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 |
|---|---|---|---|---|---|---|---|---|---|---|
CharbonnierLoss | import torch
import torch.utils.data
import torch.nn as nn
import torch.nn
class CharbonnierLoss(nn.Module):
"""Charbonnier Loss (L1)"""
def __init__(self, eps=0.001):
super(CharbonnierLoss, self).__init__()
self.eps = eps
def forward(self, x, y):
diff = x - y
loss = 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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
impo... | IceClear/MW-GAN | CharbonnierLoss | false | 8,288 | [
"MIT"
] | 36 | acb962468c984681c4a21f7b5c14588ca8f58c00 | https://github.com/IceClear/MW-GAN/tree/acb962468c984681c4a21f7b5c14588ca8f58c00 |
Transform | import torch
import torch.nn as nn
def calc_mean_std(feat, eps=1e-05):
size = feat.size()
assert len(size) == 4
N, C = size[:2]
feat_var = feat.view(N, C, -1).var(dim=2) + eps
feat_std = feat_var.sqrt().view(N, C, 1, 1)
feat_mean = feat.view(N, C, -1).mean(dim=2).view(N, C, 1, 1)
return fe... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | HalbertCH/IEContraAST | Transform | false | 8,289 | [
"MIT"
] | 39 | 50ee949f5302a7e4a3cae3226610c03462093c21 | https://github.com/HalbertCH/IEContraAST/tree/50ee949f5302a7e4a3cae3226610c03462093c21 |
EuclideanLoss | import torch
import torch.nn as nn
class EuclideanLoss(nn.Module):
def __init__(self):
super(EuclideanLoss, self).__init__()
def forward(self, pre, gt):
N = pre.shape[0]
diff = torch.sum((pre - gt).pow(2)) / (N * 2)
return diff
def get_inputs():
return [torch.rand([4, 4... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | IndigoPurple/EFENet | EuclideanLoss | false | 8,290 | [
"MIT"
] | 11 | e88234486f19534274a0a20badc251788ac67e31 | https://github.com/IndigoPurple/EFENet/tree/e88234486f19534274a0a20badc251788ac67e31 |
loss_Textures | import torch
import torch.utils.data
import torch.nn as nn
import torch.nn
class loss_Textures(nn.Module):
def __init__(self, nc=1, alpha=1.2, margin=0):
super(loss_Textures, self).__init__()
self.nc = nc
self.alpha = alpha
self.margin = margin
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 import triton_helpers
import torch.utils.data
import torch.nn as nn
import torch.nn
assert_size_stride = torch.... | IceClear/MW-GAN | loss_Textures | false | 8,291 | [
"MIT"
] | 36 | acb962468c984681c4a21f7b5c14588ca8f58c00 | https://github.com/IceClear/MW-GAN/tree/acb962468c984681c4a21f7b5c14588ca8f58c00 |
BilinearMatrixAttention | import torch
import torch.nn as nn
class BilinearMatrixAttention(nn.Module):
"""
Adopted from AllenNLP. For now there is no activation function
"""
def __init__(self, matrix_1_dim: 'int', matrix_2_dim: 'int',
use_input_biases: 'bool'=False, label_dim: 'int'=1) ->None:
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 torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Impavidity/relogic | BilinearMatrixAttention | false | 8,292 | [
"MIT"
] | 24 | f647106e143cd603b95b63e06ea530cdd516aefe | https://github.com/Impavidity/relogic/tree/f647106e143cd603b95b63e06ea530cdd516aefe |
CharbonnierLoss | import torch
import torch.nn as nn
class CharbonnierLoss(nn.Module):
def __init__(self):
super(CharbonnierLoss, self).__init__()
def forward(self, pre, gt):
N = pre.shape[0]
diff = torch.sum(torch.sqrt((pre - gt).pow(2) + 0.001 ** 2)) / N
return diff
def get_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
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | IndigoPurple/EFENet | CharbonnierLoss | false | 8,293 | [
"MIT"
] | 11 | e88234486f19534274a0a20badc251788ac67e31 | https://github.com/IndigoPurple/EFENet/tree/e88234486f19534274a0a20badc251788ac67e31 |
ConvLayer | import torch
import torch.nn as nn
class ConvLayer(nn.Module):
def __init__(self, in_channels: 'int', out_channels: 'int', kernel_size:
'int', stride: 'int'):
super().__init__()
self._conv = nn.Conv2d(in_channels=in_channels, out_channels=
out_channels, kernel_size=kernel_size... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.... | Inkln/StyleTransferWithCatalyst | ConvLayer | false | 8,294 | [
"Apache-2.0"
] | 11 | c3181ecdfd32160907efc2d9d917a55925c25c11 | https://github.com/Inkln/StyleTransferWithCatalyst/tree/c3181ecdfd32160907efc2d9d917a55925c25c11 |
HardMish | import torch
from torch import nn as nn
def hard_mish(x, inplace: 'bool'=False):
""" Hard Mish
Experimental, based on notes by Mish author Diganta Misra at
https://github.com/digantamisra98/H-Mish/blob/0da20d4bc58e696b6803f2523c58d3c8a82782d0/README.md
"""
if inplace:
return x.mul_(0.5 *... | 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 as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_strid... | JDAI-CV/CoTNet-ObjectDetection-InstanceSegmentation | HardMish | false | 8,295 | [
"Apache-2.0"
] | 34 | 2a546ef946989fc5bac8d819b3c93a9fdc83f241 | https://github.com/JDAI-CV/CoTNet-ObjectDetection-InstanceSegmentation/tree/2a546ef946989fc5bac8d819b3c93a9fdc83f241 |
SpatialPyramidPooling | import torch
import torch.nn as nn
class SpatialPyramidPooling(nn.Module):
def __init__(self, pool_sizes=[5, 9, 13]):
super(SpatialPyramidPooling, self).__init__()
self.maxpools = nn.ModuleList([nn.MaxPool2d(pool_size, 1, pool_size //
2) for pool_size in pool_sizes])
def forward(... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | IDayday/YOLOv4_CAM | SpatialPyramidPooling | false | 8,296 | [
"Apache-2.0"
] | 34 | 8df61f1c59c197126f0385c1ec1cf65a29a80cec | https://github.com/IDayday/YOLOv4_CAM/tree/8df61f1c59c197126f0385c1ec1cf65a29a80cec |
decoder4 | import torch
from torch import nn
class decoder4(nn.Module):
def __init__(self):
super(decoder4, self).__init__()
self.reflecPad11 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv11 = nn.Conv2d(512, 256, 3, 1, 0)
self.relu11 = nn.ReLU(inplace=True)
self.unpool = nn.UpsamplingN... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Holmes-Alan/RefVAE | decoder4 | false | 8,297 | [
"MIT"
] | 13 | 836b8f1168f1b0f923b609a48e202ace7806f79c | https://github.com/Holmes-Alan/RefVAE/tree/836b8f1168f1b0f923b609a48e202ace7806f79c |
Normalization | import torch
import torch.nn as nn
class Normalization(nn.Module):
def __init__(self):
super(Normalization, self).__init__()
self.mean = nn.Parameter(torch.tensor([0.485, 0.456, 0.406]).view(-
1, 1, 1))
self.std = nn.Parameter(torch.tensor([0.329, 0.224, 0.225]).view(-1,
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Inkln/StyleTransferWithCatalyst | Normalization | false | 8,298 | [
"Apache-2.0"
] | 11 | c3181ecdfd32160907efc2d9d917a55925c25c11 | https://github.com/Inkln/StyleTransferWithCatalyst/tree/c3181ecdfd32160907efc2d9d917a55925c25c11 |
GroupNorm32 | import torch
import torch.nn.functional as F
from torch import nn
class GroupNorm32(nn.GroupNorm):
def __init__(self, num_groups, num_channels, swish, eps=1e-05):
super().__init__(num_groups=num_groups, num_channels=num_channels,
eps=eps)
self.swish = swish
def forward(self, x):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Jack000/glid-3 | GroupNorm32 | false | 8,299 | [
"MIT"
] | 31 | 4a18efc2785339ebc743e149a7955e34fff436fb | https://github.com/Jack000/glid-3/tree/4a18efc2785339ebc743e149a7955e34fff436fb |
ImageGradients | import torch
import torch as th
import torch.utils.data
class ImageGradients(th.nn.Module):
"""
Args:
c_in(int): number of channels expected in the images.
use_sobel(bool): if True, uses a (smoother) Sobel filter instead of simple
finite differences.
"""
def __init__(self, c_in, use_sobel=T... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch as th
import torch.utils.data
assert_size_stride = torch._C._dynamo... | IlyaBizyaev/ttools | ImageGradients | false | 8,300 | [
"MIT"
] | 11 | b1435b19f397ce1baff9daed3cb287e52a029fdb | https://github.com/IlyaBizyaev/ttools/tree/b1435b19f397ce1baff9daed3cb287e52a029fdb |
decoder3 | import torch
from torch import nn
class decoder3(nn.Module):
def __init__(self):
super(decoder3, self).__init__()
self.reflecPad7 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv7 = nn.Conv2d(256, 128, 3, 1, 0)
self.relu7 = nn.ReLU(inplace=True)
self.unpool = nn.UpsamplingNear... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Holmes-Alan/RefVAE | decoder3 | false | 8,301 | [
"MIT"
] | 13 | 836b8f1168f1b0f923b609a48e202ace7806f79c | https://github.com/Holmes-Alan/RefVAE/tree/836b8f1168f1b0f923b609a48e202ace7806f79c |
CMMD | import torch
import torch.nn as nn
import torch.nn.functional as F
class CMMD(nn.Module):
def __init__(self, num_pos):
super(CMMD, self).__init__()
self.num_pos = num_pos
def forward(self, feat_v, feat_t):
feat_v = feat_v.view(feat_v.size(0), -1)
feat_v = F.normalize(feat_v, ... | 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... | JDAI-CV/CM-NAS | CMMD | false | 8,302 | [
"Apache-2.0"
] | 31 | bbc77f427b2c8afb9f3865f5a04e86079d33dd28 | https://github.com/JDAI-CV/CM-NAS/tree/bbc77f427b2c8afb9f3865f5a04e86079d33dd28 |
qy | import torch
import torch.nn as nn
import torch.nn.functional as F
def weights_init(m):
if isinstance(m, nn.Conv2d):
nn.init.kaiming_uniform_(m.weight, a=0, mode='fan_in', nonlinearity
='leaky_relu')
try:
nn.init.constant_(m.bias, 0.01)
except:
pass
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | IamWangYunKai/DG-TrajGen | qy | false | 8,303 | [
"MIT"
] | 31 | 0a8aab7e1c05111a5afe43d53801c55942e9ff56 | https://github.com/IamWangYunKai/DG-TrajGen/tree/0a8aab7e1c05111a5afe43d53801c55942e9ff56 |
decoder6 | import torch
from torch import nn
class decoder6(nn.Module):
def __init__(self):
super(decoder6, self).__init__()
self.reflecPad11 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv11 = nn.Conv2d(512, 256, 3, 1, 0)
self.relu11 = nn.ReLU(inplace=True)
self.unpool1 = nn.ConvTransp... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Holmes-Alan/RefVAE | decoder6 | false | 8,304 | [
"MIT"
] | 13 | 836b8f1168f1b0f923b609a48e202ace7806f79c | https://github.com/Holmes-Alan/RefVAE/tree/836b8f1168f1b0f923b609a48e202ace7806f79c |
SP | import torch
import torch.nn as nn
import torch.nn.functional as F
class SP(nn.Module):
def __init__(self):
super(SP, self).__init__()
def forward(self, feat_v, feat_t):
feat_v = feat_v.view(feat_v.size(0), -1)
G_v = torch.mm(feat_v, feat_v.t())
norm_G_v = F.normalize(G_v, p=... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | JDAI-CV/CM-NAS | SP | false | 8,305 | [
"Apache-2.0"
] | 31 | bbc77f427b2c8afb9f3865f5a04e86079d33dd28 | https://github.com/JDAI-CV/CM-NAS/tree/bbc77f427b2c8afb9f3865f5a04e86079d33dd28 |
PSNR | import torch
import torch as th
import torch.utils.data
class PSNR(th.nn.Module):
def __init__(self):
super(PSNR, self).__init__()
self.mse = th.nn.MSELoss()
def forward(self, out, ref):
mse = self.mse(out, ref)
return -10 * th.log10(mse + 1e-12)
def get_inputs():
retur... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch as th
import to... | IlyaBizyaev/ttools | PSNR | false | 8,306 | [
"MIT"
] | 11 | b1435b19f397ce1baff9daed3cb287e52a029fdb | https://github.com/IlyaBizyaev/ttools/tree/b1435b19f397ce1baff9daed3cb287e52a029fdb |
BicubicUpsampler | import torch
import torch as th
import torch.utils.data
class BicubicUpsampler(th.nn.Module):
def __init__(self, scale=2, channels=1):
super(BicubicUpsampler, self).__init__()
ksize = 2 * scale * 2
total_pad = ksize - scale // 2
if scale % 2 == 1:
ksize += 1
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 as th
import torch.utils.data
assert_size_stride = torch._C._dynamo... | IlyaBizyaev/ttools | BicubicUpsampler | false | 8,307 | [
"MIT"
] | 11 | b1435b19f397ce1baff9daed3cb287e52a029fdb | https://github.com/IlyaBizyaev/ttools/tree/b1435b19f397ce1baff9daed3cb287e52a029fdb |
FCChain | import torch
import torch.utils.data
import torch.nn as nn
def _get_activation(activation):
valid = ['relu', 'leaky_relu', 'lrelu', 'tanh', 'sigmoid']
assert activation in valid, 'activation should be one of {}'.format(valid)
if activation == 'relu':
return nn.ReLU(inplace=True)
if activation ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | IlyaBizyaev/ttools | FCChain | false | 8,308 | [
"MIT"
] | 11 | b1435b19f397ce1baff9daed3cb287e52a029fdb | https://github.com/IlyaBizyaev/ttools/tree/b1435b19f397ce1baff9daed3cb287e52a029fdb |
TransformerEncoderPostNormLayer | import torch
import torch.nn.functional as F
from torch import nn
from typing import Optional
from torch.nn import LayerNorm
def _get_activation_fn(activation):
if activation == 'relu':
return F.relu
elif activation == 'gelu':
return F.gelu
raise RuntimeError('activation should be relu/gel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | JDBumgardner/stone_ground_hearth_battles | TransformerEncoderPostNormLayer | false | 8,309 | [
"Apache-2.0"
] | 20 | 9fe095651fab60e8ddbf563f0b9b7f3e723d5f4f | https://github.com/JDBumgardner/stone_ground_hearth_battles/tree/9fe095651fab60e8ddbf563f0b9b7f3e723d5f4f |
ResidualAttentionBlock | import torch
from collections import OrderedDict
from torch import nn
class LayerNorm(nn.LayerNorm):
"""Subclass torch's LayerNorm to handle fp16."""
def forward(self, x: 'torch.Tensor'):
orig_type = x.dtype
ret = super().forward(x.type(torch.float32))
return ret.type(orig_type)
cla... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Jack000/glid-3 | ResidualAttentionBlock | false | 8,310 | [
"MIT"
] | 31 | 4a18efc2785339ebc743e149a7955e34fff436fb | https://github.com/Jack000/glid-3/tree/4a18efc2785339ebc743e149a7955e34fff436fb |
StyleLossBlock | import torch
import torch.nn as nn
import torch.nn.functional as F
class StyleLossBlock(nn.Module):
def __init__(self, target: 'torch.Tensor'):
super().__init__()
self.stored_value = None
self._loss = F.mse_loss
self.shape = target.shape
self._target_gram_matrix = nn.Param... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.functional as F
assert_size_stride = torch... | Inkln/StyleTransferWithCatalyst | StyleLossBlock | false | 8,311 | [
"Apache-2.0"
] | 11 | c3181ecdfd32160907efc2d9d917a55925c25c11 | https://github.com/Inkln/StyleTransferWithCatalyst/tree/c3181ecdfd32160907efc2d9d917a55925c25c11 |
BilinearUpsampler | import torch
import torch as th
import torch.utils.data
class BilinearUpsampler(th.nn.Module):
def __init__(self, scale=2, channels=1):
super(BilinearUpsampler, self).__init__()
ksize = 2 * scale
total_pad = ksize - scale // 2
if scale % 2 == 1:
ksize += 1
self... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch as th
import torch.utils.data
assert_size_stride = torch._C._dynamo... | IlyaBizyaev/ttools | BilinearUpsampler | false | 8,312 | [
"MIT"
] | 11 | b1435b19f397ce1baff9daed3cb287e52a029fdb | https://github.com/IlyaBizyaev/ttools/tree/b1435b19f397ce1baff9daed3cb287e52a029fdb |
Conv1dResBlock | import torch
import torch.nn as nn
class Conv1d(nn.Conv1d):
"""
Convolution 1d
Args:
x: (N, T, C_in)
Returns:
y: (N, T, C_out)
"""
def __init__(self, in_channels, out_channels, kernel_size,
activation_fn=None, drop_rate=0.0, stride=1, padding='same',
dilation=1, groups=1, bias=Tr... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Jackson-Kang/VQVC-Pytorch | Conv1dResBlock | false | 8,313 | [
"MIT"
] | 13 | d2267b5c52253b6ae11a5767963a65320ae335c2 | https://github.com/Jackson-Kang/VQVC-Pytorch/tree/d2267b5c52253b6ae11a5767963a65320ae335c2 |
DepthwiseSeparableConv | import torch
import torch.nn.functional as F
import torch.nn as nn
class DepthwiseSeparableConv(nn.Module):
"""
Depth-wise separable convolution uses less parameters to generate output by convolution.
:Examples:
>>> m = DepthwiseSeparableConv(300, 200, 5, dim=1)
>>> input_tensor = torch.ra... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | IsaacChanghau/ReLoCLNet | DepthwiseSeparableConv | false | 8,314 | [
"MIT"
] | 31 | 56cb666ce516cce9acbcfce78fb4e95d81e11e54 | https://github.com/IsaacChanghau/ReLoCLNet/tree/56cb666ce516cce9acbcfce78fb4e95d81e11e54 |
NormalDivLoss | import torch
import torch.nn as nn
def fuzzyDist(x, a=0.1, b=2):
return 1 / (1 + (x / a).abs().pow(2 * b))
class SoftHist(nn.Module):
def __init__(self, bins, dist):
super(SoftHist, self).__init__()
bins[1] - bins[0]
self.bins = nn.Parameter(bins.unsqueeze(1))
self.dist = di... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.gu... | JWHan717/CS492I-Project | NormalDivLoss | false | 8,315 | [
"MIT"
] | 23 | 5da80bc41425ee90711a3de89c5501b5f7acd4b7 | https://github.com/JWHan717/CS492I-Project/tree/5da80bc41425ee90711a3de89c5501b5f7acd4b7 |
ConvEncoder | import torch
import torch.nn.functional as F
import torch.nn as nn
class DepthwiseSeparableConv(nn.Module):
"""
Depth-wise separable convolution uses less parameters to generate output by convolution.
:Examples:
>>> m = DepthwiseSeparableConv(300, 200, 5, dim=1)
>>> input_tensor = torch.ra... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | IsaacChanghau/ReLoCLNet | ConvEncoder | false | 8,316 | [
"MIT"
] | 31 | 56cb666ce516cce9acbcfce78fb4e95d81e11e54 | https://github.com/IsaacChanghau/ReLoCLNet/tree/56cb666ce516cce9acbcfce78fb4e95d81e11e54 |
ConvChain | import torch
import torch.utils.data
import torch.nn as nn
def _get_activation(activation):
valid = ['relu', 'leaky_relu', 'lrelu', 'tanh', 'sigmoid']
assert activation in valid, 'activation should be one of {}'.format(valid)
if activation == 'relu':
return nn.ReLU(inplace=True)
if activation ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | IlyaBizyaev/ttools | ConvChain | false | 8,317 | [
"MIT"
] | 11 | b1435b19f397ce1baff9daed3cb287e52a029fdb | https://github.com/IlyaBizyaev/ttools/tree/b1435b19f397ce1baff9daed3cb287e52a029fdb |
Conv1d | import torch
import torch.nn as nn
class Conv1d(nn.Conv1d):
"""
Convolution 1d
Args:
x: (N, T, C_in)
Returns:
y: (N, T, C_out)
"""
def __init__(self, in_channels, out_channels, kernel_size,
activation_fn=None, drop_rate=0.0, stride=1, padding='same',
dilation=1, groups=1, bias=Tr... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Jackson-Kang/VQVC-Pytorch | Conv1d | false | 8,318 | [
"MIT"
] | 13 | d2267b5c52253b6ae11a5767963a65320ae335c2 | https://github.com/Jackson-Kang/VQVC-Pytorch/tree/d2267b5c52253b6ae11a5767963a65320ae335c2 |
KLLoss | import torch
from torch import nn
import torch.nn.functional as F
from math import sqrt as sqrt
from itertools import product as product
class KLLoss(nn.Module):
"""
Kl-loss function for bounding box regression from CVPR 2019 paper:
Bounding Box Regression with Uncertainty for Accurate Object Detection
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
f... | JappaB/Active_Learning_Object_Detection | KLLoss | false | 8,319 | [
"MIT"
] | 21 | 3d9ad367aa872cbf3e9d71c566042c78fe2d0e76 | https://github.com/JappaB/Active_Learning_Object_Detection/tree/3d9ad367aa872cbf3e9d71c566042c78fe2d0e76 |
ResidualBlock | import torch
import torch.nn as nn
class ConvLayer(nn.Module):
def __init__(self, in_channels: 'int', out_channels: 'int', kernel_size:
'int', stride: 'int'):
super().__init__()
self._conv = nn.Conv2d(in_channels=in_channels, out_channels=
out_channels, kernel_size=kernel_size... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Inkln/StyleTransferWithCatalyst | ResidualBlock | false | 8,320 | [
"Apache-2.0"
] | 11 | c3181ecdfd32160907efc2d9d917a55925c25c11 | https://github.com/Inkln/StyleTransferWithCatalyst/tree/c3181ecdfd32160907efc2d9d917a55925c25c11 |
FixupBasicBlock | import torch
import torch.nn as nn
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
class FixupBasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, plane... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | IlanPrice/DCTpS | FixupBasicBlock | false | 8,321 | [
"MIT"
] | 12 | e3219ac132959f484724e0d0bd48a0cb8af3d0fa | https://github.com/IlanPrice/DCTpS/tree/e3219ac132959f484724e0d0bd48a0cb8af3d0fa |
TrainablePositionalEncoding | import torch
import torch.nn as nn
class TrainablePositionalEncoding(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, max_position_embeddings, hidden_size, dropout=0.1):
super(TrainablePositionalEncoding, self).__init__()
self.positi... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | IsaacChanghau/ReLoCLNet | TrainablePositionalEncoding | false | 8,322 | [
"MIT"
] | 31 | 56cb666ce516cce9acbcfce78fb4e95d81e11e54 | https://github.com/IsaacChanghau/ReLoCLNet/tree/56cb666ce516cce9acbcfce78fb4e95d81e11e54 |
FixupResidualChain | import torch
import numpy as np
import torch as th
import torch.utils.data
import torch.nn as nn
from collections import OrderedDict
def _get_activation(activation):
valid = ['relu', 'leaky_relu', 'lrelu', 'tanh', 'sigmoid']
assert activation in valid, 'activation should be one of {}'.format(valid)
if act... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
import tor... | IlyaBizyaev/ttools | FixupResidualChain | false | 8,323 | [
"MIT"
] | 11 | b1435b19f397ce1baff9daed3cb287e52a029fdb | https://github.com/IlyaBizyaev/ttools/tree/b1435b19f397ce1baff9daed3cb287e52a029fdb |
WeightedSmoothL1Loss | import torch
import numpy as np
import torch.nn as nn
class WeightedSmoothL1Loss(nn.Module):
"""
Code-wise Weighted Smooth L1 Loss modified based on fvcore.nn.smooth_l1_loss
https://github.com/facebookresearch/fvcore/blob/master/fvcore/nn/smooth_l1_loss.py
| 0.5 * x ** 2 / beta if abs(... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import numpy as np
import torch.nn as nn
assert_size_stride = ... | Jasonkks/mlcnet | WeightedSmoothL1Loss | false | 8,324 | [
"Apache-2.0"
] | 18 | 8f89c860c709733c8baa663607004fc48d76291d | https://github.com/Jasonkks/mlcnet/tree/8f89c860c709733c8baa663607004fc48d76291d |
h_swish | import torch
import torch.nn as nn
class h_sigmoid(nn.Module):
def __init__(self, inplace=True):
super(h_sigmoid, self).__init__()
self.relu = nn.ReLU6(inplace=inplace)
def forward(self, x):
return self.relu(x + 3) / 6
class h_swish(nn.Module):
def __init__(self, inplace=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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | JaminFong/dali-pytorch | h_swish | false | 8,325 | [
"Apache-2.0"
] | 41 | 7bd5d2380d210a32d24c7309da69c8d2c5db8759 | https://github.com/JaminFong/dali-pytorch/tree/7bd5d2380d210a32d24c7309da69c8d2c5db8759 |
injective_pad | import torch
import torch.nn as nn
class injective_pad(nn.Module):
def __init__(self, pad_size):
super(injective_pad, self).__init__()
self.pad_size = pad_size
self.pad = nn.ZeroPad2d((0, 0, 0, pad_size))
def forward(self, x):
x = x.permute(0, 2, 1, 3)
x = self.pad(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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | JessieYuW/CrevNet-Traffic4cast | injective_pad | false | 8,326 | [
"Apache-2.0"
] | 13 | 810b2a951de1f99a07bf8cfcbd93e1fc016cce48 | https://github.com/JessieYuW/CrevNet-Traffic4cast/tree/810b2a951de1f99a07bf8cfcbd93e1fc016cce48 |
GridMixupLoss | import math
import random
import torch
import numpy as np
import typing as t
from torch import nn
class GridMixupLoss(nn.Module):
""" Implementation of GridMixup loss
:param alpha: Percent of the first image on the crop. Can be float or Tuple[float, float]
- if float: lambda parameter get... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import math
import ran... | IlyaDobrynin/GridMixup | GridMixupLoss | false | 8,327 | [
"MIT"
] | 42 | 11b741f234832c9a15b4e650e1e4fad0e79dc63b | https://github.com/IlyaDobrynin/GridMixup/tree/11b741f234832c9a15b4e650e1e4fad0e79dc63b |
DiagonalQuantizer | import torch
import numpy as np
import torch.cuda
import torch.fft
def diagonal_quantize_function(x, bit, phase_noise_std=0, random_state=None,
gradient_clip=False):
class DiagonalQuantizeFunction(torch.autograd.Function):
@staticmethod
def forward(ctx, x):
S_scale = x.abs().max... | 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 nump... | JeremieMelo/pytorch-onn | DiagonalQuantizer | false | 8,328 | [
"MIT"
] | 16 | 670996112277a6c19c7da400afbe0a4ce45ad5de | https://github.com/JeremieMelo/pytorch-onn/tree/670996112277a6c19c7da400afbe0a4ce45ad5de |
AffineConstantFlow | import torch
from torch import nn
class AffineConstantFlow(nn.Module):
"""
Scales + Shifts the flow by (learned) constants per dimension.
In NICE paper there is a Scaling layer which is a special case of this where t is None
"""
def __init__(self, dim, scale=True, shift=True):
super().__... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_... | JannerM/gamma-models | AffineConstantFlow | false | 8,329 | [
"MIT"
] | 32 | 4b40d828bf228385c3081d359cdc3494d70de4a1 | https://github.com/JannerM/gamma-models/tree/4b40d828bf228385c3081d359cdc3494d70de4a1 |
SqueezeExcite | import torch
from torchvision.transforms import functional as F
import torch.nn as nn
import torch.nn.functional as F
def _make_divisible(v, divisor, min_value=None):
"""
This function is taken from the original tf repo.
It ensures that all layers have a channel number that is divisible by 8
It can be... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 torchvision.transforms i... | JaminFong/dali-pytorch | SqueezeExcite | false | 8,330 | [
"Apache-2.0"
] | 41 | 7bd5d2380d210a32d24c7309da69c8d2c5db8759 | https://github.com/JaminFong/dali-pytorch/tree/7bd5d2380d210a32d24c7309da69c8d2c5db8759 |
Upsample | import torch
import torch.nn as nn
class Upsample(nn.Upsample):
"""
Upsampling via interporlation
Args:
x: (N, T, C)
Returns:
y: (N, S * T, C)
(S: scale_factor)
"""
def __init__(self, scale_factor=2, mode='nearest'):
super(Upsample, self).__init__(scale_factor=scale_factor, mode=mo... | 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... | Jackson-Kang/VQVC-Pytorch | Upsample | false | 8,331 | [
"MIT"
] | 13 | d2267b5c52253b6ae11a5767963a65320ae335c2 | https://github.com/Jackson-Kang/VQVC-Pytorch/tree/d2267b5c52253b6ae11a5767963a65320ae335c2 |
GraphConvolution | from torch.nn import Module
import torch
import torch.nn.functional as F
from torch.nn import Parameter
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
import torch.nn.modules.loss
from scipy.sparse import *
def dropout(x, drop_prob, shared_axes=[], training=False):
"""
App... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn import Module
i... | IBM/graph4nlp | GraphConvolution | false | 8,332 | [
"Apache-2.0"
] | 18 | a9bf20b23fa1ec368d9bd40cc8c557f86a9f8297 | https://github.com/IBM/graph4nlp/tree/a9bf20b23fa1ec368d9bd40cc8c557f86a9f8297 |
Context2AnswerAttention | import torch
from torch import nn
import torch.nn.modules.loss
from scipy.sparse import *
class Context2AnswerAttention(nn.Module):
def __init__(self, dim, hidden_size):
super(Context2AnswerAttention, self).__init__()
self.linear_sim = nn.Linear(dim, hidden_size, bias=False)
def forward(self... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | IBM/graph4nlp | Context2AnswerAttention | false | 8,333 | [
"Apache-2.0"
] | 18 | a9bf20b23fa1ec368d9bd40cc8c557f86a9f8297 | https://github.com/IBM/graph4nlp/tree/a9bf20b23fa1ec368d9bd40cc8c557f86a9f8297 |
decoder5 | import torch
from torch import 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.UpsamplingN... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Holmes-Alan/RefVAE | decoder5 | false | 8,334 | [
"MIT"
] | 13 | 836b8f1168f1b0f923b609a48e202ace7806f79c | https://github.com/Holmes-Alan/RefVAE/tree/836b8f1168f1b0f923b609a48e202ace7806f79c |
LxmertAttentionOutput | import torch
import torch.utils.data
import torch.nn as nn
import torch
import torch.nn.parallel
class LxmertAttentionOutput(nn.Module):
def __init__(self, hidden_size, hidden_dropout_prob):
super().__init__()
self.dense = nn.Linear(hidden_size, hidden_size)
self.LayerNorm = nn.LayerNorm(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.utils.... | IsmaelElsharkawi/new_pororo_repo | LxmertAttentionOutput | false | 8,335 | [
"MIT"
] | 19 | 4617083b420615b8a3eb0f44d02e4e91a8f407f7 | https://github.com/IsmaelElsharkawi/new_pororo_repo/tree/4617083b420615b8a3eb0f44d02e4e91a8f407f7 |
MixActiv | import torch
import torch as th
from torch import nn
def gauss(x, mean=0, std=1):
return th.exp(-(x - mean) ** 2 / (2 * std ** 2))
class MixActiv(nn.Module):
def __init__(self):
super().__init__()
self.activations = th.sin, th.tanh, gauss, th.relu
self.n_activs = len(self.activation... | 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... | JiangZehua/control-pcgrl | MixActiv | false | 8,336 | [
"MIT"
] | 15 | e4fd1bf9670e5855f04941ebca34170517c451b4 | https://github.com/JiangZehua/control-pcgrl/tree/e4fd1bf9670e5855f04941ebca34170517c451b4 |
ClassPredictor | import torch
from torch import nn
class ClassPredictor(nn.Module):
def __init__(self, nz_feat, max_object_classes):
super(ClassPredictor, self).__init__()
self.predictor = nn.Linear(nz_feat, max_object_classes)
def forward(self, feats):
class_logits = self.predictor(feats)
re... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | JasonQSY/Associative3D | ClassPredictor | false | 8,337 | [
"MIT"
] | 25 | c50818b593ec48c38ed7ee3e109c23531089da32 | https://github.com/JasonQSY/Associative3D/tree/c50818b593ec48c38ed7ee3e109c23531089da32 |
InnerProductDecoder | import torch
from torch import nn
import torch.nn.functional as F
import torch.nn.modules.loss
from scipy.sparse import *
def dropout(x, drop_prob, shared_axes=[], training=False):
"""
Apply dropout to input tensor.
Parameters
----------
input_tensor: ``torch.FloatTensor``
A tensor of shap... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.nn.modules.loss
from scipy.sparse import *
ass... | IBM/graph4nlp | InnerProductDecoder | false | 8,338 | [
"Apache-2.0"
] | 18 | a9bf20b23fa1ec368d9bd40cc8c557f86a9f8297 | https://github.com/IBM/graph4nlp/tree/a9bf20b23fa1ec368d9bd40cc8c557f86a9f8297 |
GRUStep | import torch
from torch import nn
import torch.nn.modules.loss
from scipy.sparse import *
class GRUStep(nn.Module):
def __init__(self, hidden_size, input_size):
super(GRUStep, self).__init__()
"""GRU module"""
self.linear_z = nn.Linear(hidden_size + input_size, hidden_size,
bi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | IBM/graph4nlp | GRUStep | false | 8,339 | [
"Apache-2.0"
] | 18 | a9bf20b23fa1ec368d9bd40cc8c557f86a9f8297 | https://github.com/IBM/graph4nlp/tree/a9bf20b23fa1ec368d9bd40cc8c557f86a9f8297 |
ScalePredictor | import torch
from torch import nn
class ScalePredictor(nn.Module):
def __init__(self, nz):
super(ScalePredictor, self).__init__()
self.pred_layer = nn.Linear(nz, 3)
def forward(self, feat):
scale = self.pred_layer.forward(feat) + 1
scale = torch.nn.functional.relu(scale) + 1e... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | JasonQSY/Associative3D | ScalePredictor | false | 8,340 | [
"MIT"
] | 25 | c50818b593ec48c38ed7ee3e109c23531089da32 | https://github.com/JasonQSY/Associative3D/tree/c50818b593ec48c38ed7ee3e109c23531089da32 |
CombinedTargetMSELoss | import torch
import torch.nn as nn
class CombinedTargetMSELoss(nn.Module):
"""MSE loss for combined target.
CombinedTarget: The combination of classification target
(response map) and regression target (offset map).
Paper ref: Huang et al. The Devil is in the Details: Delving into
... | 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... | Jackqu/mmpose | CombinedTargetMSELoss | false | 8,341 | [
"Apache-2.0"
] | 38 | ad8acc5ff5da7993c6befdc4b1ced2c2ecb64533 | https://github.com/Jackqu/mmpose/tree/ad8acc5ff5da7993c6befdc4b1ced2c2ecb64533 |
RelativeScalePredictor | import torch
from torch import nn
class RelativeScalePredictor(nn.Module):
def __init__(self, in_size, out_size):
super(RelativeScalePredictor, self).__init__()
self.predictor = nn.Linear(in_size, out_size)
def forward(self, feat):
predictions = self.predictor.forward(feat) + 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.... | JasonQSY/Associative3D | RelativeScalePredictor | false | 8,342 | [
"MIT"
] | 25 | c50818b593ec48c38ed7ee3e109c23531089da32 | https://github.com/JasonQSY/Associative3D/tree/c50818b593ec48c38ed7ee3e109c23531089da32 |
UpSample | import torch
import torch.nn as nn
import torch.nn.functional as F
class UpSample(nn.Sequential):
def __init__(self, skip_input, output_features):
super(UpSample, self).__init__()
self.convA = nn.Conv2d(skip_input, output_features, kernel_size=3,
stride=1, padding=1)
self.leak... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | JanRocketMan/regression-prior-networks | UpSample | false | 8,343 | [
"MIT"
] | 24 | 3c8ffa758ee6eaa15b8afe31ac1c03f87bbf6a14 | https://github.com/JanRocketMan/regression-prior-networks/tree/3c8ffa758ee6eaa15b8afe31ac1c03f87bbf6a14 |
BasicConv2d | import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicConv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, **kwargs):
super(BasicConv2d, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, bias=
False, **kwarg... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | JinkaiZheng/TraND | BasicConv2d | false | 8,344 | [
"MIT"
] | 33 | a8babc34073ee126789969bd97e149bae4015953 | https://github.com/JinkaiZheng/TraND/tree/a8babc34073ee126789969bd97e149bae4015953 |
TransNonlinear | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
class TransNonlinear(nn.Module):
def __init__(self, d_model, dim_feedforward, dropout=0.1):
super().__init__()
self.linear1 = nn.Linear(d_model, dim_feedforward)
self.dropout = nn.Dropout(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.... | Jasonkks/PTTR | TransNonlinear | false | 8,345 | [
"Apache-2.0"
] | 14 | 11f664a7f1b2281293d82a5450fdd3d4bfa5883e | https://github.com/Jasonkks/PTTR/tree/11f664a7f1b2281293d82a5450fdd3d4bfa5883e |
LxmertAttention | import math
import torch
import torch.utils.data
import torch.nn as nn
import torch
import torch.nn.parallel
class LxmertAttention(nn.Module):
def __init__(self, hidden_size, num_attention_heads,
attention_probs_dropout_prob, ctx_dim=None):
super().__init__()
if hidden_size % num_attentio... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | IsmaelElsharkawi/new_pororo_repo | LxmertAttention | false | 8,346 | [
"MIT"
] | 19 | 4617083b420615b8a3eb0f44d02e4e91a8f407f7 | https://github.com/IsmaelElsharkawi/new_pororo_repo/tree/4617083b420615b8a3eb0f44d02e4e91a8f407f7 |
Downsample | import torch
class Downsample(torch.nn.Module):
def __init__(self, s, use_max=False, batch_mode=False):
super(Downsample, self).__init__()
self.batch_mode = batch_mode
if use_max:
layer = torch.nn.MaxPool3d(s, stride=s)
else:
layer = torch.nn.Conv3d(1, 1, 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
reinterpret_tens... | JasonQSY/Associative3D | Downsample | false | 8,347 | [
"MIT"
] | 25 | c50818b593ec48c38ed7ee3e109c23531089da32 | https://github.com/JasonQSY/Associative3D/tree/c50818b593ec48c38ed7ee3e109c23531089da32 |
MeanEmbedding | import torch
from torch import nn
import torch.nn.modules.loss
from scipy.sparse import *
class MeanEmbedding(nn.Module):
"""Mean embedding class.
"""
def __init__(self):
super(MeanEmbedding, self).__init__()
def forward(self, emb, len_):
"""Compute average embeddings.
Param... | 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.nn.modules.loss
from scipy.sparse import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride... | IBM/graph4nlp | MeanEmbedding | false | 8,348 | [
"Apache-2.0"
] | 18 | a9bf20b23fa1ec368d9bd40cc8c557f86a9f8297 | https://github.com/IBM/graph4nlp/tree/a9bf20b23fa1ec368d9bd40cc8c557f86a9f8297 |
GatedFusion | import torch
from torch import nn
import torch.nn.modules.loss
from scipy.sparse import *
class GatedFusion(nn.Module):
def __init__(self, hidden_size):
super(GatedFusion, self).__init__()
"""GatedFusion module"""
self.fc_z = nn.Linear(4 * hidden_size, hidden_size, bias=True)
def for... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.nn.modules.loss
from scipy.sparse import *
ass... | IBM/graph4nlp | GatedFusion | false | 8,349 | [
"Apache-2.0"
] | 18 | a9bf20b23fa1ec368d9bd40cc8c557f86a9f8297 | https://github.com/IBM/graph4nlp/tree/a9bf20b23fa1ec368d9bd40cc8c557f86a9f8297 |
IDPredictor | import torch
import torch.nn.functional as F
from torch import nn
class IDPredictor(nn.Module):
def __init__(self, nz_feat, n_dim=5):
super(IDPredictor, self).__init__()
self.pred_layer = nn.Linear(nz_feat, 256)
self.sc_layer = nn.Linear(256, 128)
self.sc_layer2 = nn.Linear(128, 6... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | JasonQSY/Associative3D | IDPredictor | false | 8,350 | [
"MIT"
] | 25 | c50818b593ec48c38ed7ee3e109c23531089da32 | https://github.com/JasonQSY/Associative3D/tree/c50818b593ec48c38ed7ee3e109c23531089da32 |
ConvModule | import torch
import torch.utils.data
import torch.nn as nn
def _get_activation(activation):
valid = ['relu', 'leaky_relu', 'lrelu', 'tanh', 'sigmoid']
assert activation in valid, 'activation should be one of {}'.format(valid)
if activation == 'relu':
return nn.ReLU(inplace=True)
if activation ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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
import torch.nn as nn
assert_size_stride = torch._C._dyn... | IlyaBizyaev/ttools | ConvModule | false | 8,351 | [
"MIT"
] | 11 | b1435b19f397ce1baff9daed3cb287e52a029fdb | https://github.com/IlyaBizyaev/ttools/tree/b1435b19f397ce1baff9daed3cb287e52a029fdb |
Normalize | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data.distributed
class Normalize(nn.Module):
def __init__(self, p=2):
super(Normalize, self).__init__()
self.p = p
def forward(self, x):
return F.normalize(x, p=self.p, dim=1)
def get_inputs():
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import... | JindongGu/SimDis | Normalize | false | 8,352 | [
"MIT"
] | 12 | 0871a217a756acc268f35f802e35b01b12817f0d | https://github.com/JindongGu/SimDis/tree/0871a217a756acc268f35f802e35b01b12817f0d |
MultiHeadAttn | import torch
import torch.nn.functional as F
from torch import nn
class MultiHeadAttn(nn.Module):
def __init__(self, n_head, d_model, d_head, dropout, dropatt=0,
pre_lnorm=False):
super(MultiHeadAttn, self).__init__()
self.n_head = n_head
self.d_model = d_model
self.d_head... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | JasonBenn/duet | MultiHeadAttn | false | 8,353 | [
"Apache-2.0"
] | 11 | 0d6f1f66fad097023b022f2a361a1587d0f740ba | https://github.com/JasonBenn/duet/tree/0d6f1f66fad097023b022f2a361a1587d0f740ba |
PositionalWiseFeedForward | import math
import torch
import torch.nn as nn
class GELU(nn.Module):
"""
This is a smoother version of the RELU.
Original paper: https://arxiv.org/abs/1606.08415
"""
def forward(self, x):
return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x +
0.044715 * torch.pow(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.... | JiaweiSheng/FAAN | PositionalWiseFeedForward | false | 8,354 | [
"MIT"
] | 41 | b439b829506c4e2e9044a6b2ab7f3d844f445a95 | https://github.com/JiaweiSheng/FAAN/tree/b439b829506c4e2e9044a6b2ab7f3d844f445a95 |
SelfAttention | import torch
from torch import nn
import torch.nn.modules.loss
from scipy.sparse import *
class SelfAttention(nn.Module):
def __init__(self, input_size, hidden_size):
super(SelfAttention, self).__init__()
self.W1 = torch.Tensor(input_size, hidden_size)
self.W1 = nn.Parameter(nn.init.xavie... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | IBM/graph4nlp | SelfAttention | false | 8,355 | [
"Apache-2.0"
] | 18 | a9bf20b23fa1ec368d9bd40cc8c557f86a9f8297 | https://github.com/IBM/graph4nlp/tree/a9bf20b23fa1ec368d9bd40cc8c557f86a9f8297 |
ScaledDotProductAttention | import torch
import numpy as np
import torch.nn as nn
class ScaledDotProductAttention(nn.Module):
""" Scaled Dot-Product Attention
"""
def __init__(self, attn_dropout=0.0):
super(ScaledDotProductAttention, self).__init__()
self.dropout = nn.Dropout(attn_dropout)
self.softmax = 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.... | JiaweiSheng/FAAN | ScaledDotProductAttention | false | 8,356 | [
"MIT"
] | 41 | b439b829506c4e2e9044a6b2ab7f3d844f445a95 | https://github.com/JiaweiSheng/FAAN/tree/b439b829506c4e2e9044a6b2ab7f3d844f445a95 |
MLP | import torch
import torch.nn as nn
import torch.nn.functional as F
class MLP(nn.Module):
def __init__(self, input_dim, output_dim, hidden_dim=128):
""" 初始化q网络，为全连接网络
input_dim: 输入的特征数即环境的状态维度
output_dim: 输出的动作维度
"""
super(MLP, self).__init__()
self.fc1 = 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
import torch.nn as nn
assert_... | JohnJim0816/rl-tutorials | MLP | false | 8,357 | [
"MIT"
] | 16 | e99daea815da85f9f25dff2d01b030249a203d22 | https://github.com/JohnJim0816/rl-tutorials/tree/e99daea815da85f9f25dff2d01b030249a203d22 |
FixupBasicBlock | import torch
import torch as th
import torch.utils.data
import torch.nn as nn
def _get_activation(activation):
valid = ['relu', 'leaky_relu', 'lrelu', 'tanh', 'sigmoid']
assert activation in valid, 'activation should be one of {}'.format(valid)
if activation == 'relu':
return nn.ReLU(inplace=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
from torch._inductor.runtime import triton_helpers
import torch as th
import tor... | IlyaBizyaev/ttools | FixupBasicBlock | false | 8,358 | [
"MIT"
] | 11 | b1435b19f397ce1baff9daed3cb287e52a029fdb | https://github.com/IlyaBizyaev/ttools/tree/b1435b19f397ce1baff9daed3cb287e52a029fdb |
LabelPredictor | import torch
from torch import nn
class LabelPredictor(nn.Module):
def __init__(self, nz_feat, classify_rot=True):
super(LabelPredictor, self).__init__()
self.pred_layer = nn.Linear(nz_feat, 1)
def forward(self, feat):
pred = self.pred_layer.forward(feat)
pred = torch.sigmoid... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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... | JasonQSY/Associative3D | LabelPredictor | false | 8,359 | [
"MIT"
] | 25 | c50818b593ec48c38ed7ee3e109c23531089da32 | https://github.com/JasonQSY/Associative3D/tree/c50818b593ec48c38ed7ee3e109c23531089da32 |
MultimodalHead | import torch
from torch import nn
class MultimodalHead(nn.Module):
"""
Multimodal head for the conv net outputs.
This layer concatenate the outputs of audio and visual convoluational nets
and performs a fully-connected projection
"""
def __init__(self, dim_in, num_classes, dropout_rate=0.0, a... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | JiwanChung/acav100m | MultimodalHead | false | 8,360 | [
"MIT"
] | 27 | 51cb948d5682da69334a8d05d2df631971b60215 | https://github.com/JiwanChung/acav100m/tree/51cb948d5682da69334a8d05d2df631971b60215 |
CNN_small | import torch
import torch.nn as nn
from torch.nn import functional as F
import torch.utils.data
class CNN_small(nn.Module):
def __init__(self, num_classes=10):
super(CNN_small, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | JiarunLiu/Co-correcting | CNN_small | false | 8,361 | [
"Apache-2.0"
] | 19 | 4e3ca4951de5d73ca812bbbcfe666273082ff2fd | https://github.com/JiarunLiu/Co-correcting/tree/4e3ca4951de5d73ca812bbbcfe666273082ff2fd |
CRFLoss | import torch
import torch.nn as nn
class CRFLoss(nn.Module):
def __init__(self, L, init):
super(CRFLoss, self).__init__()
self.start = nn.Parameter(torch.Tensor(L).uniform_(-init, init))
self.T = nn.Parameter(torch.Tensor(L, L).uniform_(-init, init))
self.end = nn.Parameter(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 math as tl_math
import torch.nn as nn
... | Johannes0Horn/mtl-dts | CRFLoss | false | 8,362 | [
"MIT"
] | 19 | ae50253c808bbb77af3b1117f69f08d2268099e9 | https://github.com/Johannes0Horn/mtl-dts/tree/ae50253c808bbb77af3b1117f69f08d2268099e9 |
NonLocalBlock | import torch
import torch.nn as nn
from time import *
class NonLocalBlock(nn.Module):
def __init__(self, channel):
super(NonLocalBlock, self).__init__()
self.inter_channel = channel // 2
self.conv_phi = nn.Conv2d(in_channels=channel, out_channels=self.
inter_channel, kernel_si... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Jinming-Su/SGNet | NonLocalBlock | false | 8,363 | [
"MIT"
] | 13 | fcf35edaf332c1a4e2713acad5a0fc0e21509c3e | https://github.com/Jinming-Su/SGNet/tree/fcf35edaf332c1a4e2713acad5a0fc0e21509c3e |
SoftCrossEntropyLoss | import torch
def soft_cross_entropy(logit, label, weight=None, reduce=None, reduction='mean'
):
if weight is not None and weight.requires_grad:
raise RuntimeError('gradient for weight is not supported')
losses = SoftCrossEntropyFunction.apply(logit, label, weight)
reduction = {(True): 'mean', ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
assert_size_stride = t... | Jingkang50/ICCV21_SCOOD | SoftCrossEntropyLoss | false | 8,364 | [
"MIT"
] | 34 | 51204e3788a9e81aa334611072bef106fd9d13ad | https://github.com/Jingkang50/ICCV21_SCOOD/tree/51204e3788a9e81aa334611072bef106fd9d13ad |
MaxPool2dSamePadding | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
def get_same_padding(in_size, kernel_size, stride):
"""'Same 'same' operation with tensorflow
notice：padding=(0, 1, 0, 1) and padding=(1, 1, 1, 1) are different
padding=(1, 1, 1, 1):
out(H, W) = (in + [2 * padding] − k... | 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 math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_siz... | Jintao-Huang/EfficientDet_PyTorch | MaxPool2dSamePadding | false | 8,365 | [
"Apache-2.0"
] | 18 | 79616be397b7f57992cd43b772f65b58b5e25a8b | https://github.com/Jintao-Huang/EfficientDet_PyTorch/tree/79616be397b7f57992cd43b772f65b58b5e25a8b |
SoftSelectPrototype | import torch
import torch.nn as nn
class SoftSelectAttention(nn.Module):
def __init__(self, hidden_size):
super(SoftSelectAttention, self).__init__()
def forward(self, support, query):
"""
:param support: [few, dim]
:param query: [batch, 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 import triton_helpers
from torch._inductor.runtime.... | JiaweiSheng/FAAN | SoftSelectPrototype | false | 8,366 | [
"MIT"
] | 41 | b439b829506c4e2e9044a6b2ab7f3d844f445a95 | https://github.com/JiaweiSheng/FAAN/tree/b439b829506c4e2e9044a6b2ab7f3d844f445a95 |
Critic | import torch
import torch.nn as nn
import torch.nn.functional as F
class Critic(nn.Module):
def __init__(self, n_obs, action_dim, hidden_size, init_w=0.003):
super(Critic, self).__init__()
self.linear1 = nn.Linear(n_obs + action_dim, hidden_size)
self.linear2 = nn.Linear(hidden_size, hidd... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | JohnJim0816/rl-tutorials | Critic | false | 8,367 | [
"MIT"
] | 16 | e99daea815da85f9f25dff2d01b030249a203d22 | https://github.com/JohnJim0816/rl-tutorials/tree/e99daea815da85f9f25dff2d01b030249a203d22 |
GlobalAveragePooling | import torch
import torch.nn as nn
class GlobalAveragePooling(nn.Module):
"""Global Average Pooling neck.
Note that we use `view` to remove extra channel after pooling. We do not
use `squeeze` as it will also remove the batch dimension when the tensor
has a batch dimension of size 1, which can lead 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... | Jackqu/mmpose | GlobalAveragePooling | false | 8,368 | [
"Apache-2.0"
] | 38 | ad8acc5ff5da7993c6befdc4b1ced2c2ecb64533 | https://github.com/Jackqu/mmpose/tree/ad8acc5ff5da7993c6befdc4b1ced2c2ecb64533 |
GlobalAttentionGeneral | import torch
import torch.nn as nn
import torch.nn.parallel
class GlobalAttentionGeneral(nn.Module):
def __init__(self, idf, cdf):
super(GlobalAttentionGeneral, self).__init__()
self.sm = nn.Softmax()
self.mask = None
def applyMask(self, mask):
self.mask = mask
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
from torch._inductor.runtime.... | JoonHong-Kim/T2I_CL | GlobalAttentionGeneral | false | 8,369 | [
"MIT"
] | 35 | c52aa73da903d6e4174eeef2663e5bc1163785b1 | https://github.com/JoonHong-Kim/T2I_CL/tree/c52aa73da903d6e4174eeef2663e5bc1163785b1 |
PolicyNet | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions import Normal
class PolicyNet(nn.Module):
def __init__(self, state_dim, action_dim, hidden_dim, init_w=0.003,
log_std_min=-20, log_std_max=2):
super(PolicyNet, self).__init__()
self.log_std_min = l... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from to... | JohnJim0816/rl-tutorials | PolicyNet | false | 8,370 | [
"MIT"
] | 16 | e99daea815da85f9f25dff2d01b030249a203d22 | https://github.com/JohnJim0816/rl-tutorials/tree/e99daea815da85f9f25dff2d01b030249a203d22 |
SE | import torch
from itertools import chain as chain
import torch.utils.data
import torch.nn as nn
class SwishEfficient(torch.autograd.Function):
"""Swish activation function: x * sigmoid(x)."""
@staticmethod
def forward(ctx, x):
result = x * torch.sigmoid(x)
ctx.save_for_backward(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 itertools import chain a... | JaywongWang/SlowFast | SE | false | 8,371 | [
"Apache-2.0"
] | 43 | 366467aafc856712fdc3e9c4cce8e90969047ee6 | https://github.com/JaywongWang/SlowFast/tree/366467aafc856712fdc3e9c4cce8e90969047ee6 |
WasLoss | import torch
import torch.nn as nn
class WasLoss(nn.Module):
def __init__(self):
super(WasLoss, self).__init__()
self.MSEls = torch.nn.BCEWithLogitsLoss()
def forward(self, true_data, fake_data):
SLX, _ = torch.sort(true_data, 0)
SLG, _ = torch.sort(fake_data, 0)
retu... | 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... | Johnson-yue/RS-GAN | WasLoss | false | 8,372 | [
"MIT"
] | 26 | 8e8723045d63d8f9a4b510800cd909e7a6e3d195 | https://github.com/Johnson-yue/RS-GAN/tree/8e8723045d63d8f9a4b510800cd909e7a6e3d195 |
Actor | import torch
import torch.nn as nn
import torch.nn.functional as F
class Actor(nn.Module):
def __init__(self, n_obs, action_dim, hidden_size, init_w=0.003):
super(Actor, self).__init__()
self.linear1 = nn.Linear(n_obs, hidden_size)
self.linear2 = nn.Linear(hidden_size, hidden_size)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | JohnJim0816/rl-tutorials | Actor | false | 8,373 | [
"MIT"
] | 16 | e99daea815da85f9f25dff2d01b030249a203d22 | https://github.com/JohnJim0816/rl-tutorials/tree/e99daea815da85f9f25dff2d01b030249a203d22 |
Mish | import torch
import torch.utils.data
from torchvision.transforms import functional as F
import torch.nn as nn
import torch.nn.functional as F
from math import sqrt as sqrt
from itertools import product as product
class Mish(nn.Module):
def forward(self, x):
return x.mul_(F.softplus(x).tanh())
def get_i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.utils.data
import torch.nn as nn
from math import... | Het-Shah/Monk_Object_Detection | Mish | false | 8,374 | [
"Apache-2.0"
] | 15 | 1d7a07193ea3455221caa41d07c33c81d50c6b3f | https://github.com/Het-Shah/Monk_Object_Detection/tree/1d7a07193ea3455221caa41d07c33c81d50c6b3f |
AttentionPool2d | import math
import torch
from torch import nn
import torch as th
def conv_nd(dims, *args, **kwargs):
"""
Create a 1D, 2D, or 3D convolution module.
"""
if dims == 1:
return nn.Conv1d(*args, **kwargs)
elif dims == 2:
return nn.Conv2d(*args, **kwargs)
elif dims == 3:
retu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Jack000/glid-3 | AttentionPool2d | false | 8,375 | [
"MIT"
] | 31 | 4a18efc2785339ebc743e149a7955e34fff436fb | https://github.com/Jack000/glid-3/tree/4a18efc2785339ebc743e149a7955e34fff436fb |
GaussianKernel | import math
import torch
import torch.nn as nn
import torch.utils.data
class GaussianKernel(nn.Module):
def __init__(self, delta_var, pmaps_threshold):
super().__init__()
self.delta_var = delta_var
self.two_sigma = delta_var * delta_var / -math.log(pmaps_threshold)
def forward(self, ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import math
import torch.nn as nn
import torch.utils.data
assert_size_str... | JonasHell/torch-em | GaussianKernel | false | 8,376 | [
"MIT"
] | 13 | 2e008e0cd2f0ea6681581374fce4f9f47b986d55 | https://github.com/JonasHell/torch-em/tree/2e008e0cd2f0ea6681581374fce4f9f47b986d55 |
Attention | import torch
from torch import nn
import torch.nn.functional as F
class Attention(nn.Module):
"""
Applies an attention mechanism on the output features from the decoder.
"""
def __init__(self, dim):
super(Attention, self).__init__()
self.dim = dim
self.linear1 = nn.Linear(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.... | JiwanChung/tapm | Attention | false | 8,377 | [
"MIT"
] | 14 | ec42b139d1c012daccc55f85e67744488d526476 | https://github.com/JiwanChung/tapm/tree/ec42b139d1c012daccc55f85e67744488d526476 |
Net | import torch
import torch.nn as nn
class Swish(nn.Module):
def __init__(self, inplace=True):
super(Swish, self).__init__()
self.inplace = inplace
def forward(self, x):
if self.inplace:
x.mul_(torch.sigmoid(x))
return x
else:
return x * torc... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Jianxun-Wang/Physics-constrained-Bayesian-deep-learning | Net | false | 8,378 | [
"MIT"
] | 24 | cde0287f848f83c6def1fe409c67d7d4e14174da | https://github.com/Jianxun-Wang/Physics-constrained-Bayesian-deep-learning/tree/cde0287f848f83c6def1fe409c67d7d4e14174da |
Block | import torch
from torch import nn
import torch.nn.functional as F
class Block(nn.Module):
def __init__(self, dim):
super(Block, self).__init__()
self.dim = dim
self.layer_norm = nn.LayerNorm(self.dim)
self.conv = nn.Conv1d(self.dim, self.dim, kernel_size=3, padding=1)
def for... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | JiwanChung/tapm | Block | false | 8,379 | [
"MIT"
] | 14 | ec42b139d1c012daccc55f85e67744488d526476 | https://github.com/JiwanChung/tapm/tree/ec42b139d1c012daccc55f85e67744488d526476 |
RLFeatPreprocessNet | import torch
import torch.nn as nn
import torch.utils.data
class RLFeatPreprocessNet(nn.Module):
"""
Preprocess Features
1. visual feature
2. label prediction embed feature
3. box embed
4. overlap embed
"""
def __init__(self, feat_size, embed_size, bbox_size, overlap_size,
out... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | KaihuaTang/VCTree-Visual-Question-Answering | RLFeatPreprocessNet | false | 8,380 | [
"MIT"
] | 31 | b6b0a8bdb01d45d36de3bded91db42544ad6a593 | https://github.com/KaihuaTang/VCTree-Visual-Question-Answering/tree/b6b0a8bdb01d45d36de3bded91db42544ad6a593 |
ELUPlus | import torch
from torch import nn
import torch.nn
class ELUPlus(nn.Module):
def __init__(self):
super().__init__()
self.elu = nn.ELU()
def forward(self, x):
return self.elu(x) + 1.0
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
import torch.nn
assert_size_stride = torch._C._dynamo.guar... | KailinLi/nflows | ELUPlus | false | 8,381 | [
"MIT"
] | 13 | 7c07a1d5e510beb681d1b11d6ffda95a086a8153 | https://github.com/KailinLi/nflows/tree/7c07a1d5e510beb681d1b11d6ffda95a086a8153 |
Memory | import torch
import torch.nn as nn
import torch.nn.parallel
class Memory(nn.Module):
def __init__(self):
super(Memory, self).__init__()
self.sm = nn.Softmax()
self.mask = None
def applyMask(self, mask):
self.mask = mask
def forward(self, input, context_key, content_value... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | JoonHong-Kim/T2I_CL | Memory | false | 8,382 | [
"MIT"
] | 35 | c52aa73da903d6e4174eeef2663e5bc1163785b1 | https://github.com/JoonHong-Kim/T2I_CL/tree/c52aa73da903d6e4174eeef2663e5bc1163785b1 |
DiceLossWithLogits | import torch
import torch.nn as nn
import torch.utils.data
def flatten_samples(input_):
"""
Flattens a tensor or a variable such that the channel axis is first and the sample axis
is second. The shapes are transformed as follows:
(N, C, H, W) --> (C, N * H * W)
(N, C, D, H, W) --> (C, 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
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guard... | JonasHell/torch-em | DiceLossWithLogits | false | 8,383 | [
"MIT"
] | 13 | 2e008e0cd2f0ea6681581374fce4f9f47b986d55 | https://github.com/JonasHell/torch-em/tree/2e008e0cd2f0ea6681581374fce4f9f47b986d55 |
ActorNet | import torch
import torch.nn as nn
class ActorNet(nn.Module):
""" Actor Network """
def __init__(self, state_num, action_num, hidden1=256, hidden2=256,
hidden3=256):
"""
:param state_num: number of states
:param action_num: number of actions
:param hidden1: hidden 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
import torch.nn as nn
assert_... | Kanaderu/spiking-ddpg-mapless-navigation | ActorNet | false | 8,384 | [
"MIT"
] | 29 | 2b5e7e67385dee4428b8036bc4ffe95e812b34e0 | https://github.com/Kanaderu/spiking-ddpg-mapless-navigation/tree/2b5e7e67385dee4428b8036bc4ffe95e812b34e0 |
StochasticClassifier | import torch
import torch.nn as nn
from torch.nn import functional as F
class StochasticClassifier(nn.Module):
def __init__(self, num_features, num_classes, temp=0.05):
super().__init__()
self.mu = nn.Parameter(0.01 * torch.randn(num_classes, num_features))
self.sigma = nn.Parameter(torch... | 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... | KaiyangZhou/ssdg-benchmark | StochasticClassifier | false | 8,385 | [
"MIT"
] | 43 | aaa48be4f93b77347fbadff649be6b3e0f7a8779 | https://github.com/KaiyangZhou/ssdg-benchmark/tree/aaa48be4f93b77347fbadff649be6b3e0f7a8779 |
Highway | import torch
import torch.nn as nn
import torch.nn.functional as F
class Highway(nn.Module):
"""Highway network"""
def __init__(self, input_size):
super(Highway, self).__init__()
self.fc1 = nn.Linear(input_size, input_size, bias=True)
self.fc2 = nn.Linear(input_size, input_size, bias=... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | Kailianghu/Character-Aware-Neural-Language-Model | Highway | false | 8,386 | [
"MIT"
] | 35 | 6bd72ce00a3ac9eb152ba006bdae8a6922e0ad35 | https://github.com/Kailianghu/Character-Aware-Neural-Language-Model/tree/6bd72ce00a3ac9eb152ba006bdae8a6922e0ad35 |
BCEDiceLossWithLogits | import torch
import torch.nn as nn
import torch.utils.data
def flatten_samples(input_):
"""
Flattens a tensor or a variable such that the channel axis is first and the sample axis
is second. The shapes are transformed as follows:
(N, C, H, W) --> (C, N * H * W)
(N, C, D, H, W) --> (C, 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... | JonasHell/torch-em | BCEDiceLossWithLogits | false | 8,387 | [
"MIT"
] | 13 | 2e008e0cd2f0ea6681581374fce4f9f47b986d55 | https://github.com/JonasHell/torch-em/tree/2e008e0cd2f0ea6681581374fce4f9f47b986d55 |
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