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 |
|---|---|---|---|---|---|---|---|---|---|---|
HardSwish | import torch
from torch import nn
import torch.nn.functional as F
def hard_swish(x: 'torch.Tensor', inplace: 'bool'=False) ->torch.Tensor:
inner = F.relu6(x + 3.0).div_(6.0)
return x.mul_(inner) if inplace else x.mul(inner)
class HardSwish(nn.Module):
"""
HardSwish activiation layer.
Applies th... | 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
import torch.nn.functional as F
assert_size_stride = torch._C._dynam... | SimonCqk/towhee | HardSwish | false | 9,623 | [
"Apache-2.0"
] | 0 | a187833b1411216106a80a71e6f2c6e68e1be330 | https://github.com/SimonCqk/towhee/tree/a187833b1411216106a80a71e6f2c6e68e1be330 |
Conv2dSame | import math
import torch
from torch import nn
from typing import List
from typing import Union
import torch.nn.functional as F
from typing import Optional
from typing import Tuple
from torch.nn.common_types import _size_2_t
def get_same_padding(x: 'int', k: 'int', s: 'int', d: 'int') ->int:
"""
Calculate asym... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
from torch import nn
from typing import List
from typing import Unio... | SimonCqk/towhee | Conv2dSame | false | 9,624 | [
"Apache-2.0"
] | 0 | a187833b1411216106a80a71e6f2c6e68e1be330 | https://github.com/SimonCqk/towhee/tree/a187833b1411216106a80a71e6f2c6e68e1be330 |
KnowledgeDistillationLoss | import torch
import torch.nn as nn
class KnowledgeDistillationLoss(nn.Module):
def __init__(self, reduction='mean', alpha=1.0):
super().__init__()
self.reduction = reduction
self.alpha = alpha
def forward(self, inputs, targets, mask=None):
inputs = inputs.narrow(1, 0, targets... | 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
... | VitoPalmisano/MiB | KnowledgeDistillationLoss | false | 9,625 | [
"MIT"
] | 0 | 4b3d81e593471f2fb57abd852114a389ead3905c | https://github.com/VitoPalmisano/MiB/tree/4b3d81e593471f2fb57abd852114a389ead3905c |
TransformerLayer | import torch
import torch.nn as nn
class TransformerLayer(nn.Module):
def __init__(self, c, num_heads):
super().__init__()
self.q = nn.Linear(c, c, bias=False)
self.k = nn.Linear(c, c, bias=False)
self.v = nn.Linear(c, c, bias=False)
self.ma = nn.MultiheadAttention(embed_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.... | Lalihoo/yolov5-detect | TransformerLayer | false | 9,626 | [
"MIT"
] | 0 | 265c3137ea3586d913541501a1562488fbe59e9e | https://github.com/Lalihoo/yolov5-detect/tree/265c3137ea3586d913541501a1562488fbe59e9e |
GELU | import torch
from torch import nn
import torch.nn.functional as F
class GELU(nn.Module):
"""
GELU activiation layer.
Applies the Gaussian Error Linear Units function (w/ dummy inplace arg)
Described in: https://arxiv.org/abs/1606.08415.
Args:
inplace(`Bool`):
whether use inpl... | 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... | SimonCqk/towhee | GELU | false | 9,627 | [
"Apache-2.0"
] | 0 | a187833b1411216106a80a71e6f2c6e68e1be330 | https://github.com/SimonCqk/towhee/tree/a187833b1411216106a80a71e6f2c6e68e1be330 |
Classify | import torch
import torch.nn as nn
def autopad(k, p=None):
if p is None:
p = k // 2 if isinstance(k, int) else [(x // 2) for x in k]
return p
class Classify(nn.Module):
def __init__(self, c1, c2, k=1, s=1, p=None, g=1):
super().__init__()
self.aap = nn.AdaptiveAvgPool2d(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... | Lalihoo/yolov5-detect | Classify | false | 9,628 | [
"MIT"
] | 0 | 265c3137ea3586d913541501a1562488fbe59e9e | https://github.com/Lalihoo/yolov5-detect/tree/265c3137ea3586d913541501a1562488fbe59e9e |
ConvMlp | import torch
from torch import nn
class ConvMlp(nn.Module):
""" MLP using 1x1 convs that keeps spatial dims
"""
def __init__(self, in_features, hidden_features=None, out_features=None,
act_layer=nn.ReLU, norm_layer=None, drop=0.0):
super().__init__()
out_features = out_features or... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | SimonCqk/towhee | ConvMlp | false | 9,629 | [
"Apache-2.0"
] | 0 | a187833b1411216106a80a71e6f2c6e68e1be330 | https://github.com/SimonCqk/towhee/tree/a187833b1411216106a80a71e6f2c6e68e1be330 |
CosineClassifier | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
def cosine_fully_connected_layer(x_in, weight, scale=None, bias=None,
normalize_x=True, normalize_w=True):
assert x_in.dim() == 2
assert weight.dim() == 2
assert x_in.size(1) == weight.size(0)
if normalize_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.... | ZRJMoon/OMIT | CosineClassifier | false | 9,630 | [
"MIT"
] | 0 | bb063b4ac5d4fd60b28b17cb8d2119da92f936f4 | https://github.com/ZRJMoon/OMIT/tree/bb063b4ac5d4fd60b28b17cb8d2119da92f936f4 |
ConvolModel | import torch
import torch.nn as nn
import torch.nn.functional as F
class ConvolModel(nn.Module):
def __init__(self):
super(ConvolModel, self).__init__()
self.conv1 = nn.Conv2d(1, 5, 2)
self.conv2 = nn.Conv2d(5, 10, 2)
self.conv3 = nn.Conv2d(10, 10, 2)
def forward(self, x):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | VVKot/mlinseconds-die-hard | ConvolModel | false | 9,631 | [
"MIT"
] | 0 | dacbd448180bc992e0dab9e4b27bb594235d8c44 | https://github.com/VVKot/mlinseconds-die-hard/tree/dacbd448180bc992e0dab9e4b27bb594235d8c44 |
GluMlp | import torch
from torch import nn
class GluMlp(nn.Module):
""" MLP w/ GLU style gating
See: https://arxiv.org/abs/1612.08083, https://arxiv.org/abs/2002.05202
"""
def __init__(self, in_features, hidden_features=None, out_features=None,
act_layer=nn.Sigmoid, drop=0.0):
super().__init__... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | SimonCqk/towhee | GluMlp | false | 9,632 | [
"Apache-2.0"
] | 0 | a187833b1411216106a80a71e6f2c6e68e1be330 | https://github.com/SimonCqk/towhee/tree/a187833b1411216106a80a71e6f2c6e68e1be330 |
NaiveGroupNorm | from torch.nn import Module
import torch
from torch.nn import Parameter
from torch.nn import init
import torch.nn.parallel
import torch.utils.data
class NaiveGroupNorm(Module):
"""NaiveGroupNorm implements Group Normalization with the high-level matrix operations in PyTorch.
It is a temporary solution to expo... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch.nn import Module
from torch.nn import Parameter
from torch.nn import... | UrwLee/AdelaiDet | NaiveGroupNorm | false | 9,633 | [
"BSD-2-Clause"
] | 0 | 4cd88a80355d21261e94400767f44701ebc4b402 | https://github.com/UrwLee/AdelaiDet/tree/4cd88a80355d21261e94400767f44701ebc4b402 |
elu_modified | import torch
import torch.nn as nn
import torch.utils.data
class elu_modified(nn.Module):
def __init__(self, alpha=1.0, shift=5.0, epsilon=1e-07):
super(elu_modified, self).__init__()
self.alpha = alpha
self.shift = shift
self.epsilon = epsilon
self.elu = nn.ELU(alpha=alph... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dy... | aasensio/umal_pytorch | elu_modified | false | 9,634 | [
"MIT"
] | 0 | 17bf1fee006c26dc277eb31f22aee022246c0367 | https://github.com/aasensio/umal_pytorch/tree/17bf1fee006c26dc277eb31f22aee022246c0367 |
HuberLoss | import torch
import torch.nn as nn
class HuberLoss(nn.Module):
def __init__(self, delta=1):
super().__init__()
self.delta = delta
def forward(self, sr, hr):
l1 = torch.abs(sr - hr)
mask = l1 < self.delta
sq_loss = 0.5 * l1 ** 2
abs_loss = self.delta * (l1 - 0.... | 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
... | Vidit631/FLAVR | HuberLoss | false | 9,635 | [
"Apache-2.0"
] | 0 | c1cf558190761b244736786c44fe45ca114331f2 | https://github.com/Vidit631/FLAVR/tree/c1cf558190761b244736786c44fe45ca114331f2 |
FocalLoss | import torch
import torch.nn as nn
class FocalLoss(nn.Module):
def __init__(self, gamma=2, eps=1e-07):
super(FocalLoss, self).__init__()
self.gamma = gamma
self.eps = eps
self.ce = nn.CrossEntropyLoss()
def forward(self, input, target):
logp = self.ce(input, target)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | T-Visor/face.evoLVe | FocalLoss | false | 9,636 | [
"MIT"
] | 0 | 73f41a63eec2d95928d4a5401977d4a913d97eba | https://github.com/T-Visor/face.evoLVe/tree/73f41a63eec2d95928d4a5401977d4a913d97eba |
ReExp_Layer | import torch
import torch.nn as nn
class ReExp_Layer(nn.Module):
"""
Description:
A modified exponential layer.
Only the negative part of the exponential retains.
The positive part is linear: y=x+1.
"""
def __init__(self):
super().__init__()
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | Woodenonez/SimMotionPred_MDN_Pytorch | ReExp_Layer | false | 9,637 | [
"MIT"
] | 0 | 7c1b3cf4f3cd2a63d28d0ca85b6aa20675b7f212 | https://github.com/Woodenonez/SimMotionPred_MDN_Pytorch/tree/7c1b3cf4f3cd2a63d28d0ca85b6aa20675b7f212 |
MLPAutoencoder | import torch
def choose_nonlinearity(name):
nl = None
if name == 'tanh':
nl = torch.tanh
elif name == 'relu':
nl = torch.relu
elif name == 'sigmoid':
nl = torch.sigmoid
elif name == 'softplus':
nl = torch.nn.functional.softplus
elif name == 'selu':
nl = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride ... | UlyssesZh/selfsup_hnn | MLPAutoencoder | false | 9,638 | [
"MIT"
] | 0 | fedd261be81b38ec179cc71ea75d91964985a9e8 | https://github.com/UlyssesZh/selfsup_hnn/tree/fedd261be81b38ec179cc71ea75d91964985a9e8 |
EntMaxSelectLayer | from torch.autograd import Function
import torch
import torch.nn as nn
def _make_ix_like(input, dim=0):
d = input.size(dim)
rho = torch.arange(1, d + 1, device=input.device, dtype=input.dtype)
view = [1] * input.dim()
view[0] = -1
return rho.view(view).transpose(0, dim)
def entmax15(input, dim=-... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | YotamElor/ae-smote | EntMaxSelectLayer | false | 9,639 | [
"MIT"
] | 0 | 730ccc414c3b832a72a48087e709d283e27e273b | https://github.com/YotamElor/ae-smote/tree/730ccc414c3b832a72a48087e709d283e27e273b |
Affine | import torch
import torch.nn as nn
class Affine(nn.Module):
def __init__(self, dim):
super().__init__()
self.alpha = nn.Parameter(torch.ones(dim))
self.beta = nn.Parameter(torch.zeros(dim))
def forward(self, x):
return self.alpha * x + self.beta
def get_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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Uzair-Khattak/deit | Affine | false | 9,640 | [
"Apache-2.0"
] | 0 | 896004fc84d4ad2c4c9aa792822df7426af5903d | https://github.com/Uzair-Khattak/deit/tree/896004fc84d4ad2c4c9aa792822df7426af5903d |
Learned_Aggregation_Layer | import torch
import torch.nn as nn
class Learned_Aggregation_Layer(nn.Module):
def __init__(self, dim, num_heads=1, qkv_bias=False, qk_scale=None,
attn_drop=0.0, proj_drop=0.0):
super().__init__()
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = qk_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
from torch._inductor.runtime.... | Uzair-Khattak/deit | Learned_Aggregation_Layer | false | 9,641 | [
"Apache-2.0"
] | 0 | 896004fc84d4ad2c4c9aa792822df7426af5903d | https://github.com/Uzair-Khattak/deit/tree/896004fc84d4ad2c4c9aa792822df7426af5903d |
AdversarialNetwork | import torch
from torch import nn
def init_weights(layer):
"""Init weights for layers w.r.t. the original paper."""
layer_name = layer.__class__.__name__
if layer_name.find('Conv') != -1:
layer.weight.data.normal_(0.0, 0.02)
elif layer_name.find('BatchNorm') != -1:
layer.weight.data.no... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | adarshchbs/adda_sketch | AdversarialNetwork | false | 9,642 | [
"MIT"
] | 0 | 25f7adf3563d8e1edb8c431fb93876bbed4d4e76 | https://github.com/adarshchbs/adda_sketch/tree/25f7adf3563d8e1edb8c431fb93876bbed4d4e76 |
SC | import torch
import torch.nn as nn
class SC(nn.Module):
def __init__(self):
super(SC, self).__init__()
kernel_size = 3
self.spatial = nn.Conv2d(2, 1, kernel_size, stride=1, padding=(
kernel_size - 1) // 2)
def forward(self, x):
x_compress = torch.cat((torch.max(x,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Willamjie/CCWH | SC | false | 9,643 | [
"MIT"
] | 0 | 5217d76f8d112a17b2e00775b812387ab71ce798 | https://github.com/Willamjie/CCWH/tree/5217d76f8d112a17b2e00775b812387ab71ce798 |
CosineLoss | import torch
import torch.nn.functional as F
class CosineLoss(torch.nn.Module):
def __init__(self):
super(CosineLoss, self).__init__()
self.metrics = lambda x, y: 1 - torch.mean(F.cosine_similarity(x, y,
dim=-1))
def forward(self, x, label):
return self.metrics(x, label)
... | 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.functional a... | ackness/eth-xgaze-estimator | CosineLoss | false | 9,644 | [
"MIT"
] | 0 | b617cda6505885942c81b7f2d41399b62985b9a7 | https://github.com/ackness/eth-xgaze-estimator/tree/b617cda6505885942c81b7f2d41399b62985b9a7 |
GCN | import torch
from torch import nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.utils.data
class Conv2D(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, padding=
'same', stride=1, dilation=1, groups=1):
super(Conv2D, self).__init__()
assert ty... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.nn.functional as F
import torch.nn.parallel
im... | UrwLee/AdelaiDet | GCN | false | 9,645 | [
"BSD-2-Clause"
] | 0 | 4cd88a80355d21261e94400767f44701ebc4b402 | https://github.com/UrwLee/AdelaiDet/tree/4cd88a80355d21261e94400767f44701ebc4b402 |
DownsampleBlock | import torch
from torch import nn
class DownsampleBlock(nn.Module):
def __init__(self, in_channels, out_channels):
super(DownsampleBlock, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=2,
stride=2)
self.actv = nn.PReLU(out_channels)
def forw... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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... | XaviGurrola/RDUNet | DownsampleBlock | false | 9,646 | [
"MIT"
] | 0 | 549fc88c6faef1b310773944fc3988e22030d94d | https://github.com/XaviGurrola/RDUNet/tree/549fc88c6faef1b310773944fc3988e22030d94d |
weighted_mae_windows | import torch
import torch.nn as nn
class weighted_mae_windows(nn.Module):
def __init__(self, weights=(0.5, 1.2, 1.4, 1.6, 1.8, 2.0), thresholds=(
5.0, 15.0, 30.0, 40.0, 45.0)):
super(weighted_mae_windows, self).__init__()
assert len(thresholds) + 1 == len(weights)
self.weights = w... | 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
... | YuchenGUOGYC/gan_for_radar_extrapolation | weighted_mae_windows | false | 9,647 | [
"MIT"
] | 0 | cc43e6a691a81355faf0cda53a6b5555e886d75c | https://github.com/YuchenGUOGYC/gan_for_radar_extrapolation/tree/cc43e6a691a81355faf0cda53a6b5555e886d75c |
Block | import math
import torch
import torch.nn as nn
def gelu(x):
""" Original Implementation of the gelu activation function in Google Bert repo when initialy created.
For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
0.5 * x * (1 + torch.tanh(math.sqrt(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.... | SpringWave1/AutoGAN | Block | false | 9,648 | [
"MIT"
] | 0 | 209bd01b02f15847bd342d4019f87aef5440bda8 | https://github.com/SpringWave1/AutoGAN/tree/209bd01b02f15847bd342d4019f87aef5440bda8 |
OutputBlock | import torch
from torch import nn
class OutputBlock(nn.Module):
def __init__(self, in_channels, out_channels):
super(OutputBlock, self).__init__()
self.conv_1 = nn.Conv2d(in_channels, in_channels, 3, padding=1)
self.conv_2 = nn.Conv2d(in_channels, out_channels, 3, padding=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
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | XaviGurrola/RDUNet | OutputBlock | false | 9,649 | [
"MIT"
] | 0 | 549fc88c6faef1b310773944fc3988e22030d94d | https://github.com/XaviGurrola/RDUNet/tree/549fc88c6faef1b310773944fc3988e22030d94d |
MLP | import torch
def choose_nonlinearity(name):
nl = None
if name == 'tanh':
nl = torch.tanh
elif name == 'relu':
nl = torch.relu
elif name == 'sigmoid':
nl = torch.sigmoid
elif name == 'softplus':
nl = torch.nn.functional.softplus
elif name == 'selu':
nl = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride ... | UlyssesZh/selfsup_hnn | MLP | false | 9,650 | [
"MIT"
] | 0 | fedd261be81b38ec179cc71ea75d91964985a9e8 | https://github.com/UlyssesZh/selfsup_hnn/tree/fedd261be81b38ec179cc71ea75d91964985a9e8 |
ChannelAttentionModule | import torch
import torch.nn as nn
class ChannelAttentionModule(nn.Module):
def __init__(self):
super().__init__()
self.gamma = nn.Parameter(torch.zeros(1))
self.softmax = nn.Softmax(dim=-1)
def forward(self, x):
"""
inputs :
x : feature maps from feature ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | YuSuen/ACCycleGAN | ChannelAttentionModule | false | 9,651 | [
"MIT"
] | 0 | e407f2e6e7148181109d6d49b5e1006ae26493e4 | https://github.com/YuSuen/ACCycleGAN/tree/e407f2e6e7148181109d6d49b5e1006ae26493e4 |
ConditionTime | import torch
from torch import nn as nn
def condition_time(x, i=0, size=(12, 16), seq_len=15):
"""create one hot encoded time image-layers, i in [1, seq_len]"""
assert i < seq_len
times = torch.eye(seq_len, dtype=x.dtype, device=x.device)[i].unsqueeze(-1
).unsqueeze(-1)
ones = torch.ones(1, *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 import nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._emp... | ValterFallenius/metnet | ConditionTime | false | 9,652 | [
"MIT"
] | 0 | 7cde48a7b5fc0b69a8ce9083f934949362620fd5 | https://github.com/ValterFallenius/metnet/tree/7cde48a7b5fc0b69a8ce9083f934949362620fd5 |
Model | import torch
import torch.nn as nn
import torch.nn.functional as f
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
self.conv = nn.Conv2d(1, 16, 5)
self.pool = nn.MaxPool2d(2, 2)
self.fc = nn.Linear(2304, 10)
def forward(self, x):
x = self.poo... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | aabobakr/adversarial-robustness-toolbox | Model | false | 9,653 | [
"MIT"
] | 0 | d62b2606132d6e6fd5946d6bdc8f1da940eb3282 | https://github.com/aabobakr/adversarial-robustness-toolbox/tree/d62b2606132d6e6fd5946d6bdc8f1da940eb3282 |
ASPP | import torch
from torch import nn
import torch.nn.functional as F
class ASPP(nn.Module):
"""
Atrous spatial pyramid pooling used in object detection and segmentation.
"""
def __init__(self, in_channel=512, depth=256):
super().__init__()
self.mean = nn.AdaptiveAvgPool2d((1, 1))
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | SimonCqk/towhee | ASPP | false | 9,654 | [
"Apache-2.0"
] | 0 | a187833b1411216106a80a71e6f2c6e68e1be330 | https://github.com/SimonCqk/towhee/tree/a187833b1411216106a80a71e6f2c6e68e1be330 |
Normalize | import torch
import torch.nn as nn
import torch.optim
import torch.nn.parallel
class Normalize(nn.Module):
def __init__(self, power=2):
super(Normalize, self).__init__()
self.power = power
def forward(self, x):
norm = x.pow(self.power).sum(1, keepdim=True).pow(1.0 / self.power)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.optim
import torch.nn.parallel
assert_size_s... | abeSanchez/FeatureDecoupling | Normalize | false | 9,655 | [
"MIT"
] | 0 | 2a5ace5d057714b0b8657c75f1cff41e779b0ba4 | https://github.com/abeSanchez/FeatureDecoupling/tree/2a5ace5d057714b0b8657c75f1cff41e779b0ba4 |
Attention | import torch
import torch.nn as nn
import torch.nn.functional as F
class Attention(nn.Module):
"""Defining the attention layer to be used with Bi-LSTM"""
def __init__(self, hidden_dim):
"""Constructor for the Attention class.
Args:
hidden_dim (int): The double of the hidden vector size of the... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | abhinavbh08/NNTI-WS2021-NLP-Project | Attention | false | 9,656 | [
"MIT"
] | 0 | 946cfdcb0e0e64969d12423fa1b26dad3cb2d417 | https://github.com/abhinavbh08/NNTI-WS2021-NLP-Project/tree/946cfdcb0e0e64969d12423fa1b26dad3cb2d417 |
MLP | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn
class MLP(nn.Module):
"""
This is just an MLP with 1 hidden layer
"""
def __init__(self, n_units, dropout=0.1):
super(MLP, self).__init__()
self.w_1 = nn.Linear(n_units, 2048)
self.w_2 = nn.Line... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | adijo/ift6135-rnn | MLP | false | 9,657 | [
"Apache-2.0"
] | 0 | 88ebcd621cea4042f5ada688f2452ce25d02b761 | https://github.com/adijo/ift6135-rnn/tree/88ebcd621cea4042f5ada688f2452ce25d02b761 |
Word2Vec | import torch
import torch.nn as nn
import torch.nn.functional as F
class Word2Vec(nn.Module):
def __init__(self, vocabulary_size, embedding_size):
super(Word2Vec, self).__init__()
self.w1 = nn.Parameter(torch.randn(vocabulary_size, embedding_size,
requires_grad=True))
self.w2 ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | abhinavbh08/NNTI-WS2021-NLP-Project | Word2Vec | false | 9,658 | [
"MIT"
] | 0 | 946cfdcb0e0e64969d12423fa1b26dad3cb2d417 | https://github.com/abhinavbh08/NNTI-WS2021-NLP-Project/tree/946cfdcb0e0e64969d12423fa1b26dad3cb2d417 |
QNetwork | import torch
import torch.nn.functional as F
import torch.nn as nn
class QNetwork(nn.Module):
"""Actor (Policy) Model."""
def __init__(self, state_size, hidden_layer_size, action_size, seed):
"""Initialize parameters and build model.
Params
======
state_size (int): Dimensi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | ablou1/dqn-navigation | QNetwork | false | 9,659 | [
"MIT"
] | 0 | c89011220983061685ae4501d0207b8958eafc21 | https://github.com/ablou1/dqn-navigation/tree/c89011220983061685ae4501d0207b8958eafc21 |
decoder3 | import torch
import torch.nn as 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.UpsamplingNea... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | SofiaValdiviesov/LinearStyleTransfer | decoder3 | false | 9,660 | [
"BSD-2-Clause"
] | 0 | 6837c6a9be16bb5981fa0744e5d23f61d08e6940 | https://github.com/SofiaValdiviesov/LinearStyleTransfer/tree/6837c6a9be16bb5981fa0744e5d23f61d08e6940 |
LanguageModelCriterion | import torch
import torch.nn as nn
from torch.autograd import *
class LanguageModelCriterion(nn.Module):
def __init__(self):
super(LanguageModelCriterion, self).__init__()
def forward(self, input, target, mask):
target = target[:, :input.size(1)]
mask = mask[:, :input.size(1)]
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.autograd import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | Zhendong-Wang/arsm_image_captioning | LanguageModelCriterion | false | 9,661 | [
"MIT"
] | 0 | 2282b76ab03b53952269d94d6c4b19ab98636ca5 | https://github.com/Zhendong-Wang/arsm_image_captioning/tree/2282b76ab03b53952269d94d6c4b19ab98636ca5 |
GEGLU | import torch
from torch import nn
import torch.nn.functional as F
class GEGLU(nn.Module):
def forward(self, x):
x, gates = x.chunk(2, dim=-1)
return x * F.gelu(gates)
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
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | adam-mehdi/TimeSformer-pytorch | GEGLU | false | 9,662 | [
"MIT"
] | 0 | 4e6484dba2d3f9aeaaad09a3a310c0ea36b459e3 | https://github.com/adam-mehdi/TimeSformer-pytorch/tree/4e6484dba2d3f9aeaaad09a3a310c0ea36b459e3 |
LogSoftmaxOutput | import torch
import torch.nn as nn
class Linear(nn.Linear):
"""
Apply linear projection to the last dimention of a tensor.
"""
def forward(self, x):
size = x.size()
return super().forward(x.contiguous().view(-1, size[-1])).view(*
size[:-1], -1)
class LogSoftmaxOutput(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.... | aishwaryaprabhat/BRIDGE-Tabular-Semantic-Parsing | LogSoftmaxOutput | false | 9,663 | [
"BSD-3-Clause"
] | 0 | 640858024df444006dfae106a28fdb58f36f687e | https://github.com/aishwaryaprabhat/BRIDGE-Tabular-Semantic-Parsing/tree/640858024df444006dfae106a28fdb58f36f687e |
AdjustNormFunc | import torch
import torch.nn as nn
class AdjustNormFunc(nn.Module):
"""Creates a BatchNorm-like module using func : x = func(x) * scale + shift"""
def __init__(self, nf, func=torch.tanh, name=None):
super().__init__()
self.func = func
self.name = name
self.nf = nf
self... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | akashpalrecha/tanhNorm | AdjustNormFunc | false | 9,664 | [
"Apache-2.0"
] | 0 | bff7ba81aa5c805c423a59a36339254c83a3c28a | https://github.com/akashpalrecha/tanhNorm/tree/bff7ba81aa5c805c423a59a36339254c83a3c28a |
PointerSwitch | import torch
import torch.nn as nn
class Linear(nn.Linear):
"""
Apply linear projection to the last dimention of a tensor.
"""
def forward(self, x):
size = x.size()
return super().forward(x.contiguous().view(-1, size[-1])).view(*
size[:-1], -1)
class ConcatAndProject(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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | aishwaryaprabhat/BRIDGE-Tabular-Semantic-Parsing | PointerSwitch | false | 9,665 | [
"BSD-3-Clause"
] | 0 | 640858024df444006dfae106a28fdb58f36f687e | https://github.com/aishwaryaprabhat/BRIDGE-Tabular-Semantic-Parsing/tree/640858024df444006dfae106a28fdb58f36f687e |
DenoisingBlock | import torch
from torch import nn
class DenoisingBlock(nn.Module):
def __init__(self, in_channels, inner_channels, out_channels):
super(DenoisingBlock, self).__init__()
self.conv_0 = nn.Conv2d(in_channels, inner_channels, 3, padding=1)
self.conv_1 = nn.Conv2d(in_channels + inner_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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | XaviGurrola/RDUNet | DenoisingBlock | false | 9,666 | [
"MIT"
] | 0 | 549fc88c6faef1b310773944fc3988e22030d94d | https://github.com/XaviGurrola/RDUNet/tree/549fc88c6faef1b310773944fc3988e22030d94d |
ConvGRUCell | import torch
from torch import nn as nn
import torch.nn.functional as F
def one_param(m):
"""First parameter in `m`"""
return next(m.parameters())
class ConvGRUCell(nn.Module):
def __init__(self, input_dim, hidden_dim, kernel_size=(3, 3), bias=True,
activation=F.tanh, batchnorm=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.triton_helpers import libdevice
from torch import n... | ValterFallenius/metnet | ConvGRUCell | false | 9,667 | [
"MIT"
] | 0 | 7cde48a7b5fc0b69a8ce9083f934949362620fd5 | https://github.com/ValterFallenius/metnet/tree/7cde48a7b5fc0b69a8ce9083f934949362620fd5 |
PerceptualLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class PerceptualLoss(nn.Module):
def __init__(self):
super().__init__()
self.tgt_gm = None
def gram_matrix(self, x):
a, b, c, d = x.shape
features = x.view(a * b, c * d)
G = torch.mm(features, 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_... | aadhithya/mobilenet-styletransfer | PerceptualLoss | false | 9,668 | [
"MIT"
] | 0 | 58e2c29020864d82d92d52d01427618bc35773fd | https://github.com/aadhithya/mobilenet-styletransfer/tree/58e2c29020864d82d92d52d01427618bc35773fd |
MeanEmbedding | import torch
import torch.nn as nn
import torch.utils.data
import torch.multiprocessing
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_):... | 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.data
import torch.multiprocessing
import torch.nn.modules.loss
from scipy.sparse import *
assert_si... | LucasAPayne/graph4nlp | MeanEmbedding | false | 9,669 | [
"Apache-2.0"
] | 0 | 3b72308f6ed9ce04c535f78b4b21b6ae0a8f5421 | https://github.com/LucasAPayne/graph4nlp/tree/3b72308f6ed9ce04c535f78b4b21b6ae0a8f5421 |
LayerScale_Block | import torch
import torch.nn as nn
class Mlp(nn.Module):
def __init__(self, in_features, hidden_features=None, out_features=None,
act_layer=nn.GELU, drop=0.0):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Uzair-Khattak/deit | LayerScale_Block | false | 9,670 | [
"Apache-2.0"
] | 0 | 896004fc84d4ad2c4c9aa792822df7426af5903d | https://github.com/Uzair-Khattak/deit/tree/896004fc84d4ad2c4c9aa792822df7426af5903d |
ChannelSqueezeAndSpatialExcitation | import torch
import torch.nn as nn
from torch.nn.modules.loss import *
from torch.nn.modules import *
from torch.optim import *
from torch.optim.lr_scheduler import *
import torch.distributed
import torch.backends
class ChannelSqueezeAndSpatialExcitation(nn.Module):
"""
The sSE (Channel Squeeze and Spatial Ex... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.nn.modules.loss import *
from torch.nn.modules ... | YaLTeR/catalyst | ChannelSqueezeAndSpatialExcitation | false | 9,671 | [
"Apache-2.0"
] | 0 | 4b875b50b3c63ac2dac1f19399af0c016dfb4e2f | https://github.com/YaLTeR/catalyst/tree/4b875b50b3c63ac2dac1f19399af0c016dfb4e2f |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self, input_dim, n_classes):
super(Net, self).__init__()
self.n_classes = n_classes
self.fc = nn.Linear(input_dim, 2048)
def _forward2(self, x):
x = self.fc(x)
x = F.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | alexanderrichard/cvpr2016_python3 | Net | false | 9,672 | [
"MIT"
] | 0 | cddd77420d1be25fe2bba3b069d2cb966c6e366a | https://github.com/alexanderrichard/cvpr2016_python3/tree/cddd77420d1be25fe2bba3b069d2cb966c6e366a |
Attention | import math
import torch
from torch import nn
from torch.nn import functional as F
class Attention(nn.Module):
def __init__(self, hidden_size):
super(Attention, self).__init__()
self.hidden_size = hidden_size
self.attn = nn.Linear(self.hidden_size * 2, hidden_size)
self.v = nn.Par... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | alexarnimueller/smiles-transformer | Attention | false | 9,673 | [
"MIT"
] | 0 | 4584a0bd043d6659a941589677951b2c6823cd2a | https://github.com/alexarnimueller/smiles-transformer/tree/4584a0bd043d6659a941589677951b2c6823cd2a |
Layer_scale_init_Block_only_token | import torch
import torch.nn as nn
class Mlp(nn.Module):
def __init__(self, in_features, hidden_features=None, out_features=None,
act_layer=nn.GELU, drop=0.0):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Uzair-Khattak/deit | Layer_scale_init_Block_only_token | false | 9,674 | [
"Apache-2.0"
] | 0 | 896004fc84d4ad2c4c9aa792822df7426af5903d | https://github.com/Uzair-Khattak/deit/tree/896004fc84d4ad2c4c9aa792822df7426af5903d |
CNN | import torch
from torch import nn
import torch.nn.functional as F
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.conv1 = nn.Conv2d(3, 32, 3)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(32, 64, 3)
self.conv3 = nn.Conv2d(64, 64, 3)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | ZJU-DistributedAI/RDFL-GAN | CNN | false | 9,675 | [
"Apache-2.0"
] | 0 | e5f10b071d25db7931749515b1b8a3c477a91257 | https://github.com/ZJU-DistributedAI/RDFL-GAN/tree/e5f10b071d25db7931749515b1b8a3c477a91257 |
TensorCumsum | import torch
class TensorCumsum(torch.nn.Module):
def __init__(self, dim=1):
super().__init__()
self.dim = dim
def forward(self, input):
return torch.cumsum(input, dim=self.dim)
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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | Minyus/kedex | TensorCumsum | false | 9,676 | [
"Apache-2.0"
] | 0 | 92f952eed3cb6109bc783f449051f2bd13579d2a | https://github.com/Minyus/kedex/tree/92f952eed3cb6109bc783f449051f2bd13579d2a |
Actor | import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class Actor(nn.Module):
"""Actor (Policy) Model."""
def __init__(self, state_size, action_size, seed, f... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | adriaciurana/udacity-project-3 | Actor | false | 9,677 | [
"MIT"
] | 0 | 806f78e35a6699eeb0a3272e326d0edc199d16be | https://github.com/adriaciurana/udacity-project-3/tree/806f78e35a6699eeb0a3272e326d0edc199d16be |
encoder3 | import torch
import torch.nn as nn
class encoder3(nn.Module):
def __init__(self):
super(encoder3, self).__init__()
self.conv1 = nn.Conv2d(3, 3, 1, 1, 0)
self.reflecPad1 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv2 = nn.Conv2d(3, 64, 3, 1, 0)
self.relu2 = nn.ReLU(inplace=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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | SofiaValdiviesov/LinearStyleTransfer | encoder3 | false | 9,678 | [
"BSD-2-Clause"
] | 0 | 6837c6a9be16bb5981fa0744e5d23f61d08e6940 | https://github.com/SofiaValdiviesov/LinearStyleTransfer/tree/6837c6a9be16bb5981fa0744e5d23f61d08e6940 |
Critic | import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class Critic(nn.Module):
"""Critic (Value) Model."""
def __init__(self, state_size, action_size, seed, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | adriaciurana/udacity-project-3 | Critic | false | 9,679 | [
"MIT"
] | 0 | 806f78e35a6699eeb0a3272e326d0edc199d16be | https://github.com/adriaciurana/udacity-project-3/tree/806f78e35a6699eeb0a3272e326d0edc199d16be |
LocalDiscriminator | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
class LocalDiscriminator(nn.Module):
"""The local discriminator class.
A network that analyses the relation between the
output of the encoder y, and the feature map M.
It is called "local" because it compares y with... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | ValerioB88/self-supervised-relational-reasoning | LocalDiscriminator | false | 9,680 | [
"MIT"
] | 0 | 12692b93d5c8dd3f56a31aa8b790366556e7a621 | https://github.com/ValerioB88/self-supervised-relational-reasoning/tree/12692b93d5c8dd3f56a31aa8b790366556e7a621 |
CNN | import torch
import torch.nn as nn
import torch.nn.functional as F
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.conv1 = nn.Conv2d(3, 16, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(16, 32, 5)
self.gap = nn.AdaptiveAvgPool2d(1)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | ai-antena/cifar10 | CNN | false | 9,681 | [
"MIT"
] | 0 | a3c72693cffae4a5150f1ca5f19472098163ed1a | https://github.com/ai-antena/cifar10/tree/a3c72693cffae4a5150f1ca5f19472098163ed1a |
TensorLog | import torch
class TensorLog(torch.nn.Module):
def forward(self, input):
return torch.log(input)
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 math as tl_math
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_str... | Minyus/kedex | TensorLog | false | 9,682 | [
"Apache-2.0"
] | 0 | 92f952eed3cb6109bc783f449051f2bd13579d2a | https://github.com/Minyus/kedex/tree/92f952eed3cb6109bc783f449051f2bd13579d2a |
TensorExp | import torch
class TensorExp(torch.nn.Module):
def forward(self, input):
return torch.exp(input)
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 math as tl_math
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_str... | Minyus/kedex | TensorExp | false | 9,683 | [
"Apache-2.0"
] | 0 | 92f952eed3cb6109bc783f449051f2bd13579d2a | https://github.com/Minyus/kedex/tree/92f952eed3cb6109bc783f449051f2bd13579d2a |
TensorNearestPad | import torch
class TensorNearestPad(torch.nn.Module):
def __init__(self, lower=1, upper=1):
super().__init__()
assert isinstance(lower, int) and lower >= 0
assert isinstance(upper, int) and upper >= 0
self.lower = lower
self.upper = upper
def forward(self, input):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | Minyus/kedex | TensorNearestPad | false | 9,684 | [
"Apache-2.0"
] | 0 | 92f952eed3cb6109bc783f449051f2bd13579d2a | https://github.com/Minyus/kedex/tree/92f952eed3cb6109bc783f449051f2bd13579d2a |
CenterNessNet | import math
import torch
import torch.nn as nn
from torch.nn.modules.utils import _pair
class BasicBlock(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0):
super(BasicBlock, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ZCDu/CenternessNet | CenterNessNet | false | 9,685 | [
"MIT"
] | 0 | 03f5d01999a4e1595eaceef9f62b4450ed017843 | https://github.com/ZCDu/CenternessNet/tree/03f5d01999a4e1595eaceef9f62b4450ed017843 |
PriorDiscriminator | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
class PriorDiscriminator(nn.Module):
"""The prior discriminator class.
This discriminate between a vector drawn from random uniform,
and the vector y obtained as output of the encoder.
It enforces y to be close to 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
import torch.nn as nn
import ... | ValerioB88/self-supervised-relational-reasoning | PriorDiscriminator | false | 9,686 | [
"MIT"
] | 0 | 12692b93d5c8dd3f56a31aa8b790366556e7a621 | https://github.com/ValerioB88/self-supervised-relational-reasoning/tree/12692b93d5c8dd3f56a31aa8b790366556e7a621 |
TensorMin | import torch
def tensor_min(input, dim, keepdim=False):
if isinstance(dim, int):
return torch.min(input, dim=dim, keepdim=keepdim)[0]
else:
if isinstance(dim, tuple):
dim = list(dim)
for d in dim:
input = torch.min(input, dim=d, keepdim=keepdim)[0]
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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | Minyus/kedex | TensorMin | false | 9,687 | [
"Apache-2.0"
] | 0 | 92f952eed3cb6109bc783f449051f2bd13579d2a | https://github.com/Minyus/kedex/tree/92f952eed3cb6109bc783f449051f2bd13579d2a |
TensorRange | import torch
def tensor_max(input, dim, keepdim=False):
if isinstance(dim, int):
return torch.max(input, dim=dim, keepdim=keepdim)[0]
else:
if isinstance(dim, tuple):
dim = list(dim)
for d in dim:
input = torch.max(input, dim=d, keepdim=keepdim)[0]
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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | Minyus/kedex | TensorRange | false | 9,688 | [
"Apache-2.0"
] | 0 | 92f952eed3cb6109bc783f449051f2bd13579d2a | https://github.com/Minyus/kedex/tree/92f952eed3cb6109bc783f449051f2bd13579d2a |
GraphConvolution | from torch.nn import Module
import torch
from torch.nn import functional as F
from torch.nn import Parameter
import torch.utils.data
import torch.multiprocessing
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, 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.nn import Module
f... | LucasAPayne/graph4nlp | GraphConvolution | false | 9,689 | [
"Apache-2.0"
] | 0 | 3b72308f6ed9ce04c535f78b4b21b6ae0a8f5421 | https://github.com/LucasAPayne/graph4nlp/tree/3b72308f6ed9ce04c535f78b4b21b6ae0a8f5421 |
GlobalDiscriminator | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
class GlobalDiscriminator(nn.Module):
def __init__(self, y_size, M_channels):
super().__init__()
self.c0 = nn.Conv2d(M_channels, 64, kernel_size=3)
self.c1 = nn.Conv2d(64, 32, kernel_size=3)
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
import torch.nn as nn
import ... | ValerioB88/self-supervised-relational-reasoning | GlobalDiscriminator | false | 9,690 | [
"MIT"
] | 0 | 12692b93d5c8dd3f56a31aa8b790366556e7a621 | https://github.com/ValerioB88/self-supervised-relational-reasoning/tree/12692b93d5c8dd3f56a31aa8b790366556e7a621 |
AdaptiveAvgPool3dOutSize1 | import torch
import torch.nn as nn
from abc import abstractmethod
from typing import Tuple
import torch.utils.data
import torch.nn
class EfficientBlockBase(nn.Module):
"""
PyTorchVideo/accelerator provides a set of efficient blocks
that have optimal efficiency for each target hardware device.
Each ef... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from abc import abstractmethod
from typing import Tuple
import torch.utils.data
import torch.nn
assert_size_stride = t... | TheShadow29/pytorchvideo | AdaptiveAvgPool3dOutSize1 | false | 9,691 | [
"Apache-2.0"
] | 0 | 39a3e34e33fb0e1ec142288df08f6e8c3585961a | https://github.com/TheShadow29/pytorchvideo/tree/39a3e34e33fb0e1ec142288df08f6e8c3585961a |
TensorMax | import torch
def tensor_max(input, dim, keepdim=False):
if isinstance(dim, int):
return torch.max(input, dim=dim, keepdim=keepdim)[0]
else:
if isinstance(dim, tuple):
dim = list(dim)
for d in dim:
input = torch.max(input, dim=d, keepdim=keepdim)[0]
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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | Minyus/kedex | TensorMax | false | 9,692 | [
"Apache-2.0"
] | 0 | 92f952eed3cb6109bc783f449051f2bd13579d2a | https://github.com/Minyus/kedex/tree/92f952eed3cb6109bc783f449051f2bd13579d2a |
Affine2D | import torch
import torch.nn as nn
class Affine2D(nn.Module):
def __init__(self, cin):
"""
:param cin:
"""
super(Affine2D, self).__init__()
self.weight = nn.Parameter(torch.ones(1, cin, 1, 1))
self.bias = nn.Parameter(torch.zeros(1, cin, 1, 1))
def forward(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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | alexandre-giuly/Project-Acoustic-Scene-Classification-DCASE | Affine2D | false | 9,693 | [
"Apache-2.0"
] | 0 | 13b565c20e59f204151d2dafbd221c7e1b9303c5 | https://github.com/alexandre-giuly/Project-Acoustic-Scene-Classification-DCASE/tree/13b565c20e59f204151d2dafbd221c7e1b9303c5 |
ActorNetwork | import torch
import torch.nn.functional as F
import torch.nn as nn
class ActorNetwork(nn.Module):
def __init__(self, state_size, action_size, seed):
super(ActorNetwork, self).__init__()
torch.manual_seed(seed)
hidden1 = 64
hidden2 = 64
self.fc1 = nn.Linear(state_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
from torch._inductor.runtime.... | aishikawa/drl-impl | ActorNetwork | false | 9,694 | [
"MIT"
] | 0 | 1afe7426494cd94990cb4dae247486a25dfe37bf | https://github.com/aishikawa/drl-impl/tree/1afe7426494cd94990cb4dae247486a25dfe37bf |
GRUStep | import torch
import torch.nn as nn
import torch.utils.data
import torch.multiprocessing
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... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | LucasAPayne/graph4nlp | GRUStep | false | 9,695 | [
"Apache-2.0"
] | 0 | 3b72308f6ed9ce04c535f78b4b21b6ae0a8f5421 | https://github.com/LucasAPayne/graph4nlp/tree/3b72308f6ed9ce04c535f78b4b21b6ae0a8f5421 |
Network | import torch
import torch.nn as nn
import torch.nn.functional as F
class Network(nn.Module):
def __init__(self):
nn.Module.__init__(self)
self.l1 = nn.Linear(4, 24)
self.l5 = nn.Linear(24, 2)
def forward(self, x):
x = F.relu(self.l1(x))
x = self.l5(x)
return x... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | alexljenkins/reinforcement-learning-agents | Network | false | 9,696 | [
"MIT"
] | 0 | d5bdfad56c9b095d5bb0ac22ca69e19553327416 | https://github.com/alexljenkins/reinforcement-learning-agents/tree/d5bdfad56c9b095d5bb0ac22ca69e19553327416 |
MaskedTemporalPooling | import torch
from typing import Optional
import torch.utils.data
import torch.nn
class MaskedTemporalPooling(torch.nn.Module):
"""
Applies temporal pooling operations on masked inputs. For each pooling operation
all masked values are ignored.
"""
def __init__(self, method: 'str'):
"""
... | 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
assert_size_stride = torch._C._dynamo.guards.asse... | TheShadow29/pytorchvideo | MaskedTemporalPooling | false | 9,697 | [
"Apache-2.0"
] | 0 | 39a3e34e33fb0e1ec142288df08f6e8c3585961a | https://github.com/TheShadow29/pytorchvideo/tree/39a3e34e33fb0e1ec142288df08f6e8c3585961a |
InnerProductDecoder | import torch
from torch.nn import functional as F
import torch.nn as nn
import torch.utils.data
import torch.multiprocessing
import torch.nn.modules.loss
from scipy.sparse import *
def dropout(x, drop_prob, shared_axes=[], training=False):
"""
Apply dropout to input tensor.
Parameters
----------
i... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
import torch.multiprocessing
impor... | LucasAPayne/graph4nlp | InnerProductDecoder | false | 9,698 | [
"Apache-2.0"
] | 0 | 3b72308f6ed9ce04c535f78b4b21b6ae0a8f5421 | https://github.com/LucasAPayne/graph4nlp/tree/3b72308f6ed9ce04c535f78b4b21b6ae0a8f5421 |
LearnMaskedDefault | import torch
import torch.nn as nn
import torch.utils.data
import torch.nn
class LearnMaskedDefault(nn.Module):
"""
Learns default values to fill invalid entries within input tensors. The
invalid entries are represented by a mask which is passed into forward alongside
the input tensor. Note the defaul... | 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
import torch.nn
assert_size_stride = torch.... | TheShadow29/pytorchvideo | LearnMaskedDefault | false | 9,699 | [
"Apache-2.0"
] | 0 | 39a3e34e33fb0e1ec142288df08f6e8c3585961a | https://github.com/TheShadow29/pytorchvideo/tree/39a3e34e33fb0e1ec142288df08f6e8c3585961a |
ConvGLU | import torch
import torch.cuda
from torch import nn
import torch.distributed
import torch.utils.data
import torch.optim
def str2act(txt):
"""Translates text to neural network activation"""
return {'sigmoid': nn.Sigmoid(), 'relu': nn.ReLU(), 'none': nn.
Sequential(), 'lrelu': nn.LeakyReLU(0.2), 'selu':... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.cuda
from torch import nn
import torch.distributed
import torch.uti... | Oreoluwa1234/NeMo | ConvGLU | false | 9,700 | [
"Apache-2.0"
] | 0 | b01e3ceed34efe31fd43866685dbdd19a6b30928 | https://github.com/Oreoluwa1234/NeMo/tree/b01e3ceed34efe31fd43866685dbdd19a6b30928 |
TransposeMultiheadAttention | import torch
import torch.nn as nn
from typing import Optional
import torch.utils.data
import torch.nn
class TransposeMultiheadAttention(nn.Module):
"""
Wrapper for nn.MultiheadAttention which first transposes the input tensor
from (batch_size, seq_len, feature_dim) to (seq_length, batch_size, feature_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.... | TheShadow29/pytorchvideo | TransposeMultiheadAttention | false | 9,701 | [
"Apache-2.0"
] | 0 | 39a3e34e33fb0e1ec142288df08f6e8c3585961a | https://github.com/TheShadow29/pytorchvideo/tree/39a3e34e33fb0e1ec142288df08f6e8c3585961a |
LayerNorm | import torch
import torch.cuda
from torch import nn
import torch.distributed
from torch.nn import LayerNorm
import torch.utils.data
import torch.optim
class LayerNorm(nn.Module):
def __init__(self, channels, eps=0.0001):
super().__init__()
self.channels = channels
self.eps = eps
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.cuda
from torch import nn
import torch.distributed
import torch.ut... | Oreoluwa1234/NeMo | LayerNorm | false | 9,702 | [
"Apache-2.0"
] | 0 | b01e3ceed34efe31fd43866685dbdd19a6b30928 | https://github.com/Oreoluwa1234/NeMo/tree/b01e3ceed34efe31fd43866685dbdd19a6b30928 |
JustConvBody | import torch
import torch.nn as nn
import torch.nn.functional as F
def layer_init(layer, w_scale=1.0):
nn.init.orthogonal_(layer.weight.data)
layer.weight.data.mul_(w_scale)
nn.init.constant_(layer.bias.data, 0)
return layer
class JustConvBody(nn.Module):
def __init__(self, in_channels=4):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Louis-Bagot/DeepRL | JustConvBody | false | 9,703 | [
"MIT"
] | 0 | 0b152c52bbba90362c8276c223fee3f9a464eb32 | https://github.com/Louis-Bagot/DeepRL/tree/0b152c52bbba90362c8276c223fee3f9a464eb32 |
Context2AnswerAttention | import torch
import torch.nn as nn
import torch.utils.data
import torch.multiprocessing
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.Line... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | LucasAPayne/graph4nlp | Context2AnswerAttention | false | 9,704 | [
"Apache-2.0"
] | 0 | 3b72308f6ed9ce04c535f78b4b21b6ae0a8f5421 | https://github.com/LucasAPayne/graph4nlp/tree/3b72308f6ed9ce04c535f78b4b21b6ae0a8f5421 |
MaskedInstanceNorm1d | import torch
import torch.cuda
from torch import nn
import torch.distributed
import torch.utils.data
import torch.optim
class MaskedInstanceNorm1d(nn.Module):
"""Instance norm + masking."""
MAX_CNT = 100000.0
def __init__(self, d_channel: 'int', unbiased: 'bool'=True, affine:
'bool'=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
import torch.cuda
from torch... | Oreoluwa1234/NeMo | MaskedInstanceNorm1d | false | 9,705 | [
"Apache-2.0"
] | 0 | b01e3ceed34efe31fd43866685dbdd19a6b30928 | https://github.com/Oreoluwa1234/NeMo/tree/b01e3ceed34efe31fd43866685dbdd19a6b30928 |
TorchModule | import torch
import torch.nn
class TorchLinearModule(torch.nn.Module):
def __init__(self, in_size, out_size):
super(TorchLinearModule, self).__init__()
self._linear = torch.nn.Linear(in_size, out_size)
def forward(self, x):
return self._linear(x)
class TorchModule(torch.nn.Module):... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn
ass... | amit828as/ivy | TorchModule | false | 9,706 | [
"Apache-2.0"
] | 0 | fd12e513c58e337cc3775e456ad26a942a501c65 | https://github.com/amit828as/ivy/tree/fd12e513c58e337cc3775e456ad26a942a501c65 |
ConvReLUNorm | import torch
import torch.cuda
import torch.distributed
import torch.utils.data
import torch.optim
class ConvReLUNorm(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=1, dropout=0.0):
super(ConvReLUNorm, self).__init__()
self.conv = torch.nn.Conv1d(in_channels, out_chan... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Oreoluwa1234/NeMo | ConvReLUNorm | false | 9,707 | [
"Apache-2.0"
] | 0 | b01e3ceed34efe31fd43866685dbdd19a6b30928 | https://github.com/Oreoluwa1234/NeMo/tree/b01e3ceed34efe31fd43866685dbdd19a6b30928 |
LeakyReLU | import torch
class Activation(torch.nn.Module):
def __init__(self) ->None:
super().__init__()
def forward(self, inputs: 'torch.Tensor') ->torch.Tensor:
raise NotImplementedError
class LeakyReLU(Activation):
def forward(self, inputs: 'torch.Tensor') ->torch.Tensor:
return torch... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | altescy/xtorch | LeakyReLU | false | 9,708 | [
"MIT"
] | 0 | bcbbbe645f4d62c211af5b3555c526cc60792c32 | https://github.com/altescy/xtorch/tree/bcbbbe645f4d62c211af5b3555c526cc60792c32 |
ELU | import torch
class Activation(torch.nn.Module):
def __init__(self) ->None:
super().__init__()
def forward(self, inputs: 'torch.Tensor') ->torch.Tensor:
raise NotImplementedError
class ELU(Activation):
def forward(self, inputs: 'torch.Tensor') ->torch.Tensor:
return torch.nn.fu... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | altescy/xtorch | ELU | false | 9,709 | [
"MIT"
] | 0 | bcbbbe645f4d62c211af5b3555c526cc60792c32 | https://github.com/altescy/xtorch/tree/bcbbbe645f4d62c211af5b3555c526cc60792c32 |
FocalLoss | import torch
import torch.nn as nn
import torch.optim
class FocalLoss(torch.nn.Module):
"""Sigmoid focal cross entropy loss.
Focal loss down-weights well classified examples and focusses on the hard
examples. See https://arxiv.org/pdf/1708.02002.pdf for the loss definition.
"""
def __init__(self, gamma... | 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... | ValerioB88/self-supervised-relational-reasoning | FocalLoss | false | 9,710 | [
"MIT"
] | 0 | 12692b93d5c8dd3f56a31aa8b790366556e7a621 | https://github.com/ValerioB88/self-supervised-relational-reasoning/tree/12692b93d5c8dd3f56a31aa8b790366556e7a621 |
CriticNetwork | import torch
import torch.nn.functional as F
import torch.nn as nn
class CriticNetwork(nn.Module):
def __init__(self, state_size, action_size, seed):
super(CriticNetwork, self).__init__()
torch.manual_seed(seed)
fcs1_units = 64
fc2_units = 64
self.fcs1 = nn.Linear(state_si... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | aishikawa/drl-impl | CriticNetwork | false | 9,711 | [
"MIT"
] | 0 | 1afe7426494cd94990cb4dae247486a25dfe37bf | https://github.com/aishikawa/drl-impl/tree/1afe7426494cd94990cb4dae247486a25dfe37bf |
DuelingNetwork | import torch
import torch.nn.functional as F
import torch.nn as nn
class DuelingNetwork(nn.Module):
def __init__(self, state_size, action_size, seed):
super(DuelingNetwork, self).__init__()
torch.manual_seed(seed)
hidden1 = 64
hidden2 = 64
self.fc1 = nn.Linear(state_size, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | aishikawa/drl-impl | DuelingNetwork | false | 9,712 | [
"MIT"
] | 0 | 1afe7426494cd94990cb4dae247486a25dfe37bf | https://github.com/aishikawa/drl-impl/tree/1afe7426494cd94990cb4dae247486a25dfe37bf |
ConvSigmoidInplace | import torch
from torch import nn
import torch.cuda
import torch.backends.cudnn
import torch.backends.mkl
import torch.backends.cuda
import torch.backends.quantized
class ConvSigmoidInplace(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, image_size):
super(ConvSigmoidInplace, 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 import nn
import torch.cuda
import torch.backends.cudnn
import torch.... | XiaobingSuper/intel-extension-for-pytorch | ConvSigmoidInplace | false | 9,713 | [
"Apache-2.0"
] | 0 | b61029be10e46e6d2e13b0e700c81f8e59164df0 | https://github.com/XiaobingSuper/intel-extension-for-pytorch/tree/b61029be10e46e6d2e13b0e700c81f8e59164df0 |
FocalLoss | import torch
from torch import nn
import torch.nn.functional as F
class FocalLoss(nn.Module):
def __init__(self, alpha=1, gamma=2):
super().__init__()
self.alpha = alpha
self.gamma = gamma
def forward(self, x, y):
ce = F.binary_cross_entropy_with_logits(x, y)
fc = sel... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch ... | agrawalshubham01/FracNet | FocalLoss | false | 9,714 | [
"Apache-2.0"
] | 0 | 8b912ca65651ff0ee203d9d73cf6ca18539728ac | https://github.com/agrawalshubham01/FracNet/tree/8b912ca65651ff0ee203d9d73cf6ca18539728ac |
MultiLayerPerceptron | import torch
import torch.cuda
import torch.distributed
import torch.utils.data
import torch.optim
class MultiLayerPerceptron(torch.nn.Module):
"""
A simple MLP that can either be used independently or put on top
of pretrained models (such as BERT) and act as a classifier.
Args:
hidden_size (i... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Oreoluwa1234/NeMo | MultiLayerPerceptron | false | 9,715 | [
"Apache-2.0"
] | 0 | b01e3ceed34efe31fd43866685dbdd19a6b30928 | https://github.com/Oreoluwa1234/NeMo/tree/b01e3ceed34efe31fd43866685dbdd19a6b30928 |
MLP | import torch
import torch.nn as nn
from collections import OrderedDict
class MLP(nn.Module):
def __init__(self, input_dims, n_hiddens, n_class):
super(MLP, self).__init__()
assert isinstance(input_dims, int), 'Please provide int for input_dims'
self.input_dims = input_dims
current... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 co... | ZhiTingXin/pytorch-playground | MLP | false | 9,716 | [
"MIT"
] | 0 | b319eaf290ad6d793e41efc488309cedf24eba96 | https://github.com/ZhiTingXin/pytorch-playground/tree/b319eaf290ad6d793e41efc488309cedf24eba96 |
MultiHeadAttention | import math
import torch
import torch.cuda
from torch import nn
import torch.distributed
import torch.utils.data
import torch.optim
class MultiHeadAttention(nn.Module):
"""
Multi-head scaled dot-product attention layer.
Args:
hidden_size: size of the embeddings in the model, also known as d_model... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Oreoluwa1234/NeMo | MultiHeadAttention | false | 9,717 | [
"Apache-2.0"
] | 0 | b01e3ceed34efe31fd43866685dbdd19a6b30928 | https://github.com/Oreoluwa1234/NeMo/tree/b01e3ceed34efe31fd43866685dbdd19a6b30928 |
ConvElu | import torch
from torch import nn
import torch.cuda
import torch.backends.cudnn
import torch.backends.mkl
import torch.backends.cuda
import torch.backends.quantized
class ConvElu(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, image_size,
inplace=False):
super(ConvElu, 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
from torch import n... | XiaobingSuper/intel-extension-for-pytorch | ConvElu | false | 9,718 | [
"Apache-2.0"
] | 0 | b61029be10e46e6d2e13b0e700c81f8e59164df0 | https://github.com/XiaobingSuper/intel-extension-for-pytorch/tree/b61029be10e46e6d2e13b0e700c81f8e59164df0 |
ConvSwishInplace | import torch
from torch import nn
import torch.cuda
import torch.backends.cudnn
import torch.backends.mkl
import torch.backends.cuda
import torch.backends.quantized
class ConvSwishInplace(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, image_size):
super(ConvSwishInplace, self).__i... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.cuda
import torch.backends.cudnn
import torch.... | XiaobingSuper/intel-extension-for-pytorch | ConvSwishInplace | false | 9,719 | [
"Apache-2.0"
] | 0 | b61029be10e46e6d2e13b0e700c81f8e59164df0 | https://github.com/XiaobingSuper/intel-extension-for-pytorch/tree/b61029be10e46e6d2e13b0e700c81f8e59164df0 |
ConvSwishOutplace | import torch
from torch import nn
import torch.cuda
import torch.backends.cudnn
import torch.backends.mkl
import torch.backends.cuda
import torch.backends.quantized
class ConvSwishOutplace(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, image_size):
super(ConvSwishOutplace, 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 import nn
import torch.cuda
import torch.backends.cudnn
import torch.... | XiaobingSuper/intel-extension-for-pytorch | ConvSwishOutplace | false | 9,720 | [
"Apache-2.0"
] | 0 | b61029be10e46e6d2e13b0e700c81f8e59164df0 | https://github.com/XiaobingSuper/intel-extension-for-pytorch/tree/b61029be10e46e6d2e13b0e700c81f8e59164df0 |
ConvHardtanh | import torch
from torch import nn
import torch.cuda
import torch.backends.cudnn
import torch.backends.mkl
import torch.backends.cuda
import torch.backends.quantized
class ConvHardtanh(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, image_size,
inplace=False):
super(ConvHard... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
import t... | XiaobingSuper/intel-extension-for-pytorch | ConvHardtanh | false | 9,721 | [
"Apache-2.0"
] | 0 | b61029be10e46e6d2e13b0e700c81f8e59164df0 | https://github.com/XiaobingSuper/intel-extension-for-pytorch/tree/b61029be10e46e6d2e13b0e700c81f8e59164df0 |
MultiHeadAttn | import torch
import torch.cuda
from torch.nn import functional as F
from torch import nn
import torch.distributed
import torch.utils.data
import torch.optim
class MultiHeadAttn(nn.Module):
def __init__(self, n_head, d_model, d_head, dropout, dropatt=0.1,
pre_lnorm=False):
super(MultiHeadAttn, sel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Oreoluwa1234/NeMo | MultiHeadAttn | false | 9,722 | [
"Apache-2.0"
] | 0 | b01e3ceed34efe31fd43866685dbdd19a6b30928 | https://github.com/Oreoluwa1234/NeMo/tree/b01e3ceed34efe31fd43866685dbdd19a6b30928 |
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