectivePad(nn.Module):
"""
i-RevNet channel zero padding block.
Parameters:
----------
padding : int
Size of the padding.
"""
def __init__(self, padding):
super(IRevInjectivePad, self).__init__()
self.padding = paddin... | 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... | iofthetiger/pkuad | IRevInjectivePad | false | 6,895 | [
"Apache-2.0"
] | 1 | 07496d108c614c84be028f344830becc9cac8fe5 | https://github.com/iofthetiger/pkuad/tree/07496d108c614c84be028f344830becc9cac8fe5 |
BERTIntermediate | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
def gelu(x):
"""Implementation of the gelu activation function.
For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
0.5 * x * (1 + torch.tanh(math.sqrt(2 / math... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import ... | DAQuestionAnswering/Bert-n-Pals | BERTIntermediate | false | 6,896 | [
"MIT"
] | 1 | d5a288b9ac62259e70c249635108ba3906e19f00 | https://github.com/DAQuestionAnswering/Bert-n-Pals/tree/d5a288b9ac62259e70c249635108ba3906e19f00 |
Decoder5 | import torch
import torch.nn as nn
class Decoder5(nn.Module):
def __init__(self):
super(Decoder5, self).__init__()
self.reflecPad15 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv15 = nn.Conv2d(512, 512, 3, 1, 0)
self.relu15 = nn.ReLU(inplace=True)
self.unpool = nn.Upsampling... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | hologerry/wct_experiment | Decoder5 | false | 6,897 | [
"MIT"
] | 1 | 890d885561dc8df8c4ae732aebd902aa838257e6 | https://github.com/hologerry/wct_experiment/tree/890d885561dc8df8c4ae732aebd902aa838257e6 |
FirstLSTMAmp | import torch
import torch.nn as nn
class FirstLSTMAmp(nn.Module):
"""
First LSTM amplifier branch.
Parameters:
----------
in_features : int
Number of input channels.
out_features : int
Number of output channels.
"""
def __init__(self, in_features, out_features):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | iofthetiger/pkuad | FirstLSTMAmp | false | 6,898 | [
"Apache-2.0"
] | 1 | 07496d108c614c84be028f344830becc9cac8fe5 | https://github.com/iofthetiger/pkuad/tree/07496d108c614c84be028f344830becc9cac8fe5 |
AlexConv | import torch
import torch.nn as nn
import torch.nn.functional as F
from inspect import isfunction
def get_activation_layer(activation):
"""
Create activation layer from string/function.
Parameters:
----------
activation : function, or str, or nn.Module
Activation function or name of activ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | iofthetiger/pkuad | AlexConv | false | 6,899 | [
"Apache-2.0"
] | 1 | 07496d108c614c84be028f344830becc9cac8fe5 | https://github.com/iofthetiger/pkuad/tree/07496d108c614c84be028f344830becc9cac8fe5 |
Attention | import torch
import torch.nn.functional as F
class Attention(torch.nn.Module):
"""Scaled dot product attention."""
def __init__(self, hidden_dim, **kwargs):
super(Attention, self).__init__(**kwargs)
self.projection_layer = torch.nn.Linear(hidden_dim, 1)
def forward(self, atten_post):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | isspek/Cross-Lingual-Cyberbullying | Attention | false | 6,900 | [
"MIT"
] | 1 | 710c136b9233f0be87af72e43e25722e73158c52 | https://github.com/isspek/Cross-Lingual-Cyberbullying/tree/710c136b9233f0be87af72e43e25722e73158c52 |
MobileNetV3Classifier | import torch
import torch.nn as nn
import torch.nn.functional as F
def conv1x1(in_channels, out_channels, stride=1, groups=1, bias=False):
"""
Convolution 1x1 layer.
Parameters:
----------
in_channels : int
Number of input channels.
out_channels : int
Number of output 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
import ... | iofthetiger/pkuad | MobileNetV3Classifier | false | 6,901 | [
"Apache-2.0"
] | 1 | 07496d108c614c84be028f344830becc9cac8fe5 | https://github.com/iofthetiger/pkuad/tree/07496d108c614c84be028f344830becc9cac8fe5 |
SPHead | import torch
import torch.nn as nn
import torch.nn.functional as F
from inspect import isfunction
def conv1x1(in_channels, out_channels, stride=1, groups=1, bias=False):
"""
Convolution 1x1 layer.
Parameters:
----------
in_channels : int
Number of input channels.
out_channels : 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.nn as nn
import ... | iofthetiger/pkuad | SPHead | false | 6,902 | [
"Apache-2.0"
] | 1 | 07496d108c614c84be028f344830becc9cac8fe5 | https://github.com/iofthetiger/pkuad/tree/07496d108c614c84be028f344830becc9cac8fe5 |
NTXent | import torch
import torch.nn as nn
import torch.nn.functional as F
class NTXent(nn.Module):
def forward(self, z1, z2, t):
batch_size = z1.shape[0]
device = z1.device
z1 = F.normalize(z1, dim=-1)
z2 = F.normalize(z2, dim=-1)
similarity = torch.matmul(z1, z2.T)
simil... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | isaaccorley/contrastive-surface-image-pretraining | NTXent | false | 6,903 | [
"MIT"
] | 1 | a918d4fd3b9cc61ec512af978fb4f086d3b46a70 | https://github.com/isaaccorley/contrastive-surface-image-pretraining/tree/a918d4fd3b9cc61ec512af978fb4f086d3b46a70 |
VectorQuantizer | import torch
import torch.nn as nn
import torch.nn.functional as F
class VectorQuantizer(nn.Module):
def __init__(self, num_embeddings, embedding_dim, commitment_cost):
super(VectorQuantizer, self).__init__()
self._embedding_dim = embedding_dim
self._num_embeddings = num_embeddings
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | imatge-upc/pixelcoordEDL | VectorQuantizer | false | 6,904 | [
"MIT"
] | 1 | 353632feed6ac8c93758c1a2a1b7a477e7ff053c | https://github.com/imatge-upc/pixelcoordEDL/tree/353632feed6ac8c93758c1a2a1b7a477e7ff053c |
NavigatorBranch | import torch
import torch.nn as nn
def conv1x1(in_channels, out_channels, stride=1, groups=1, bias=False):
"""
Convolution 1x1 layer.
Parameters:
----------
in_channels : int
Number of input channels.
out_channels : int
Number of output channels.
stride : int or tuple/list... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | iofthetiger/pkuad | NavigatorBranch | false | 6,905 | [
"Apache-2.0"
] | 1 | 07496d108c614c84be028f344830becc9cac8fe5 | https://github.com/iofthetiger/pkuad/tree/07496d108c614c84be028f344830becc9cac8fe5 |
SCLN | import torch
import torch.nn as nn
class LinearNorm(nn.Module):
""" LinearNorm Projection """
def __init__(self, in_features, out_features, bias=False):
super(LinearNorm, self).__init__()
self.linear = nn.Linear(in_features, out_features, bias)
nn.init.xavier_uniform_(self.linear.weig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | ishine/Cross-Speaker-Emotion-Transfer | SCLN | false | 6,906 | [
"MIT"
] | 1 | 9d38e8058f5abc06167bac244d8ace083e2a6220 | https://github.com/ishine/Cross-Speaker-Emotion-Transfer/tree/9d38e8058f5abc06167bac244d8ace083e2a6220 |
Tile | import torch
import torch.nn as nn
class Tile(nn.Module):
def __init__(self, max_size, dim):
super(Tile, self).__init__()
self.max_size = max_size
self.dim = dim
def forward(self, input):
return input.repeat(*[(self.max_size if x == self.dim else 1) for x in
range... | 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... | ixaxaar/pytorch-npi | Tile | false | 6,907 | [
"MIT"
] | 1 | 50b028840c00f7807fb6490ce6bb0918832dc360 | https://github.com/ixaxaar/pytorch-npi/tree/50b028840c00f7807fb6490ce6bb0918832dc360 |
GlobalAvgPool2d | import torch
import torch.nn as nn
import torch.utils
class GlobalAvgPool2d(nn.Module):
def __init__(self):
"""Global average pooling over the input's spatial dimensions"""
super(GlobalAvgPool2d, self).__init__()
def forward(self, inputs):
in_size = inputs.size()
inputs = inp... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dyna... | jameslong95/FasterSeg | GlobalAvgPool2d | false | 6,908 | [
"MIT"
] | 1 | 872e04964ea46494a6018d9915cee5476e361c27 | https://github.com/jameslong95/FasterSeg/tree/872e04964ea46494a6018d9915cee5476e361c27 |
Vec2ArousalNet | import torch
import torch.utils.data
class Vec2ArousalNet(torch.nn.Module):
def __init__(self, D_in, H, D_out):
super(Vec2ArousalNet, self).__init__()
self.layer_1 = torch.nn.Linear(D_in, H)
self.layer_2 = torch.nn.Linear(H, D_out)
def forward(self, x):
h = self.layer_1(x).cl... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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
asser... | jackvandrunen/hackuci18 | Vec2ArousalNet | false | 6,909 | [
"BSD-2-Clause"
] | 1 | fff3fd7d116a6a83f19229a17377b84922145ebd | https://github.com/jackvandrunen/hackuci18/tree/fff3fd7d116a6a83f19229a17377b84922145ebd |
LinearAttention2d | import torch
class LinearAttention2d(torch.nn.Module):
"""
Linear attention based on parametrized compatibility score function with softmax normalization.
"""
def __init__(self, in_features, out_features):
super(LinearAttention2d, self).__init__()
self.in_features = in_features
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | itsfrank98/CT-COVID | LinearAttention2d | false | 6,911 | [
"MIT"
] | 1 | 3f054000ca0518be2486cf00cfab695b09e39a26 | https://github.com/itsfrank98/CT-COVID/tree/3f054000ca0518be2486cf00cfab695b09e39a26 |
UpConv2x2 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
from torch.cuda import *
class UpConv2x2(nn.Module):
def __init__(self, channels):
super(UpConv2x2, self).__init__()
self.conv = nn.Conv2d(channels, channels // 2, kernel_size=2,
stride=1, paddi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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
from torch.cuda import *
assert_si... | jabae/detectEM | UpConv2x2 | false | 6,912 | [
"MIT"
] | 1 | 2d1a5116164d0bed0a8ea767a227d05a8970a448 | https://github.com/jabae/detectEM/tree/2d1a5116164d0bed0a8ea767a227d05a8970a448 |
MultiheadAttention | import torch
import numpy as np
from typing import Optional
import torch.nn as nn
class MultiheadAttention(nn.Module):
"""Multihead scaled dot-product attention.
"""
def __init__(self, contexts: 'int', queries: 'int', channels: 'int',
heads: 'int'):
"""Initializer.
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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ishine/torch-retriever-vc | MultiheadAttention | false | 6,913 | [
"MIT"
] | 1 | db5119d9d703ea819e2ac9185871ea3db52c14e1 | https://github.com/ishine/torch-retriever-vc/tree/db5119d9d703ea819e2ac9185871ea3db52c14e1 |
Bilinear | import torch
import torch.nn as nn
class Bilinear(nn.Module):
def __init__(self, size):
super(Bilinear, self).__init__()
self.size = size
self.mat = nn.Parameter(torch.FloatTensor(self.size, self.size))
self.reset_parameters()
def reset_parameters(self):
params = [p 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | jalshr21/diora-1 | Bilinear | false | 6,914 | [
"Apache-2.0"
] | 1 | a9b680fde6a840707340e9e8232643b0f0e637bd | https://github.com/jalshr21/diora-1/tree/a9b680fde6a840707340e9e8232643b0f0e637bd |
SigmoidFocalLoss | import torch
import torch.nn as nn
import torch.utils
class SigmoidFocalLoss(nn.Module):
def __init__(self, ignore_label, gamma=2.0, alpha=0.25, reduction='mean'):
super(SigmoidFocalLoss, self).__init__()
self.ignore_label = ignore_label
self.gamma = gamma
self.alpha = alpha
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | jameslong95/FasterSeg | SigmoidFocalLoss | false | 6,915 | [
"MIT"
] | 1 | 872e04964ea46494a6018d9915cee5476e361c27 | https://github.com/jameslong95/FasterSeg/tree/872e04964ea46494a6018d9915cee5476e361c27 |
ScaledDotProductAttention | import torch
import numpy as np
import torch.nn as nn
class ScaledDotProductAttention(nn.Module):
def __init__(self, dropout):
super().__init__()
self.dropout = nn.Dropout(dropout)
def forward(self, q, k, v, mask=None, rpe_q=None, rpe_v=None):
"""
Args:
q: query (... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | jahsylla/stochastic-cslr | ScaledDotProductAttention | false | 6,916 | [
"MIT"
] | 1 | d12d48ebec34183d939917cda2d54f38593dcddb | https://github.com/jahsylla/stochastic-cslr/tree/d12d48ebec34183d939917cda2d54f38593dcddb |
JSloss | import torch
import torch.nn as nn
import torch.nn.functional as F
class JSloss(nn.Module):
""" Compute the Jensen-Shannon loss using the torch native kl_div"""
def __init__(self, reduction='batchmean'):
super().__init__()
self.red = reduction
def forward(self, input, target):
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 torc... | jaredaevans/UltrafastNST | JSloss | false | 6,917 | [
"MIT"
] | 1 | 6671c6b618ce6bb4920b15f782be962e484a5423 | https://github.com/jaredaevans/UltrafastNST/tree/6671c6b618ce6bb4920b15f782be962e484a5423 |
USConv2d | import torch
import torch.nn as nn
import torch.utils
def make_divisible(v, divisor=8, min_value=1):
"""
forked from slim:
https://github.com/tensorflow/models/blob/ 0344c5503ee55e24f0de7f37336a6e08f10976fd/ research/slim/nets/mobilenet/mobilenet.py#L62-L69
"""
if min_value is None:
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils
assert_size_stride = torch._C._dynamo.g... | jameslong95/FasterSeg | USConv2d | false | 6,918 | [
"MIT"
] | 1 | 872e04964ea46494a6018d9915cee5476e361c27 | https://github.com/jameslong95/FasterSeg/tree/872e04964ea46494a6018d9915cee5476e361c27 |
GetStyleLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
def gram_matrix(input):
""" gram matrix for feature assignments """
a, b, c, d = input.size()
allG = []
for i in range(a):
features = input[i].view(b, c * d)
gram = torch.mm(features, features.t())
gram = gram.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
import torch.nn as nn
assert_... | jaredaevans/UltrafastNST | GetStyleLoss | false | 6,919 | [
"MIT"
] | 1 | 6671c6b618ce6bb4920b15f782be962e484a5423 | https://github.com/jaredaevans/UltrafastNST/tree/6671c6b618ce6bb4920b15f782be962e484a5423 |
ConvBlock | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
from torch.cuda import *
def conv3x3(in_channels, out_channels):
return nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1,
padding=1, bias=True)
class ConvBlock(nn.Module):
def __init__(self, in_ch... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | jabae/detectEM | ConvBlock | false | 6,920 | [
"MIT"
] | 1 | 2d1a5116164d0bed0a8ea767a227d05a8970a448 | https://github.com/jabae/detectEM/tree/2d1a5116164d0bed0a8ea767a227d05a8970a448 |
BertAttention | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
class BertAttention(nn.Module):
def __init__(self, config, ctx_dim=None):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
'The 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.... | AsmitaBhat30/lxmert | BertAttention | false | 6,921 | [
"MIT"
] | 1 | 90292dc36a25c04c4f76fe9119e3141d5dc05874 | https://github.com/AsmitaBhat30/lxmert/tree/90292dc36a25c04c4f76fe9119e3141d5dc05874 |
TVLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class TVLoss(nn.Module):
"""L2 total variation loss, as in Mahendran et al."""
def forward(self, input):
input = F.pad(input, (0, 1, 0, 1), 'replicate')
x_diff = input[..., :-1, 1:] - input[..., :-1, :-1]
y_diff = inpu... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | jayChung0302/SideProject-sudalchongbo | TVLoss | false | 6,922 | [
"MIT"
] | 1 | fb0a3d0aee53ba24d3b8ec2dd8c52d0e8f6c33d7 | https://github.com/jayChung0302/SideProject-sudalchongbo/tree/fb0a3d0aee53ba24d3b8ec2dd8c52d0e8f6c33d7 |
FocalLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class FocalSigmoidLossFunc(torch.autograd.Function):
"""
compute backward directly for better numeric stability
"""
@staticmethod
def forward(ctx, logits, label, alpha, gamma):
logits = logits.float()
coeff = 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, math as tl_math
import torc... | jaredaevans/UltrafastNST | FocalLoss | false | 6,923 | [
"MIT"
] | 1 | 6671c6b618ce6bb4920b15f782be962e484a5423 | https://github.com/jaredaevans/UltrafastNST/tree/6671c6b618ce6bb4920b15f782be962e484a5423 |
LogisticRegression | import torch
import torch.nn as nn
class LogisticRegression(nn.Module):
"""
A logistic regression model of the form
P(y = 1 | x) = 1 / (1 + exp(-(mx + b)))
"""
def __init__(self, init_m=1.0, init_b=1.0):
"""
Initialize a logistic regression model by defining its initial
pa... | 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... | jayelm/pytorch-project-template | LogisticRegression | false | 6,924 | [
"MIT"
] | 1 | 30306ce07b21c97c6993432764cbbe0a73092a0c | https://github.com/jayelm/pytorch-project-template/tree/30306ce07b21c97c6993432764cbbe0a73092a0c |
DownConvBlock | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
from torch.cuda import *
def conv3x3(in_channels, out_channels):
return nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1,
padding=1, bias=True)
def maxpool2x2():
return nn.MaxPool2d(kernel_size=2, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | jabae/detectEM | DownConvBlock | false | 6,925 | [
"MIT"
] | 1 | 2d1a5116164d0bed0a8ea767a227d05a8970a448 | https://github.com/jabae/detectEM/tree/2d1a5116164d0bed0a8ea767a227d05a8970a448 |
VariationalLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class VariationalLoss(nn.Module):
""" Variational loss to enforce continuity of images
"""
def forward(self, input):
""" forward pass """
self.loss = F.mse_loss(input[:, :, 1:, :], input[:, :, :-1, :]
) + F.mse... | 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... | jaredaevans/UltrafastNST | VariationalLoss | false | 6,926 | [
"MIT"
] | 1 | 6671c6b618ce6bb4920b15f782be962e484a5423 | https://github.com/jaredaevans/UltrafastNST/tree/6671c6b618ce6bb4920b15f782be962e484a5423 |
TwoHiddenLayerFc | import torch
import torch.nn as nn
import torch.nn.functional as F
class TwoHiddenLayerFc(nn.Module):
def __init__(self, input_shape, out_dim):
super(TwoHiddenLayerFc, self).__init__()
self.fc1 = nn.Linear(input_shape, 200)
self.fc2 = nn.Linear(200, 200)
self.fc3 = nn.Linear(200, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | jasonyanglu/fedavgpy | TwoHiddenLayerFc | false | 6,927 | [
"MIT"
] | 1 | cefbe5854f02d3df1197d849872286439c86e949 | https://github.com/jasonyanglu/fedavgpy/tree/cefbe5854f02d3df1197d849872286439c86e949 |
SoftCrossEntropyLoss2d | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils
class SoftCrossEntropyLoss2d(nn.Module):
def __init__(self):
super(SoftCrossEntropyLoss2d, self).__init__()
def forward(self, inputs, targets):
loss = 0
inputs = -F.log_softmax(inputs, dim=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.... | jameslong95/FasterSeg | SoftCrossEntropyLoss2d | false | 6,928 | [
"MIT"
] | 1 | 872e04964ea46494a6018d9915cee5476e361c27 | https://github.com/jameslong95/FasterSeg/tree/872e04964ea46494a6018d9915cee5476e361c27 |
StyleTrack | import torch
import torch.nn as nn
def gram_matrix(input):
""" gram matrix for feature assignments """
a, b, c, d = input.size()
allG = []
for i in range(a):
features = input[i].view(b, c * d)
gram = torch.mm(features, features.t())
gram = gram.div(c * d)
allG.append(gr... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | jaredaevans/UltrafastNST | StyleTrack | false | 6,929 | [
"MIT"
] | 1 | 6671c6b618ce6bb4920b15f782be962e484a5423 | https://github.com/jaredaevans/UltrafastNST/tree/6671c6b618ce6bb4920b15f782be962e484a5423 |
ReflectPad2d | import torch
class ReflectPad2d(torch.nn.Module):
""" reflectionpad2d that can be transfered across onnx etc
size : int (the size of padding)
"""
def __init__(self, size):
super().__init__()
self.size = size
def forward(self, ins):
size = self.size
l_list, r_l... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | jaredaevans/UltrafastNST | ReflectPad2d | false | 6,930 | [
"MIT"
] | 1 | 6671c6b618ce6bb4920b15f782be962e484a5423 | https://github.com/jaredaevans/UltrafastNST/tree/6671c6b618ce6bb4920b15f782be962e484a5423 |
EncoderImagePrecomp | import torch
import numpy as np
import torch.nn as nn
from collections import OrderedDict
import torch.nn.init
def l2norm(x, dim=-1):
return x / x.norm(2, dim=dim, keepdim=True).clamp(min=1e-06)
class EncoderImagePrecomp(nn.Module):
""" image encoder """
def __init__(self, img_dim, embed_size, no_imgno... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | jefflai108/VGNSL | EncoderImagePrecomp | false | 6,931 | [
"MIT"
] | 1 | 0edc3db3691abbad2a505b2165bd99e7a62d784f | https://github.com/jefflai108/VGNSL/tree/0edc3db3691abbad2a505b2165bd99e7a62d784f |
BehlerAngular | import torch
from torch import nn as nn
class BehlerAngular(nn.Module):
"""
Compute Behler type angular contribution of the angle spanned by three atoms:
:math:`2^{(1-\\zeta)} (1 + \\lambda \\cos( {\\theta}_{ijk} ) )^\\zeta`
Sets of zetas with lambdas of -1 and +1 are generated automatically.
A... | 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 as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._emp... | jduerholt/schnetpack | BehlerAngular | false | 6,932 | [
"MIT"
] | 1 | 228d50fdeba4592b1de54d3a9570d766757c2ee1 | https://github.com/jduerholt/schnetpack/tree/228d50fdeba4592b1de54d3a9570d766757c2ee1 |
Mult | import torch
import torch.utils.data
import torch
from torch import nn
class Mult(nn.Module):
def __init__(self, nc):
super(Mult, self).__init__()
self.register_parameter(name='exp', param=torch.nn.Parameter(torch.
diag(torch.ones(nc)).unsqueeze(-1).unsqueeze(-1)))
"""self.reg... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
impo... | jayin92/vae-pix2pix-terrain-generator | Mult | false | 6,933 | [
"BSD-3-Clause"
] | 1 | 805ea0b053dc9d9c22301af7f536a8fb7e2118d1 | https://github.com/jayin92/vae-pix2pix-terrain-generator/tree/805ea0b053dc9d9c22301af7f536a8fb7e2118d1 |
VectorQuantizeLayer_GB | import torch
from torch import nn
import torch.nn.functional as F
class VectorQuantizeLayer_GB(nn.Module):
def __init__(self, input_dim, vq_size, vq_dim, temp=(1.0, 0.1, 0.99),
groups=1, combine_groups=True, time_first=True, activation=nn.GELU(
), weight_proj_depth=1, weight_proj_factor=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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | jefflai108/Self-Supervised-Speech-Pretraining-and-Representation-Learning | VectorQuantizeLayer_GB | false | 6,934 | [
"MIT"
] | 1 | bb8df008397d5a0360ab7d4b68e91588ed648270 | https://github.com/jefflai108/Self-Supervised-Speech-Pretraining-and-Representation-Learning/tree/bb8df008397d5a0360ab7d4b68e91588ed648270 |
Accuracy | import torch
from torch import nn
def accuracy(logits: 'torch.Tensor', labels: 'torch.Tensor', ignore_index:
'int'=-100) ->torch.Tensor:
with torch.no_grad():
valid_mask = labels != ignore_index
predictions = logits.float().argmax(-1)
correct = (predictions == labels) * valid_mask
... | 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... | jgoodson/TraGeC | Accuracy | false | 6,935 | [
"BSD-3-Clause"
] | 1 | 3370e29ba0639745055cbee726a40181a4dd61df | https://github.com/jgoodson/TraGeC/tree/3370e29ba0639745055cbee726a40181a4dd61df |
ComboLossOnlyPos | import torch
import torch.nn as nn
class SoftDiceLoss(nn.Module):
"""Differentiable soft dice loss.
Note: Sigmoid is automatically applied here!
"""
def __init__(self):
super(SoftDiceLoss, self).__init__()
def forward(self, logits, targets):
eps = 1e-09
num = targets.siz... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | jchen42703/reproducing-cloud-3rd-place | ComboLossOnlyPos | false | 6,936 | [
"Apache-2.0"
] | 1 | 25571f53efd48f68735d7fe2991e3ad783cbd4b1 | https://github.com/jchen42703/reproducing-cloud-3rd-place/tree/25571f53efd48f68735d7fe2991e3ad783cbd4b1 |
MultiLabelDiceLoss | import torch
import torch.nn as nn
class SoftDiceLoss(nn.Module):
"""Differentiable soft dice loss.
Note: Sigmoid is automatically applied here!
"""
def __init__(self):
super(SoftDiceLoss, self).__init__()
def forward(self, logits, targets):
eps = 1e-09
num = targets.siz... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | jchen42703/reproducing-cloud-3rd-place | MultiLabelDiceLoss | false | 6,937 | [
"Apache-2.0"
] | 1 | 25571f53efd48f68735d7fe2991e3ad783cbd4b1 | https://github.com/jchen42703/reproducing-cloud-3rd-place/tree/25571f53efd48f68735d7fe2991e3ad783cbd4b1 |
ConvPlus | import torch
import torch.nn as nn
import torch.utils.data
class ConvPlus(nn.Module):
def __init__(self, c1, c2, k=3, s=1, g=1, bias=True):
super(ConvPlus, self).__init__()
self.cv1 = nn.Conv2d(c1, c2, (k, 1), s, (k // 2, 0), groups=g, bias
=bias)
self.cv2 = nn.Conv2d(c1, c2, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | jiangbestone/detect_rcnn | ConvPlus | false | 6,938 | [
"MIT"
] | 1 | 41c4f4d3f8409cc146314c41a3d02ceafa9a7477 | https://github.com/jiangbestone/detect_rcnn/tree/41c4f4d3f8409cc146314c41a3d02ceafa9a7477 |
PredictionHeadTransform | import math
import torch
import typing
from torch import nn
from torch.nn import LayerNorm
def gelu(x: 'torch.Tensor') ->torch.Tensor:
return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
def swish(x: 'torch.Tensor') ->torch.Tensor:
return x * torch.sigmoid(x)
def get_activation_fn(name: 'str') ->typing... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | jgoodson/TraGeC | PredictionHeadTransform | false | 6,939 | [
"BSD-3-Clause"
] | 1 | 3370e29ba0639745055cbee726a40181a4dd61df | https://github.com/jgoodson/TraGeC/tree/3370e29ba0639745055cbee726a40181a4dd61df |
AddCoords | import torch
import torch.nn as nn
class AddCoords(nn.Module):
def __init__(self, with_r=False):
super().__init__()
self.with_r = with_r
def forward(self, input_tensor):
"""
Args:
input_tensor: shape(batch, channel, x_dim, y_dim)
"""
batch_size, _,... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | jiangxiluning/TCPN | AddCoords | false | 6,940 | [
"Apache-2.0"
] | 1 | 916bd8455be5c784068b7bb5bd6226da3f2d95c7 | https://github.com/jiangxiluning/TCPN/tree/916bd8455be5c784068b7bb5bd6226da3f2d95c7 |
NegativeCosineSimilarity | import torch
import torch.nn.functional as F
class NegativeCosineSimilarity(torch.nn.Module):
"""Implementation of the Negative Cosine Simililarity used in the
SimSiam[0] paper.
[0] SimSiam, 2020, https://arxiv.org/abs/2011.10566
Examples:
>>> # initialize loss function
>>> loss_fn ... | 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._... | jianzhnie/self_supervised | NegativeCosineSimilarity | false | 6,941 | [
"Apache-2.0"
] | 1 | d1e0f31ab032150ab0ad007c1e19773135a5fb79 | https://github.com/jianzhnie/self_supervised/tree/d1e0f31ab032150ab0ad007c1e19773135a5fb79 |
Net | import torch
import torch.utils.data
import torch.utils.data.distributed
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | jesterhazy/sagemaker-pytorch-container | Net | false | 6,942 | [
"Apache-2.0"
] | 1 | 2eb4ba9216e5d72cd4d61eadc173764a41dea6b9 | https://github.com/jesterhazy/sagemaker-pytorch-container/tree/2eb4ba9216e5d72cd4d61eadc173764a41dea6b9 |
GCNModelVAE | import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
class GraphConvolution(nn.Module):
def __init__(self, input_dim, output_dim, dropout, bias=False):
super(GraphConvolution, self).__init__()
self.input_dim = input_dim
self.output_dim = output_dim
self.weig... | 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... | jiangnanboy/gcn_for_prediction_of_protein_interactions | GCNModelVAE | false | 6,943 | [
"Apache-2.0"
] | 1 | b2a9eb06cdfe0971d0c352299db1075ec4827dd9 | https://github.com/jiangnanboy/gcn_for_prediction_of_protein_interactions/tree/b2a9eb06cdfe0971d0c352299db1075ec4827dd9 |
GraphAttentionLayer | import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
import torch.nn.functional as F
class GraphAttentionLayer(nn.Module):
def __init__(self, input_dim, output_dim, dropout, alpha):
super(GraphAttentionLayer, self).__init__()
self.input_dim = input_dim
self.output_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.... | jiangnanboy/gcn_for_prediction_of_protein_interactions | GraphAttentionLayer | false | 6,944 | [
"Apache-2.0"
] | 1 | b2a9eb06cdfe0971d0c352299db1075ec4827dd9 | https://github.com/jiangnanboy/gcn_for_prediction_of_protein_interactions/tree/b2a9eb06cdfe0971d0c352299db1075ec4827dd9 |
ComboLoss | import torch
import torch.nn as nn
class SoftDiceLoss(nn.Module):
"""Differentiable soft dice loss.
Note: Sigmoid is automatically applied here!
"""
def __init__(self):
super(SoftDiceLoss, self).__init__()
def forward(self, logits, targets):
eps = 1e-09
num = targets.siz... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | jchen42703/reproducing-cloud-3rd-place | ComboLoss | false | 6,945 | [
"Apache-2.0"
] | 1 | 25571f53efd48f68735d7fe2991e3ad783cbd4b1 | https://github.com/jchen42703/reproducing-cloud-3rd-place/tree/25571f53efd48f68735d7fe2991e3ad783cbd4b1 |
DomainCNN | import torch
from torch.nn import functional as F
import torch.utils.data
class DomainCNN(torch.nn.Module):
def __init__(self, domains):
super(DomainCNN, self).__init__()
self.conv1 = torch.nn.Conv1d(1, 32, kernel_size=5)
self.pool1 = torch.nn.MaxPool1d(kernel_size=2)
self.conv2 =... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | jenchen1398/artistic-music-style-transfer | DomainCNN | false | 6,946 | [
"BSD-3-Clause"
] | 1 | aa02bcf9c27cb6124c6316a756f7fd77d42be11a | https://github.com/jenchen1398/artistic-music-style-transfer/tree/aa02bcf9c27cb6124c6316a756f7fd77d42be11a |
GeCEmbeddings | from _paritybench_helpers import _mock_config
import torch
import typing
from torch import nn
def create_sinusoidal_embeddings(n_pos, dim, out):
out.requires_grad = False
positions = torch.arange(0, n_pos)[:, None]
dimensions = torch.arange(0, dim)
position_enc = positions / torch.pow(10000, 2 * (dime... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | jgoodson/TraGeC | GeCEmbeddings | false | 6,947 | [
"BSD-3-Clause"
] | 1 | 3370e29ba0639745055cbee726a40181a4dd61df | https://github.com/jgoodson/TraGeC/tree/3370e29ba0639745055cbee726a40181a4dd61df |
RoutingBase | import torch
from torch.nn import functional as F
import torch.nn as nn
def cal_normal(v, dim=-1, keepdim=False):
"""
:return:
"""
normal = torch.sum(v ** 2, dim=dim, keepdim=keepdim) ** 0.5
return normal
def squash(sr, dim=1):
"""
:param dim:
:param sr:(bs, dim)
:return:
"... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | jiangzhiwei2018/Pytorch_CapsNet | RoutingBase | false | 6,948 | [
"Apache-2.0"
] | 1 | b8931d65d5a99a4ff18fd209c16d3ff7d094d1ad | https://github.com/jiangzhiwei2018/Pytorch_CapsNet/tree/b8931d65d5a99a4ff18fd209c16d3ff7d094d1ad |
MCDropout2d | import torch
from torch import Tensor
import torch.nn as nn
from torch.functional import F
import torch.nn.functional as F
class MCDropout2d(nn.Dropout2d):
"""2D dropout that stays on during training and testing
"""
def forward(self, input: 'Tensor') ->Tensor:
return F.dropout2d(input, self.p, 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | jiwoncpark/ex-con | MCDropout2d | false | 6,949 | [
"MIT"
] | 1 | 6775d11ec1c3e7005890e58d16dd07b711861cdf | https://github.com/jiwoncpark/ex-con/tree/6775d11ec1c3e7005890e58d16dd07b711861cdf |
BarlowTwinLoss | import torch
import torch.nn.functional as F
def off_diagonal(x):
"""Return a flattened view of the off-diagonal elements of a square matrix.
>>> x = np.array([[1,2,3],[4,5,6],[7,8,9]])
array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
>>> x.flatten()
array([1, 2, 3, 4, 5, 6, 7, 8,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | jianzhnie/self_supervised | BarlowTwinLoss | false | 6,950 | [
"Apache-2.0"
] | 1 | d1e0f31ab032150ab0ad007c1e19773135a5fb79 | https://github.com/jianzhnie/self_supervised/tree/d1e0f31ab032150ab0ad007c1e19773135a5fb79 |
SamePadConv2d | import torch
from torch.nn import functional as F
import torch.nn as nn
class SamePadConv2d(nn.Conv2d):
"""
Conv with TF padding='same'
https://github.com/pytorch/pytorch/issues/3867#issuecomment-349279036
"""
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
dilation=1... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | jjeamin/obJDetection | SamePadConv2d | false | 6,951 | [
"MIT"
] | 1 | eb7fbc410beb00fad1a6477e827e9ce2d8efbac5 | https://github.com/jjeamin/obJDetection/tree/eb7fbc410beb00fad1a6477e827e9ce2d8efbac5 |
Conv2dWithConstraint | import torch
import torch.nn as nn
class Conv2dWithConstraint(nn.Conv2d):
def __init__(self, *args, max_norm=1, **kwargs):
self.max_norm = max_norm
super(Conv2dWithConstraint, self).__init__(*args, **kwargs)
def forward(self, x):
self.weight.data = torch.renorm(self.weight.data, p=2,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | jiuney/XAI606-EEGNet | Conv2dWithConstraint | false | 6,952 | [
"MIT"
] | 1 | 45ff28630ed1b09d0853f2cfb148a5dd2693e5ab | https://github.com/jiuney/XAI606-EEGNet/tree/45ff28630ed1b09d0853f2cfb148a5dd2693e5ab |
CrossEntropyLossSoft | import torch
class CrossEntropyLossSoft(torch.nn.modules.loss._Loss):
""" inplace distillation for image classification """
def forward(self, output, target):
output_log_prob = torch.nn.functional.log_softmax(output, dim=1)
target = target.unsqueeze(1)
output_log_prob = output_log_pro... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | jiuyecao/Opt-CoInfer | CrossEntropyLossSoft | false | 6,953 | [
"MIT"
] | 1 | 60f29a28c34d3bf9b2f23c98bb8e98caf1abc4f0 | https://github.com/jiuyecao/Opt-CoInfer/tree/60f29a28c34d3bf9b2f23c98bb8e98caf1abc4f0 |
Selector | import torch
import torch.nn as nn
import torch.utils.data
class Selector(nn.Module):
def __init__(self):
super(Selector, self).__init__()
self.conv1 = nn.Conv2d(2048 + 256, 256, 3)
self.relu1 = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(256, 16, 3)
self.relu2 = nn.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
import torch.nn as nn
import ... | hsuanchuu/maskrcnn-benchmark | Selector | false | 6,954 | [
"MIT"
] | 1 | 39429eca800fb912418c34d104ff6f3f2ea07bbd | https://github.com/hsuanchuu/maskrcnn-benchmark/tree/39429eca800fb912418c34d104ff6f3f2ea07bbd |
ShuffleCatChunk | import torch
import torch.nn as nn
class ShuffleCatChunk(nn.Module):
def forward(self, a, b):
assert a.size() == b.size()
_n, c, _h, _w = a.size()
a = torch.chunk(a, chunks=c, dim=1)
b = torch.chunk(b, chunks=c, dim=1)
x = [None] * (c * 2)
x[::2] = a
x[1::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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | jjkennedy3/PINTO_model_zoo | ShuffleCatChunk | false | 6,955 | [
"MIT"
] | 1 | a181c3015a6241873798c4ad3eadd4ce97024f70 | https://github.com/jjkennedy3/PINTO_model_zoo/tree/a181c3015a6241873798c4ad3eadd4ce97024f70 |
ShuffleCat | import torch
import torch.nn as nn
class ShuffleCat(nn.Module):
def forward(self, a, b):
assert a.size() == b.size()
n, c, h, w = a.size()
a = a.permute(0, 2, 3, 1).contiguous().view(-1, c)
b = b.permute(0, 2, 3, 1).contiguous().view(-1, c)
x = torch.cat((a, b), dim=0).tra... | 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... | jjkennedy3/PINTO_model_zoo | ShuffleCat | false | 6,956 | [
"MIT"
] | 1 | a181c3015a6241873798c4ad3eadd4ce97024f70 | https://github.com/jjkennedy3/PINTO_model_zoo/tree/a181c3015a6241873798c4ad3eadd4ce97024f70 |
BatchNormDense | import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
class BatchNormDense(nn.Module):
def __init__(self, num_features, eps=1e-08):
super().__init__()
self.num_features = num_features
self.eps = eps
self.gamma = Parameter(torch.Tensor(num_features))
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 libdevice
import torch.nn as nn
from torch.nn.parameter import Parameter
assert_size_stri... | jkoscialkowski/dnn-exercises | BatchNormDense | false | 6,957 | [
"MIT"
] | 1 | 5d1616fce1b461e39858c68279d2fafefab00a56 | https://github.com/jkoscialkowski/dnn-exercises/tree/5d1616fce1b461e39858c68279d2fafefab00a56 |
BasicBlock | import torch
import torch.nn as nn
import torch.utils.data
def conv1x1(in_planes, out_planes, stride=1):
"""1x1 convolution"""
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride,
bias=False)
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
"""3x3 convolution ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | jinwoo1225/MichiGAN-HAiR | BasicBlock | false | 6,958 | [
"MIT"
] | 1 | dece2ad2e93de3a7c52b4a657ecc0f1a667ccc7e | https://github.com/jinwoo1225/MichiGAN-HAiR/tree/dece2ad2e93de3a7c52b4a657ecc0f1a667ccc7e |
ShuffleCatAlt | import torch
import torch.nn as nn
class ShuffleCatAlt(nn.Module):
def forward(self, a, b):
assert a.size() == b.size()
n, c, h, w = a.size()
x = torch.zeros(n, c * 2, h, w, dtype=a.dtype, device=a.device)
x[:, ::2] = a
x[:, 1::2] = b
return x
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | jjkennedy3/PINTO_model_zoo | ShuffleCatAlt | false | 6,959 | [
"MIT"
] | 1 | a181c3015a6241873798c4ad3eadd4ce97024f70 | https://github.com/jjkennedy3/PINTO_model_zoo/tree/a181c3015a6241873798c4ad3eadd4ce97024f70 |
DummyMCObjective | from torch.nn import Module
import torch
from torch import Tensor
from abc import ABC
from abc import abstractmethod
class AcquisitionObjective(Module, ABC):
"""Abstract base class for objectives."""
...
class MCAcquisitionObjective(AcquisitionObjective):
"""Abstract base class for MC-based objectives."... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
from torch import Tensor
from abc import ABC
from abc import abstractmethod
assert_size_stride = torch._C._dynam... | jmren168/botorch | DummyMCObjective | false | 6,960 | [
"MIT"
] | 1 | 6c067185f56d3a244c4093393b8a97388fb1c0b3 | https://github.com/jmren168/botorch/tree/6c067185f56d3a244c4093393b8a97388fb1c0b3 |
PolicyNetworkGridworld | import torch
import torch.nn as nn
import torch.nn.functional as F
class PolicyNetworkGridworld(nn.Module):
"""
Deep neural network which represents policy network.
"""
def __init__(self, input_size, num_actions):
super(PolicyNetworkGridworld, self).__init__()
self.linear1 = nn.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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | jlebensold/flrl-ddpg | PolicyNetworkGridworld | false | 6,961 | [
"MIT"
] | 1 | d91e9f4aedf48d0614e33bd22c7f684ecda089b1 | https://github.com/jlebensold/flrl-ddpg/tree/d91e9f4aedf48d0614e33bd22c7f684ecda089b1 |
DQNGridworld | import torch
import torch.nn as nn
import torch.nn.functional as F
class DQNGridworld(nn.Module):
"""
Deep neural network with represents an agent.
"""
def __init__(self, input_size, num_actions):
super(DQNGridworld, self).__init__()
self.linear1 = nn.Linear(input_size, 50)
se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | jlebensold/flrl-ddpg | DQNGridworld | false | 6,962 | [
"MIT"
] | 1 | d91e9f4aedf48d0614e33bd22c7f684ecda089b1 | https://github.com/jlebensold/flrl-ddpg/tree/d91e9f4aedf48d0614e33bd22c7f684ecda089b1 |
ProjectionHead | import torch
from torch import nn as nn
class ProjectionHead(nn.Module):
def __init__(self, embedding_dim, projection_dim, dropout):
super().__init__()
self.projection = nn.Linear(embedding_dim, projection_dim)
self.gelu = nn.GELU()
self.fc = nn.Linear(projection_dim, projection_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.triton_helpers import libdevice
from torch import n... | jianzhnie/MultimodalTransformer | ProjectionHead | false | 6,963 | [
"Apache-2.0"
] | 1 | 6cd4ca8034a53da361149745aecead68fbe304a0 | https://github.com/jianzhnie/MultimodalTransformer/tree/6cd4ca8034a53da361149745aecead68fbe304a0 |
FFDNN | import torch
import torch as tc
import torch.nn as nn
class FFDNN(nn.Module):
def __init__(self, insize, action_space):
super(FFDNN, self).__init__()
self.input = nn.Linear(insize, 64)
self.layer1 = nn.Linear(64, 32)
self.layer2 = nn.Linear(32, action_space)
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.triton_helpers import libdevice
import torch.nn as ... | johan-gras/rl-camb-kaggle-connect-x | FFDNN | false | 6,964 | [
"Apache-2.0"
] | 1 | 764463e556c5aea6f61390d2fec83f363510d029 | https://github.com/johan-gras/rl-camb-kaggle-connect-x/tree/764463e556c5aea6f61390d2fec83f363510d029 |
EncoderImagePrecomp | import torch
import numpy as np
from collections import OrderedDict
import torch.nn as nn
import torch.nn.init
def l2norm(X):
"""L2-normalize columns of X
"""
norm = torch.pow(X, 2).sum(dim=1).sqrt()
X = torch.div(X, norm.unsqueeze(1).expand_as(X))
return X
class EncoderImagePrecomp(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 numpy as np
... | joannezhouyi/visual_textual_cross_retrieval | EncoderImagePrecomp | false | 6,965 | [
"Apache-2.0"
] | 1 | 6d5c55a475af74bba63887fff0774d5597830a2b | https://github.com/joannezhouyi/visual_textual_cross_retrieval/tree/6d5c55a475af74bba63887fff0774d5597830a2b |
BatchNormConv | import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
class BatchNormConv(nn.Module):
def __init__(self, num_channels, eps=1e-08):
super().__init__()
self.num_channels = num_channels
self.eps = eps
self.gamma = Parameter(torch.Tensor(num_channels))
se... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
from torch.nn.parameter import Parameter
assert_size_stri... | jkoscialkowski/dnn-exercises | BatchNormConv | false | 6,966 | [
"MIT"
] | 1 | 5d1616fce1b461e39858c68279d2fafefab00a56 | https://github.com/jkoscialkowski/dnn-exercises/tree/5d1616fce1b461e39858c68279d2fafefab00a56 |
LinearWithConstraint | import torch
import torch.nn as nn
class LinearWithConstraint(nn.Linear):
def __init__(self, *args, max_norm=1, **kwargs):
self.max_norm = max_norm
super(LinearWithConstraint, self).__init__(*args, **kwargs)
def forward(self, x):
self.weight.data = torch.renorm(self.weight.data, p=2,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | jiuney/XAI606-EEGNet | LinearWithConstraint | false | 6,967 | [
"MIT"
] | 1 | 45ff28630ed1b09d0853f2cfb148a5dd2693e5ab | https://github.com/jiuney/XAI606-EEGNet/tree/45ff28630ed1b09d0853f2cfb148a5dd2693e5ab |
patch_extractor | import torch
from torch import nn
class patch_extractor(nn.Module):
"""
Module for creating custom patch extractor
"""
def __init__(self, patch_size, pad=False, center=False, dim=2):
super(patch_extractor, self).__init__()
self.dim = dim
self.im2pat = nn.Unfold(kernel_size=pat... | 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... | johertrich/Wasserstein_Patch_Prior | patch_extractor | false | 6,968 | [
"MIT"
] | 1 | 70877a6f1031e51b7868984b97027951d1d190d3 | https://github.com/johertrich/Wasserstein_Patch_Prior/tree/70877a6f1031e51b7868984b97027951d1d190d3 |
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