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 |
|---|---|---|---|---|---|---|---|---|---|---|
BinaryReg | import torch
import torch.nn as nn
import torch.utils.data
class BinaryReg(nn.Module):
"""Regularization for encouraging the outputs to be binary.
"""
def __init__(self, alpha=1.0):
super().__init__()
self.alpha = alpha
def forward(self, input):
diff = input - 0.5
dif... | 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
... | pragyasingh7/pytorch_connectomics | BinaryReg | false | 4,134 | [
"MIT"
] | 0 | fdc8e1900b0a38d19ea50f78f8c81da2a4f015a9 | https://github.com/pragyasingh7/pytorch_connectomics/tree/fdc8e1900b0a38d19ea50f78f8c81da2a4f015a9 |
DepthWiseSeparableConvBlock | import torch
import torch.nn as nn
class DepthWiseSeparableConvBlock(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, bias=True, padding_mode='zeros',
inner_kernel_size=1, inner_stride=1, inner_padding=0):
"""Depthwise separable 2D Co... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | pppyykknen/LFDisplay-PyTorch | DepthWiseSeparableConvBlock | false | 4,135 | [
"MIT"
] | 0 | d19261dac1717a799bb5ba5f96563be1d2383340 | https://github.com/pppyykknen/LFDisplay-PyTorch/tree/d19261dac1717a799bb5ba5f96563be1d2383340 |
MDN | from torch.nn import Module
import torch
from torch.nn.modules import Module
from torch.nn.modules import Linear
class MDN(Module):
def __init__(self, input_size, num_mixtures):
super(MDN, self).__init__()
self.input_size = input_size
self.num_mixtures = num_mixtures
self.paramete... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | poctaviano/Handwriting-Model | MDN | false | 4,136 | [
"MIT"
] | 0 | 30311ea0f4cb6e7bc0114cf0b2a96dc915dd9795 | https://github.com/poctaviano/Handwriting-Model/tree/30311ea0f4cb6e7bc0114cf0b2a96dc915dd9795 |
KARAttention | from _paritybench_helpers import _mock_config
import math
import torch
from torch import nn
class KARMultiHeadAttention(nn.Module):
def __init__(self, config, hidden_size):
super(KARMultiHeadAttention, self).__init__()
if hidden_size % config.num_attention_heads != 0:
raise ValueError... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | ohadrozen/inferbert | KARAttention | false | 4,137 | [
"Apache-2.0"
] | 0 | 2e450aba894937e5769dcf028e4a8a597991fe43 | https://github.com/ohadrozen/inferbert/tree/2e450aba894937e5769dcf028e4a8a597991fe43 |
AttentiveTrans2d | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class AttentiveTrans2d(nn.Module):
def __init__(self, num_features, hidden_channels=32):
super(AttentiveTrans2d, self).__init__()
self.avgpool = nn.AdaptiveAvgPool2d(1)
self.smooth_gamma = 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.triton_helpers import libdevice
import torch.nn as ... | ppomelo/Attentive-Transformation-Based-Normalization | AttentiveTrans2d | false | 4,138 | [
"Apache-2.0"
] | 0 | 62ad02eb025613e90f4fe0e0a9f0f85839e53092 | https://github.com/ppomelo/Attentive-Transformation-Based-Normalization/tree/62ad02eb025613e90f4fe0e0a9f0f85839e53092 |
DepthLogLoss | import torch
import torch.nn as nn
class DepthLogLoss(nn.Module):
def __init__(self, balance_factor):
super(DepthLogLoss, self).__init__()
self.balance_factor = balance_factor
def forward(self, inputs, targets):
n, _, h, w = inputs.shape
n_pixel = n * h * w
inputs = t... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | pystokes/depth_estimation | DepthLogLoss | false | 4,140 | [
"MIT"
] | 0 | b5b1955bcb5b3f1a1f1c8ddde45431cf38514f90 | https://github.com/pystokes/depth_estimation/tree/b5b1955bcb5b3f1a1f1c8ddde45431cf38514f90 |
ConditionalBottleNeck | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class FiLM(nn.Module):
""" Feature-wise Linear Modulation (FiLM) layer"""
def __init__(self, input_size, output_size, num_film_layers=1,
layer_norm=False):
"""
:param input_size: feature size of x_cond
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Daupler/CA-MTL | ConditionalBottleNeck | false | 4,141 | [
"MIT"
] | 0 | d417b039dee973e32f42ba5c1c346738cd29ab3c | https://github.com/Daupler/CA-MTL/tree/d417b039dee973e32f42ba5c1c346738cd29ab3c |
TextureFinder | import torch
import torch.nn as nn
import torch.nn.functional as F
class TextureFinder(nn.Module):
def __init__(self):
super(TextureFinder, self).__init__()
self.encoder_conv1 = nn.Conv2d(in_channels=1, out_channels=4,
kernel_size=4, stride=2, padding=1, dilation=1, groups=1, bias=Tru... | 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... | paucarre/staal | TextureFinder | false | 4,142 | [
"MIT"
] | 0 | 1635e514f0ed978a08c078afd258980bcb6f0cec | https://github.com/paucarre/staal/tree/1635e514f0ed978a08c078afd258980bcb6f0cec |
C3D | import torch
import torch.nn as nn
import torch.nn
class C3D(nn.Module):
"""
The C3D network as described in [1].
"""
def __init__(self):
super(C3D, self).__init__()
self.conv1 = nn.Conv3d(3, 64, kernel_size=(3, 3, 3), padding=(1, 1, 1))
self.pool1 = nn.MaxPool3d(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.... | kar98kbang/c3d-pytorch | C3D | false | 4,143 | [
"MIT"
] | 0 | 22b3564798cb9249ad6fdb6c9d929bff3fdfa567 | https://github.com/kar98kbang/c3d-pytorch/tree/22b3564798cb9249ad6fdb6c9d929bff3fdfa567 |
Model | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
"""conv. autoencoder"""
def __init__(self):
"""constructor"""
super().__init__()
self.conv1 = nn.Conv2d(3, 32, 5, padding=2)
self.conv2 = nn.Conv2d(32, 64, 3, padding=1)
self.con... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | positivevaib/semi-supervised-imagenet-classification | Model | false | 4,144 | [
"MIT"
] | 0 | 4fb6427f5a72951c1b866a1ddbc2599811bb5770 | https://github.com/positivevaib/semi-supervised-imagenet-classification/tree/4fb6427f5a72951c1b866a1ddbc2599811bb5770 |
ActorCritic | import torch
import torch.nn.functional as F
import torch.nn as nn
def swish(x):
return x * F.sigmoid(x)
class ActorCritic(nn.Module):
"""Actor (Policy) Model."""
def __init__(self, state_size, action_size, seed, fc1_units=64,
fc2_units=64):
"""Initialize parameters and build 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.triton_helpers import libdevice, math as tl_math
im... | postBG/deep-reinforcement-learning | ActorCritic | false | 4,145 | [
"MIT"
] | 0 | 5df5662b091c4c3f00beba1aa6f9ce8a52001c93 | https://github.com/postBG/deep-reinforcement-learning/tree/5df5662b091c4c3f00beba1aa6f9ce8a52001c93 |
ODEfunc | import torch
import torch.nn as nn
def norm(dim):
"""
Group normalization to improve model accuracy and training speed.
"""
return nn.GroupNorm(min(1, dim), dim)
class ConcatConv1d(nn.Module):
"""
1d convolution concatenated with time for usage in ODENet.
"""
def __init__(self, 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.... | puneat/SS-using-NODE | ODEfunc | false | 4,146 | [
"MIT"
] | 0 | 29f053769420a2d1cab1ad45f59a912c2ac737da | https://github.com/puneat/SS-using-NODE/tree/29f053769420a2d1cab1ad45f59a912c2ac737da |
ConcatConv1d | import torch
import torch.nn as nn
class ConcatConv1d(nn.Module):
"""
1d convolution concatenated with time for usage in ODENet.
"""
def __init__(self, dim_in, dim_out, kernel_size=3, stride=1, padding=0,
bias=True, transpose=False):
super(ConcatConv1d, self).__init__()
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | puneat/SS-using-NODE | ConcatConv1d | false | 4,147 | [
"MIT"
] | 0 | 29f053769420a2d1cab1ad45f59a912c2ac737da | https://github.com/puneat/SS-using-NODE/tree/29f053769420a2d1cab1ad45f59a912c2ac737da |
AdversarialNetwork | import torch
import torch.nn as nn
class AdversarialNetwork(nn.Module):
def __init__(self, in_feature):
super(AdversarialNetwork, self).__init__()
self.ad_layer1 = nn.Linear(in_feature, 32)
self.ad_layer2 = nn.Linear(32, 32)
self.ad_layer3 = nn.Linear(32, 1)
self.ad_layer1... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | pwjworks/MS-MDA | AdversarialNetwork | false | 4,148 | [
"MIT"
] | 0 | 21f921a933a318820239541adb26b9fc6feba699 | https://github.com/pwjworks/MS-MDA/tree/21f921a933a318820239541adb26b9fc6feba699 |
CollaborativeAttention | import math
import torch
import torch.utils.data
from enum import Enum
import torch.nn as nn
class MixingMatrixInit(Enum):
CONCATENATE = 1
ALL_ONES = 2
UNIFORM = 3
class CollaborativeAttention(nn.Module):
def __init__(self, dim_input: 'int', dim_value_all: 'int',
dim_key_query_all: 'int', n... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | prattcmp/NonAttentiveTacotron2 | CollaborativeAttention | false | 4,149 | [
"BSD-3-Clause"
] | 0 | c65722133c392fba233b5003b480ee498fc0a44a | https://github.com/prattcmp/NonAttentiveTacotron2/tree/c65722133c392fba233b5003b480ee498fc0a44a |
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_... | pystokes/depth_estimation | UpSample | false | 4,150 | [
"MIT"
] | 0 | b5b1955bcb5b3f1a1f1c8ddde45431cf38514f90 | https://github.com/pystokes/depth_estimation/tree/b5b1955bcb5b3f1a1f1c8ddde45431cf38514f90 |
SelfExpression | import torch
import torch.nn as nn
class SelfExpression(nn.Module):
def __init__(self, n):
super(SelfExpression, self).__init__()
self.Coefficient = nn.Parameter(0.0001 * torch.ones(n, n, dtype=
torch.float32), requires_grad=True)
def forward(self, x):
y = torch.matmul(se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | qilinli/DSC-Net | SelfExpression | false | 4,151 | [
"MIT"
] | 0 | c0e7a3cae3e07c34b2989234f568c7007cf0fc55 | https://github.com/qilinli/DSC-Net/tree/c0e7a3cae3e07c34b2989234f568c7007cf0fc55 |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 16, 3, stride=3)
self.conv2 = nn.Conv2d(16, 32, 3, stride=3)
self.conv3 = nn.Conv2d(32, 64, 3, stride=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
assert_... | prasad5141/cat_vs_dog_webapp | Net | false | 4,152 | [
"MIT"
] | 0 | 29c82addbc62104c3b9250af5f465b269cf68039 | https://github.com/prasad5141/cat_vs_dog_webapp/tree/29c82addbc62104c3b9250af5f465b269cf68039 |
LearnedPositionalEmbedding | import torch
import torch.nn as nn
import torch.nn.functional as F
class LearnedPositionalEmbedding(nn.Embedding):
"""
This module learns positional embeddings up to a fixed maximum size.
Padding ids are ignored by either offsetting based on padding_idx
or by setting padding_idx to None and ensuring 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... | qinwang-ai/Contact-Distil | LearnedPositionalEmbedding | false | 4,153 | [
"Apache-2.0"
] | 0 | 5e98389de70e0d9c4d16bd91ca1326689dc220a6 | https://github.com/qinwang-ai/Contact-Distil/tree/5e98389de70e0d9c4d16bd91ca1326689dc220a6 |
ConvAE | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class Conv2dSamePad(nn.Module):
"""
Implement Tensorflow's 'SAME' padding mode in Conv2d.
When an odd number, say `m`, of pixels are need to pad, Tensorflow will pad one more column at right or one more
row at bottom. But 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
import math
import torch.nn a... | qilinli/DSC-Net | ConvAE | false | 4,154 | [
"MIT"
] | 0 | c0e7a3cae3e07c34b2989234f568c7007cf0fc55 | https://github.com/qilinli/DSC-Net/tree/c0e7a3cae3e07c34b2989234f568c7007cf0fc55 |
MultiHeadedAttention | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class MultiHeadedAttention(nn.Module):
def __init__(self, num_head, d_model, dropout=0.1):
super(MultiHeadedAttention, self).__init__()
assert d_model % num_head == 0
self.d_k = d_model // num_head
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.... | qi700/my_point_summarize | MultiHeadedAttention | false | 4,155 | [
"Apache-2.0"
] | 0 | e269c2d0411fc61ea34055c3080472bc9111bcaa | https://github.com/qi700/my_point_summarize/tree/e269c2d0411fc61ea34055c3080472bc9111bcaa |
Attention | import torch
import torch.utils.data
from torch import nn
import torch.nn.functional as F
import torch.hub
class Attention(nn.Module):
def forward(self, query, key, value, mask=None, dropout=None):
scale = query.size(-1) ** -0.5
scores = query.matmul(key.transpose(-2, -1)) / scale
if mask... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | opqi/VMZ | Attention | false | 4,156 | [
"Apache-2.0"
] | 0 | bc9c3bf5f7d9e7d0ef433f9d9b4a3155ac5ed969 | https://github.com/opqi/VMZ/tree/bc9c3bf5f7d9e7d0ef433f9d9b4a3155ac5ed969 |
MultiHeadAttention | import torch
import torch.nn as nn
class MultiHeadAttention(nn.Module):
def __init__(self, hidden_state, num_heads=1):
super().__init__()
self.q_linear = nn.Linear(hidden_state, hidden_state)
self.v_linear = nn.Linear(hidden_state, hidden_state)
self.k_linear = nn.Linear(hidden_st... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | qinyiwei/MuTual | MultiHeadAttention | false | 4,157 | [
"MIT"
] | 0 | 3bdd13c1388d6136b8944666dfd434870760cc93 | https://github.com/qinyiwei/MuTual/tree/3bdd13c1388d6136b8944666dfd434870760cc93 |
_SubPixelBlock | import torch
import torch.nn as nn
class _SubPixelBlock(nn.Module):
def __init__(self, in_channels: 'int'=64, out_channels: 'int'=64,
scale_factor: 'int'=2):
super(_SubPixelBlock, self).__init__()
n_out = out_channels * scale_factor ** 2
self.conv = nn.Conv2d(in_channels, n_out, k... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | pvrancx/torch_isr | _SubPixelBlock | false | 4,158 | [
"MIT"
] | 0 | 831278ae5c3b939b4147bae1a99bc3f3d4fc415d | https://github.com/pvrancx/torch_isr/tree/831278ae5c3b939b4147bae1a99bc3f3d4fc415d |
LocalContextNorm | import math
import torch
import torch.utils.data
from torchvision.transforms import functional as F
from torch import nn
from torch.nn import functional as F
class LocalContextNorm(nn.Module):
def __init__(self, num_features, channels_per_group=2, window_size=(227,
227), eps=1e-05):
super(LocalCo... | 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.utils.data
from torch import nn
assert_size_stride = torch._C._dyn... | pjh4993/FCOS | LocalContextNorm | false | 4,159 | [
"BSD-2-Clause"
] | 0 | 27f79e3fd3f5043796450b9a2201b42c744fd3df | https://github.com/pjh4993/FCOS/tree/27f79e3fd3f5043796450b9a2201b42c744fd3df |
NeuralNet | import torch
class NeuralNet(torch.nn.Module):
def __init__(self, input_features, hidden_layer_size, output_classes):
super(NeuralNet, self).__init__()
self.l1 = torch.nn.Linear(input_features, hidden_layer_size)
self.l2 = torch.nn.Linear(hidden_layer_size, output_classes)
def forwar... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cu... | rahimftd/digit_recognizer | NeuralNet | false | 4,160 | [
"MIT"
] | 0 | a134efa915670308ad7a77c8ace2662e5c775913 | https://github.com/rahimftd/digit_recognizer/tree/a134efa915670308ad7a77c8ace2662e5c775913 |
FCNet | import torch
import torch.nn.functional
from torch import nn
from torch.nn.utils import weight_norm
class FCNet(nn.Module):
def __init__(self, in_size, out_size, activate=None, drop=0.0):
super(FCNet, self).__init__()
self.lin = weight_norm(nn.Linear(in_size, out_size), dim=None)
self.dro... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn.fun... | rafiberlin/clp-sose21-pm-vision | FCNet | false | 4,161 | [
"MIT"
] | 0 | 55c786182ed4568cdeda4bb3676fa02b9580d68d | https://github.com/rafiberlin/clp-sose21-pm-vision/tree/55c786182ed4568cdeda4bb3676fa02b9580d68d |
SharpenedCosineSimilarity | import torch
import torch.nn as nn
import torch.nn.functional as F
def unfold2d(x, kernel_size: 'int', stride: 'int', padding: 'int'):
x = F.pad(x, [padding] * 4)
bs, in_c, h, w = x.size()
ks = kernel_size
strided_x = x.as_strided((bs, in_c, (h - ks) // stride + 1, (w - ks) //
stride + 1, ks, ... | 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
import torch.nn.functional as F
assert_s... | quickgrid/sharpened_cosine_similarity_torch | SharpenedCosineSimilarity | false | 4,162 | [
"MIT"
] | 0 | d652d76a4994a0b3817e248d5899827d35a5ebeb | https://github.com/quickgrid/sharpened_cosine_similarity_torch/tree/d652d76a4994a0b3817e248d5899827d35a5ebeb |
EncoderLayer | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class AffineLayer(nn.Module):
def __init__(self, dropout, d_model, d_ff):
super(AffineLayer, self).__init__()
self.w_1 = nn.Linear(d_model, d_ff)
self.w_2 = nn.Linear(d_ff, d_model)
self.dropout = nn.Dr... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | qi700/my_point_summarize | EncoderLayer | false | 4,163 | [
"Apache-2.0"
] | 0 | e269c2d0411fc61ea34055c3080472bc9111bcaa | https://github.com/qi700/my_point_summarize/tree/e269c2d0411fc61ea34055c3080472bc9111bcaa |
DSCNet | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class Conv2dSamePad(nn.Module):
"""
Implement Tensorflow's 'SAME' padding mode in Conv2d.
When an odd number, say `m`, of pixels are need to pad, Tensorflow will pad one more column at right or one more
row at bottom. But 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
import math
import torch.nn a... | qilinli/DSC-Net | DSCNet | false | 4,164 | [
"MIT"
] | 0 | c0e7a3cae3e07c34b2989234f568c7007cf0fc55 | https://github.com/qilinli/DSC-Net/tree/c0e7a3cae3e07c34b2989234f568c7007cf0fc55 |
FuseLayer | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class FuseLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.linear1 = nn.Linear(4 * config.hidden_size, config.hidden_size)
self.linear2 = nn.Linear(4 * config.hidden_size, config.hidden_s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | qinyiwei/MuTual | FuseLayer | false | 4,165 | [
"MIT"
] | 0 | 3bdd13c1388d6136b8944666dfd434870760cc93 | https://github.com/qinyiwei/MuTual/tree/3bdd13c1388d6136b8944666dfd434870760cc93 |
AbsModule | import torch
class AbsModule(torch.nn.Module):
def __init__(self):
super(AbsModule, self).__init__()
def forward(self, x):
return torch.abs(x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_str... | mirecta/nncase | AbsModule | false | 4,166 | [
"Apache-2.0"
] | 0 | d2efa59677a26f4259b3b6a5b6ec05ea16d4e40c | https://github.com/mirecta/nncase/tree/d2efa59677a26f4259b3b6a5b6ec05ea16d4e40c |
Tanh | import math
import torch
class Tanh(torch.nn.Tanh):
"""
Class that extends ``torch.nn.Tanh`` additionally computing the log diagonal
blocks of the Jacobian.
"""
def forward(self, inputs, grad: 'torch.Tensor'=None):
"""
Parameters
----------
inputs : ``torch.Tensor`... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
assert_size_stride = torch._C._dynamo.guards.assert_size_strid... | ralphc1212/BNAF | Tanh | false | 4,167 | [
"MIT"
] | 0 | b6e331aa96cdd4496b6eed6c6ce65512a99f4149 | https://github.com/ralphc1212/BNAF/tree/b6e331aa96cdd4496b6eed6c6ce65512a99f4149 |
MHA | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
class MHA(nn.Module):
def __init__(self, config):
super().__init__()
self.num_attention_heads = config.num_attention_heads
self.hidden_size = config.hidden_size
self.attention_head_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.... | qinyiwei/MuTual | MHA | false | 4,168 | [
"MIT"
] | 0 | 3bdd13c1388d6136b8944666dfd434870760cc93 | https://github.com/qinyiwei/MuTual/tree/3bdd13c1388d6136b8944666dfd434870760cc93 |
CosModule | import torch
class CosModule(torch.nn.Module):
def __init__(self):
super(CosModule, self).__init__()
def forward(self, x):
return torch.cos(x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_str... | mirecta/nncase | CosModule | false | 4,169 | [
"Apache-2.0"
] | 0 | d2efa59677a26f4259b3b6a5b6ec05ea16d4e40c | https://github.com/mirecta/nncase/tree/d2efa59677a26f4259b3b6a5b6ec05ea16d4e40c |
PopArt | import torch
import numpy as np
import torch.nn as nn
class PopArt(nn.Module):
"""Normalize a vector of observations - across the first norm_axes dimensions"""
def __init__(self, input_shape, norm_axes=1, beta=0.99999,
per_element_update=False, epsilon=1e-05, device=torch.device('cpu')):
supe... | 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 numpy as np
import to... | rainwangphy/TRPO-in-MARL | PopArt | false | 4,170 | [
"MIT"
] | 0 | 22229abba417708922ecf6455c1c5180dbe80391 | https://github.com/rainwangphy/TRPO-in-MARL/tree/22229abba417708922ecf6455c1c5180dbe80391 |
RegressionHead | import abc
import torch
import torch.nn as nn
from torch.nn.functional import *
import torch.utils.data.dataset
class BaseHead(nn.Module, metaclass=abc.ABCMeta):
"""Absract class for task heads"""
@abc.abstractmethod
def __init__(self):
super().__init__()
class RegressionHead(BaseHead):
de... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 abc
import t... | mfk3138/jiant | RegressionHead | false | 4,171 | [
"MIT"
] | 0 | 6e67ff1ecb1bb98533c1019a86af4ad2c04c6a64 | https://github.com/mfk3138/jiant/tree/6e67ff1ecb1bb98533c1019a86af4ad2c04c6a64 |
CeilModule | import torch
class CeilModule(torch.nn.Module):
def __init__(self):
super(CeilModule, self).__init__()
def forward(self, x):
return torch.ceil(x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | mirecta/nncase | CeilModule | false | 4,172 | [
"Apache-2.0"
] | 0 | d2efa59677a26f4259b3b6a5b6ec05ea16d4e40c | https://github.com/mirecta/nncase/tree/d2efa59677a26f4259b3b6a5b6ec05ea16d4e40c |
AttFlowLayer | import torch
import torch.nn as nn
import torch.nn.functional as F
class AttFlowLayer(nn.Module):
def __init__(self, embed_length):
super(AttFlowLayer, self).__init__()
self.embed_length = embed_length
self.alpha = nn.Linear(3 * embed_length, 1, bias=False)
def forward(self, context,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | qtxcm/Joint_NER_with_NTP | AttFlowLayer | false | 4,173 | [
"Apache-2.0"
] | 0 | 02f26f2cc891d36808b2e28f337cc4846524e5df | https://github.com/qtxcm/Joint_NER_with_NTP/tree/02f26f2cc891d36808b2e28f337cc4846524e5df |
SqrtModule | import torch
class SqrtModule(torch.nn.Module):
def __init__(self):
super(SqrtModule, self).__init__()
def forward(self, x):
return torch.sqrt(x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | mirecta/nncase | SqrtModule | false | 4,174 | [
"Apache-2.0"
] | 0 | d2efa59677a26f4259b3b6a5b6ec05ea16d4e40c | https://github.com/mirecta/nncase/tree/d2efa59677a26f4259b3b6a5b6ec05ea16d4e40c |
ReduceMaxModule | import torch
class ReduceMaxModule(torch.nn.Module):
def __init__(self):
super(ReduceMaxModule, self).__init__()
def forward(self, x):
return torch.max(x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | mirecta/nncase | ReduceMaxModule | false | 4,175 | [
"Apache-2.0"
] | 0 | d2efa59677a26f4259b3b6a5b6ec05ea16d4e40c | https://github.com/mirecta/nncase/tree/d2efa59677a26f4259b3b6a5b6ec05ea16d4e40c |
NegModule | import torch
class NegModule(torch.nn.Module):
def __init__(self):
super(NegModule, self).__init__()
def forward(self, x):
return -x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | mirecta/nncase | NegModule | false | 4,176 | [
"Apache-2.0"
] | 0 | d2efa59677a26f4259b3b6a5b6ec05ea16d4e40c | https://github.com/mirecta/nncase/tree/d2efa59677a26f4259b3b6a5b6ec05ea16d4e40c |
FloorModule | import torch
class FloorModule(torch.nn.Module):
def __init__(self):
super(FloorModule, self).__init__()
def forward(self, x):
return torch.floor(x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | mirecta/nncase | FloorModule | false | 4,177 | [
"Apache-2.0"
] | 0 | d2efa59677a26f4259b3b6a5b6ec05ea16d4e40c | https://github.com/mirecta/nncase/tree/d2efa59677a26f4259b3b6a5b6ec05ea16d4e40c |
ReduceMeanModule | import torch
class ReduceMeanModule(torch.nn.Module):
def __init__(self):
super(ReduceMeanModule, self).__init__()
def forward(self, x):
return torch.mean(x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | mirecta/nncase | ReduceMeanModule | false | 4,178 | [
"Apache-2.0"
] | 0 | d2efa59677a26f4259b3b6a5b6ec05ea16d4e40c | https://github.com/mirecta/nncase/tree/d2efa59677a26f4259b3b6a5b6ec05ea16d4e40c |
ReduceMinModule | import torch
class ReduceMinModule(torch.nn.Module):
def __init__(self):
super(ReduceMinModule, self).__init__()
def forward(self, x):
return torch.min(x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | mirecta/nncase | ReduceMinModule | false | 4,179 | [
"Apache-2.0"
] | 0 | d2efa59677a26f4259b3b6a5b6ec05ea16d4e40c | https://github.com/mirecta/nncase/tree/d2efa59677a26f4259b3b6a5b6ec05ea16d4e40c |
DenseSAGEConv | import math
import torch
import torch.nn.functional as F
from torch.nn import Parameter
import torch.utils.data
def uniform(size, tensor):
bound = 1.0 / math.sqrt(size)
if tensor is not None:
tensor.data.uniform_(-bound, bound)
class DenseSAGEConv(torch.nn.Module):
"""See :class:`torch_geometric... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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
from torch.nn imp... | rbshi/pytorch_geometric | DenseSAGEConv | false | 4,180 | [
"MIT"
] | 0 | fcfbad49219974689eb5c6e32365939ae09ace84 | https://github.com/rbshi/pytorch_geometric/tree/fcfbad49219974689eb5c6e32365939ae09ace84 |
ResizeModule | import torch
class ResizeModule(torch.nn.Module):
def __init__(self):
super(ResizeModule, self).__init__()
def forward(self, x):
return torch.nn.functional.interpolate(x, size=(3, 4))
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... | mirecta/nncase | ResizeModule | false | 4,181 | [
"Apache-2.0"
] | 0 | d2efa59677a26f4259b3b6a5b6ec05ea16d4e40c | https://github.com/mirecta/nncase/tree/d2efa59677a26f4259b3b6a5b6ec05ea16d4e40c |
RMSELoss | import torch
from torch import nn
import torch.cuda
class RMSELoss(nn.Module):
def __init__(self, eps=1e-06):
super().__init__()
self.mse = nn.MSELoss()
self.eps = eps
def forward(self, yhat, y):
loss = torch.sqrt(self.mse(yhat, y) + self.eps)
return loss
def get_in... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
import ... | rgbayrak/multi-task-physio | RMSELoss | false | 4,182 | [
"MIT"
] | 0 | 01ea98f26cc9b96ec94105d5213cb1ef93673c2c | https://github.com/rgbayrak/multi-task-physio/tree/01ea98f26cc9b96ec94105d5213cb1ef93673c2c |
_ASPPModule | import torch
import torch.nn as nn
class _ASPPModule(nn.Module):
"""Atrous Spatial Pyramid Pooling"""
def __init__(self, in_channels, out_channels, pyramids):
super(_ASPPModule, self).__init__()
self.stages = nn.Module()
for i, (dilation, padding) in enumerate(zip(pyramids, pyramids))... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | reyuwei/deeplab-pytorch | _ASPPModule | false | 4,183 | [
"MIT"
] | 0 | f4e241c83be5f85f0f2e1be5d76160b8c2d7ec9a | https://github.com/reyuwei/deeplab-pytorch/tree/f4e241c83be5f85f0f2e1be5d76160b8c2d7ec9a |
Net | import torch
import torch.nn as nn
class Net(nn.Module):
def __init__(self, input_size):
super(Net, self).__init__()
hlayer1 = int(input_size * 10)
hlayer2 = int(input_size * 10 / 2)
self.fc1 = nn.Linear(input_size, hlayer1)
self.relu1 = nn.ReLU()
self.fc2 = nn.Lin... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | rcaborges/music-cold-start | Net | false | 4,184 | [
"Apache-2.0"
] | 0 | a2b321e8b5ef7b894b5e0659c5da2f9ae3df25d8 | https://github.com/rcaborges/music-cold-start/tree/a2b321e8b5ef7b894b5e0659c5da2f9ae3df25d8 |
L2Loss | import torch
import torch.nn as nn
import torch.utils.data
class L2Loss(nn.Module):
"""
Compute the l2 distance
"""
def __init__(self):
super(L2Loss, self).__init__()
def forward(self, h_pred, h_target):
return torch.norm(h_target - h_pred, p=2)
def get_inputs():
return [torch.... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import... | riokt/video-paragraph | L2Loss | false | 4,185 | [
"MIT"
] | 0 | 2da3298819e73809af495457db2cf1dfffad712f | https://github.com/riokt/video-paragraph/tree/2da3298819e73809af495457db2cf1dfffad712f |
SNNBlock | from torch.nn import Module
import math
import torch
from torch.nn import SELU
from torch.nn import AlphaDropout
from torch.nn import Identity
from torch.nn import Parameter
from torch.nn.functional import conv2d
class SNNBlock(Module):
"""Block for a self-normalizing fully-connected layer.
This block consis... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 impor... | rharish101/CIL-Project | SNNBlock | false | 4,186 | [
"MIT"
] | 0 | fed1be8b22bb4228329b719a301f74459a7bf13b | https://github.com/rharish101/CIL-Project/tree/fed1be8b22bb4228329b719a301f74459a7bf13b |
FinalPool | import torch
import torch.utils.data
class FinalPool(torch.nn.Module):
def __init__(self):
super(FinalPool, self).__init__()
def forward(self, input):
"""
input : Tensor of shape (batch size, T, Cin)
Outputs a Tensor of shape (batch size, Cin).
"""
re... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
e... | praesc/end-to-end-SLU | FinalPool | false | 4,187 | [
"Apache-2.0"
] | 0 | c4e8a5be0ea6a8d93ea7cfd3a5bdab0560c50848 | https://github.com/praesc/end-to-end-SLU/tree/c4e8a5be0ea6a8d93ea7cfd3a5bdab0560c50848 |
CAE_ENC | import torch
import torch.nn as nn
import torch.nn.functional as F
class CAE_ENC(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(3, 32, kernel_size=5, padding=2, stride=2)
self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1, stride=2)
self.conv3 = 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_... | positivevaib/semi-supervised-imagenet-classification | CAE_ENC | false | 4,188 | [
"MIT"
] | 0 | 4fb6427f5a72951c1b866a1ddbc2599811bb5770 | https://github.com/positivevaib/semi-supervised-imagenet-classification/tree/4fb6427f5a72951c1b866a1ddbc2599811bb5770 |
PSA_p | import torch
import torch.nn as nn
import torch._utils
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
def kaiming_init(module, a=0, mode='fan_out', nonlinearity='relu', bias=0,
distribution='normal'):
assert distribution in ['uniform', 'normal']
if ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | realphongha/human-pose-estimation.pytorch | PSA_p | false | 4,189 | [
"MIT"
] | 0 | 29b106d3e6c6e12325a7d4bca4abc56ecbc12b1f | https://github.com/realphongha/human-pose-estimation.pytorch/tree/29b106d3e6c6e12325a7d4bca4abc56ecbc12b1f |
ContrastiveLoss | from torch.nn import Module
import torch
from torch.nn import LogSoftmax
from torch.nn.functional import cosine_similarity
class ContrastiveLoss(Module):
"""A contrastive loss adapted from SimCLR.
Link to SimCLR: https://arxiv.org/abs/2002.05709v3.
"""
def __init__(self, temperature: 'float'=1.0):
... | 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 libdevice, math as tl_math
from torch.... | rharish101/CIL-Project | ContrastiveLoss | false | 4,190 | [
"MIT"
] | 0 | fed1be8b22bb4228329b719a301f74459a7bf13b | https://github.com/rharish101/CIL-Project/tree/fed1be8b22bb4228329b719a301f74459a7bf13b |
FilterNorm | import torch
import torch.nn as nn
from torch.nn.init import calculate_gain
import torch.nn.parallel
class FilterNorm(nn.Module):
def __init__(self, in_channels, kernel_size, filter_type, nonlinearity=
'linear', running_std=False, running_mean=False):
assert filter_type in ('spatial', 'channel')
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
from torch.nn.init import calculate_gain
import torch.nn.... | rightchose/ddfnet | FilterNorm | false | 4,191 | [
"MIT"
] | 0 | 44a2f63933c1784a53f26a10c1157a164d044485 | https://github.com/rightchose/ddfnet/tree/44a2f63933c1784a53f26a10c1157a164d044485 |
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.... | ricklentz/deep-reinforcement-learning | Actor | false | 4,192 | [
"MIT"
] | 0 | 4a034a955c64a630e0fd72f4380d81e2c25a4c68 | https://github.com/ricklentz/deep-reinforcement-learning/tree/4a034a955c64a630e0fd72f4380d81e2c25a4c68 |
TransformerLayer | import math
import torch
import uuid
from torch import Tensor
from typing import Tuple
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional
from typing import Dict
from torch.nn import Parameter
def gelu(x):
"""Implementation of the gelu activation function.
For information: Open... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | qinwang-ai/Contact-Distil | TransformerLayer | false | 4,193 | [
"Apache-2.0"
] | 0 | 5e98389de70e0d9c4d16bd91ca1326689dc220a6 | https://github.com/qinwang-ai/Contact-Distil/tree/5e98389de70e0d9c4d16bd91ca1326689dc220a6 |
MLP | import torch
import torch as th
import torch.nn as nn
class MLP(nn.Module):
def __init__(self, input_size, output_size, hidden=128):
super(MLP, self).__init__()
self.linear1 = nn.Linear(input_size, hidden, bias=False)
self.linear2 = nn.Linear(hidden, output_size, bias=False)
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._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | ngoby/cherry | MLP | false | 4,194 | [
"Apache-2.0"
] | 0 | ec88bac03bf3ac3fae1010c5db8329db595dc5d6 | https://github.com/ngoby/cherry/tree/ec88bac03bf3ac3fae1010c5db8329db595dc5d6 |
EncoderLayer | import math
import torch
import torch.nn.functional as F
import torch.nn as nn
def attention(q, k, v, d_k, mask=None, dropout=None):
scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(d_k)
if mask is not None:
mask = mask.unsqueeze(1)
scores = scores.masked_fill(mask == 0, -1000000000.0... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | rcasero/Transformer | EncoderLayer | false | 4,195 | [
"Apache-2.0"
] | 0 | 82f51e04f80634d56b134e0ac87f67d6ba8c736a | https://github.com/rcasero/Transformer/tree/82f51e04f80634d56b134e0ac87f67d6ba8c736a |
ResidualBlock | import torch
import torch.nn as nn
import torch.nn.functional as F
class ResidualBlock(nn.Module):
"""
Vanilla convolutional residual block from seminal paper by He et al.
Use of instance normalization suggested by Ulyanov et al. in
https://arxiv.org/pdf/1607.08022.pdf%C2%A0%C2%A0%C2%A0%C2%A0.
""... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | rileypsmith/Fast-Style-Transfer | ResidualBlock | false | 4,196 | [
"MIT"
] | 0 | 8b2164f8bc6d63530f914610b6c5c5c1b0f4ffd5 | https://github.com/rileypsmith/Fast-Style-Transfer/tree/8b2164f8bc6d63530f914610b6c5c5c1b0f4ffd5 |
RegL1 | import torch
import torch.nn as nn
class RegL1(nn.Module):
"""
Run Regression with L1
"""
def __init__(self, n_input, n_output):
super(RegL1, self).__init__()
self.linear = nn.Linear(n_input, n_output, bias=True)
def forward(self, x, training=True):
self.training = traini... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | rmporsch/ML_genetic_risk | RegL1 | false | 4,197 | [
"MIT"
] | 0 | 4e1a0510c94260e69f93639ff4104c5f85080d9f | https://github.com/rmporsch/ML_genetic_risk/tree/4e1a0510c94260e69f93639ff4104c5f85080d9f |
DecoderRNN | import torch
from torch import nn
import torch.nn.functional as F
class DecoderRNN(nn.Module):
def __init__(self, T, d):
super().__init__()
self.T = T
self.d = d
self.W = nn.Linear(d, d)
self.U = nn.Linear(d, d)
self.V = nn.Linear(d, d)
self.b = nn.Paramete... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | rish-16/SHA-RNN | DecoderRNN | false | 4,198 | [
"MIT"
] | 0 | 08c701396217f0b645de043963ff8ec4bf27e835 | https://github.com/rish-16/SHA-RNN/tree/08c701396217f0b645de043963ff8ec4bf27e835 |
SpatialAttention | import torch
import torch.utils.data
import torch
import torch.nn as nn
class SpatialAttention(nn.Module):
def __init__(self):
super(SpatialAttention, self).__init__()
self.conv1 = nn.Conv2d(in_channels=2, out_channels=1, kernel_size=3,
padding=1, bias=False)
self.sigmoid = 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.utils.data
impor... | robvincen/robot_gradet | SpatialAttention | false | 4,199 | [
"BSD-3-Clause"
] | 0 | a39e3c772c72806dfc99e4d24d8787e0d1bdeef5 | https://github.com/robvincen/robot_gradet/tree/a39e3c772c72806dfc99e4d24d8787e0d1bdeef5 |
QNet | import torch
import torch.nn as nn
class QNet(nn.Module):
def __init__(self, in_size: 'int', out_size: 'int'):
super(QNet, self).__init__()
self.fc1 = nn.Linear(in_size, 16)
self.fc_out = nn.Linear(16, out_size)
self.act = nn.LeakyReLU()
def forward(self, x):
o1 = sel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | rosebin/gymlab | QNet | false | 4,200 | [
"BSD-3-Clause"
] | 0 | de97fc24e0ddf5e328a2aa732cc339b2371d92d1 | https://github.com/rosebin/gymlab/tree/de97fc24e0ddf5e328a2aa732cc339b2371d92d1 |
L0Linear | import torch
import numpy as np
import torch.nn as nn
from torch.nn import functional as F
from torch.autograd import Variable
import logging as lg
def hard_sigmoid(x):
"""Hard Sigmoid function."""
return torch.min(torch.max(x, torch.zeros_like(x)), torch.ones_like(x))
class _L0Norm(nn.Module):
"""L0 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
import numpy as np
import tor... | rmporsch/ML_genetic_risk | L0Linear | false | 4,201 | [
"MIT"
] | 0 | 4e1a0510c94260e69f93639ff4104c5f85080d9f | https://github.com/rmporsch/ML_genetic_risk/tree/4e1a0510c94260e69f93639ff4104c5f85080d9f |
QNetwork | import torch
import torch.nn.functional as F
import torch.nn as nn
class QNetwork(nn.Module):
def __init__(self, state_size, action_size, seed):
super(QNetwork, self).__init__()
self.seed = torch.manual_seed(seed)
self.fc1 = nn.Linear(state_size, 128)
self.fc2 = nn.Linear(128, 64)... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | royveshovda/deep-reinforcement-learning | QNetwork | false | 4,202 | [
"MIT"
] | 0 | 64ba7ef5ab44f095b7e8b29f6c4ff1585025981a | https://github.com/royveshovda/deep-reinforcement-learning/tree/64ba7ef5ab44f095b7e8b29f6c4ff1585025981a |
Discriminator | import torch
import torch.nn as nn
class Discriminator(nn.Module):
def __init__(self, state_dim, action_dim):
super(Discriminator, self).__init__()
self.l1 = nn.Linear(state_dim + action_dim, 500)
self.l2 = nn.Linear(500, 300)
self.l3 = nn.Linear(300, 300)
self.l4 = nn.Lin... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | rortiz9/meleeml | Discriminator | false | 4,203 | [
"MIT"
] | 0 | 9be4bf53a377dfb46dbb3b51f102f1bffc0124d2 | https://github.com/rortiz9/meleeml/tree/9be4bf53a377dfb46dbb3b51f102f1bffc0124d2 |
RelationalTransformerEncoderLayer | import torch
import warnings
from torch import Tensor
from torch.nn import TransformerEncoderLayer
from torch.nn.functional import *
from torch.nn.modules.activation import MultiheadAttention
from torch.nn.modules.activation import xavier_uniform_
from torch.nn.modules.activation import xavier_normal_
from torch.nn.mod... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | mfk3138/jiant | RelationalTransformerEncoderLayer | false | 4,204 | [
"MIT"
] | 0 | 6e67ff1ecb1bb98533c1019a86af4ad2c04c6a64 | https://github.com/mfk3138/jiant/tree/6e67ff1ecb1bb98533c1019a86af4ad2c04c6a64 |
LxmertAttentionOutput | from _paritybench_helpers import _mock_config
import torch
from torch import nn
class LxmertAttentionOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=1e-12... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | rsgit95/med_kg_txt_multimodal | LxmertAttentionOutput | false | 4,205 | [
"Apache-2.0"
] | 0 | 80355b0cf58e0571531ad6f9728c533110ca996d | https://github.com/rsgit95/med_kg_txt_multimodal/tree/80355b0cf58e0571531ad6f9728c533110ca996d |
Block | import torch
import torch as th
from torch import nn
def drop_path(x, drop_prob: 'float'=0.0, training: 'bool'=False):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
th... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | q5628077/Transformer-in-RL | Block | false | 4,206 | [
"MIT"
] | 0 | 14679656779a372d91d9fbd89bd802b5ff34c200 | https://github.com/q5628077/Transformer-in-RL/tree/14679656779a372d91d9fbd89bd802b5ff34c200 |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self, state_dim, action_dim):
super(Net, self).__init__()
fc1_dim = 32
fc2_dim = 64
fc3_dim = 128
self.fc1 = nn.Linear(state_dim, fc1_dim)
self.fc2 = nn.Linear(fc1... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | ronekko/study_reinforcement_learning | Net | false | 4,207 | [
"MIT"
] | 0 | ef5201e3eae69c20f29b7f176b5a6de7ecdb856a | https://github.com/ronekko/study_reinforcement_learning/tree/ef5201e3eae69c20f29b7f176b5a6de7ecdb856a |
IReLU | import math
import torch
class IReLU(torch.nn.Module):
__constants__ = ['negative_slope', 'positive_slope']
negative_slope: 'float'
positive_slope: 'float'
def __init__(self, negative_slope=math.tan(math.pi / 8), positive_slope
=math.tan(3 * math.pi / 8)):
super(IReLU, self).__init__(... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided... | rupumped/DFL | IReLU | false | 4,208 | [
"BSD-3-Clause"
] | 0 | a4e4d96b7ce7522cf7fee3c2cfdbb54eb7a473f2 | https://github.com/rupumped/DFL/tree/a4e4d96b7ce7522cf7fee3c2cfdbb54eb7a473f2 |
Affine | import torch
from torch import nn
class Affine(nn.Module):
def __init__(self, channel):
super().__init__()
self.g = nn.Parameter(torch.ones(1, 1, channel))
self.b = nn.Parameter(torch.zeros(1, 1, channel))
def forward(self, x):
return x * self.g + self.b
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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | rushirajsherlocked/External-Attention-pytorch | Affine | false | 4,209 | [
"MIT"
] | 0 | 7d6814b2d90909adf81c62f3f8a89e30a59d6481 | https://github.com/rushirajsherlocked/External-Attention-pytorch/tree/7d6814b2d90909adf81c62f3f8a89e30a59d6481 |
ECAAttention | import torch
from torch import nn
from torch.nn import init
class ECAAttention(nn.Module):
def __init__(self, kernel_size=3):
super().__init__()
self.gap = nn.AdaptiveAvgPool2d(1)
self.conv = nn.Conv1d(1, 1, kernel_size=kernel_size, padding=(
kernel_size - 1) // 2)
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 import nn
from torch.nn import init
assert_size_stride = torch._C._dy... | rushirajsherlocked/External-Attention-pytorch | ECAAttention | false | 4,210 | [
"MIT"
] | 0 | 7d6814b2d90909adf81c62f3f8a89e30a59d6481 | https://github.com/rushirajsherlocked/External-Attention-pytorch/tree/7d6814b2d90909adf81c62f3f8a89e30a59d6481 |
GTXAttentionOutput | from _paritybench_helpers import _mock_config
import torch
from torch import nn
class GTXAttentionOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=1e-12)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | rsgit95/med_kg_txt_multimodal | GTXAttentionOutput | false | 4,211 | [
"Apache-2.0"
] | 0 | 80355b0cf58e0571531ad6f9728c533110ca996d | https://github.com/rsgit95/med_kg_txt_multimodal/tree/80355b0cf58e0571531ad6f9728c533110ca996d |
Actor | import torch
import torch.nn as nn
import torch.nn.functional as F
class Actor(nn.Module):
def __init__(self, state_dim, action_dim):
super(Actor, self).__init__()
self.l1 = nn.Linear(state_dim, 400)
self.l2 = nn.Linear(400, 200)
self.l3 = nn.Linear(200, action_dim)
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.... | rortiz9/meleeml | Actor | false | 4,212 | [
"MIT"
] | 0 | 9be4bf53a377dfb46dbb3b51f102f1bffc0124d2 | https://github.com/rortiz9/meleeml/tree/9be4bf53a377dfb46dbb3b51f102f1bffc0124d2 |
PolicyNetwork | import torch
import torch.nn as nn
from torch.nn import functional as F
from torch.distributions import Normal
class PolicyNetwork(nn.Module):
def __init__(self, state_dim, action_dim, hidden_dim, init_w=0.003,
log_std_min=-20, log_std_max=2):
super(PolicyNetwork, self).__init__()
self.lo... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | rtharungowda/Soft-Actor-Critic-Pytorch | PolicyNetwork | false | 4,213 | [
"MIT"
] | 0 | 0d2c20c6cfd4e578e0b7cff4525ddf0bc956812f | https://github.com/rtharungowda/Soft-Actor-Critic-Pytorch/tree/0d2c20c6cfd4e578e0b7cff4525ddf0bc956812f |
Depth_Pointwise_Conv1d | import torch
from torch import nn
class Depth_Pointwise_Conv1d(nn.Module):
def __init__(self, in_ch, out_ch, k):
super().__init__()
if k == 1:
self.depth_conv = nn.Identity()
else:
self.depth_conv = nn.Conv1d(in_channels=in_ch, out_channels=
in_ch, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | rushirajsherlocked/External-Attention-pytorch | Depth_Pointwise_Conv1d | false | 4,214 | [
"MIT"
] | 0 | 7d6814b2d90909adf81c62f3f8a89e30a59d6481 | https://github.com/rushirajsherlocked/External-Attention-pytorch/tree/7d6814b2d90909adf81c62f3f8a89e30a59d6481 |
ExternalAttention | import torch
from torch import nn
from torch.nn import init
class ExternalAttention(nn.Module):
def __init__(self, d_model, S=64):
super().__init__()
self.mk = nn.Linear(d_model, S, bias=False)
self.mv = nn.Linear(S, d_model, bias=False)
self.softmax = nn.Softmax(dim=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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | rushirajsherlocked/External-Attention-pytorch | ExternalAttention | false | 4,215 | [
"MIT"
] | 0 | 7d6814b2d90909adf81c62f3f8a89e30a59d6481 | https://github.com/rushirajsherlocked/External-Attention-pytorch/tree/7d6814b2d90909adf81c62f3f8a89e30a59d6481 |
SpatialAttention | import torch
from torch import nn
class SpatialAttention(nn.Module):
def __init__(self, kernel_size=7):
super().__init__()
self.conv = nn.Conv2d(2, 1, kernel_size=kernel_size, padding=
kernel_size // 2)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
max_result,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | rushirajsherlocked/External-Attention-pytorch | SpatialAttention | false | 4,216 | [
"MIT"
] | 0 | 7d6814b2d90909adf81c62f3f8a89e30a59d6481 | https://github.com/rushirajsherlocked/External-Attention-pytorch/tree/7d6814b2d90909adf81c62f3f8a89e30a59d6481 |
GTXSelfAttentionLayer | from _paritybench_helpers import _mock_config
import math
import torch
from torch import nn
class GTXAttention(nn.Module):
def __init__(self, config, ctx_dim=None):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
'The hidde... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | rsgit95/med_kg_txt_multimodal | GTXSelfAttentionLayer | false | 4,217 | [
"Apache-2.0"
] | 0 | 80355b0cf58e0571531ad6f9728c533110ca996d | https://github.com/rsgit95/med_kg_txt_multimodal/tree/80355b0cf58e0571531ad6f9728c533110ca996d |
MlpBlock | import torch
from torch import nn
class MlpBlock(nn.Module):
def __init__(self, input_dim, mlp_dim=512):
super().__init__()
self.fc1 = nn.Linear(input_dim, mlp_dim)
self.gelu = nn.GELU()
self.fc2 = nn.Linear(mlp_dim, input_dim)
def forward(self, x):
return self.fc2(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.triton_helpers import libdevice
from torch import n... | rushirajsherlocked/External-Attention-pytorch | MlpBlock | false | 4,218 | [
"MIT"
] | 0 | 7d6814b2d90909adf81c62f3f8a89e30a59d6481 | https://github.com/rushirajsherlocked/External-Attention-pytorch/tree/7d6814b2d90909adf81c62f3f8a89e30a59d6481 |
LxmertCrossAttentionLayer | from _paritybench_helpers import _mock_config
import math
import torch
from torch import nn
class LxmertAttention(nn.Module):
def __init__(self, config, ctx_dim=None):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
'The hi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | rsgit95/med_kg_txt_multimodal | LxmertCrossAttentionLayer | false | 4,219 | [
"Apache-2.0"
] | 0 | 80355b0cf58e0571531ad6f9728c533110ca996d | https://github.com/rsgit95/med_kg_txt_multimodal/tree/80355b0cf58e0571531ad6f9728c533110ca996d |
GTXCrossAttentionLayer | from _paritybench_helpers import _mock_config
import math
import torch
from torch import nn
class GTXAttention(nn.Module):
def __init__(self, config, ctx_dim=None):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
'The hidde... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | rsgit95/med_kg_txt_multimodal | GTXCrossAttentionLayer | false | 4,220 | [
"Apache-2.0"
] | 0 | 80355b0cf58e0571531ad6f9728c533110ca996d | https://github.com/rsgit95/med_kg_txt_multimodal/tree/80355b0cf58e0571531ad6f9728c533110ca996d |
ConvEncoder | import torch
from torch import nn
class ConvEncoder(nn.Module):
""" Simple convolutional encoder network.
It consists of 5 convolutional layers, each downsampling the input by a
factor of 2, and a final fully-connected layer projecting the output to
c_dim dimensions.
Args:
c_dim (int): o... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | planetceres/differentiable_volumetric_rendering | ConvEncoder | false | 4,221 | [
"MIT"
] | 0 | f2fe46d139244c7642439ced23656db1e7f5c128 | https://github.com/planetceres/differentiable_volumetric_rendering/tree/f2fe46d139244c7642439ced23656db1e7f5c128 |
DoubleAttention | import torch
from torch import nn
from torch.nn import functional as F
from torch.nn import init
class DoubleAttention(nn.Module):
def __init__(self, in_channels, c_m, c_n, reconstruct=True):
super().__init__()
self.in_channels = in_channels
self.reconstruct = reconstruct
self.c_m... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | rushirajsherlocked/External-Attention-pytorch | DoubleAttention | false | 4,222 | [
"MIT"
] | 0 | 7d6814b2d90909adf81c62f3f8a89e30a59d6481 | https://github.com/rushirajsherlocked/External-Attention-pytorch/tree/7d6814b2d90909adf81c62f3f8a89e30a59d6481 |
LxmertSelfAttentionLayer | from _paritybench_helpers import _mock_config
import math
import torch
from torch import nn
class LxmertAttention(nn.Module):
def __init__(self, config, ctx_dim=None):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
'The hi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | rsgit95/med_kg_txt_multimodal | LxmertSelfAttentionLayer | false | 4,223 | [
"Apache-2.0"
] | 0 | 80355b0cf58e0571531ad6f9728c533110ca996d | https://github.com/rsgit95/med_kg_txt_multimodal/tree/80355b0cf58e0571531ad6f9728c533110ca996d |
SimplifiedScaledDotProductAttention | import torch
import numpy as np
from torch import nn
from torch.nn import init
class SimplifiedScaledDotProductAttention(nn.Module):
"""
Scaled dot-product attention
"""
def __init__(self, d_model, h, dropout=0.1):
"""
:param d_model: Output dimensionality of the model
: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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | rushirajsherlocked/External-Attention-pytorch | SimplifiedScaledDotProductAttention | false | 4,224 | [
"MIT"
] | 0 | 7d6814b2d90909adf81c62f3f8a89e30a59d6481 | https://github.com/rushirajsherlocked/External-Attention-pytorch/tree/7d6814b2d90909adf81c62f3f8a89e30a59d6481 |
SpatialGroupEnhance | import torch
from torch import nn
from torch.nn import init
class SpatialGroupEnhance(nn.Module):
def __init__(self, groups):
super().__init__()
self.groups = groups
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.weight = nn.Parameter(torch.zeros(1, groups, 1, 1))
self.bias ... | 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
from torch.nn import init
assert_size_stride = torch._C._d... | rushirajsherlocked/External-Attention-pytorch | SpatialGroupEnhance | false | 4,225 | [
"MIT"
] | 0 | 7d6814b2d90909adf81c62f3f8a89e30a59d6481 | https://github.com/rushirajsherlocked/External-Attention-pytorch/tree/7d6814b2d90909adf81c62f3f8a89e30a59d6481 |
ScaledDotProductAttention | import torch
import numpy as np
from torch import nn
from torch.nn import init
class ScaledDotProductAttention(nn.Module):
"""
Scaled dot-product attention
"""
def __init__(self, d_model, d_k, d_v, h, dropout=0.1):
"""
:param d_model: Output dimensionality of the model
: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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | rushirajsherlocked/External-Attention-pytorch | ScaledDotProductAttention | false | 4,226 | [
"MIT"
] | 0 | 7d6814b2d90909adf81c62f3f8a89e30a59d6481 | https://github.com/rushirajsherlocked/External-Attention-pytorch/tree/7d6814b2d90909adf81c62f3f8a89e30a59d6481 |
AttentionHead | import torch
from torch import Tensor
import torch.nn as nn
import torch.nn.functional as F
from torch.functional import Tensor
def scaled_dot_product_attention(query: 'torch.Tensor', key: 'torch.Tensor',
value: 'torch.Tensor') ->Tensor:
temp = query.bmm(key.transpose(1, 2))
scale = query.size(-1) ** 0.5
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | sabernn/vit-pytorch | AttentionHead | false | 4,227 | [
"MIT"
] | 0 | 21a2671aa92adb941a56ae629f6089f550949fb2 | https://github.com/sabernn/vit-pytorch/tree/21a2671aa92adb941a56ae629f6089f550949fb2 |
SE_Connect | import torch
import torch.nn.functional as F
import torch.nn
import torch.nn as nn
class SE_Connect(nn.Module):
def __init__(self, channels, s=4):
super().__init__()
assert channels % s == 0, '{} % {} != 0'.format(channesl, s)
self.linear1 = nn.Linear(channels, channels // s)
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
import torch.... | qlindazm/asv-subtools | SE_Connect | false | 4,228 | [
"Apache-2.0"
] | 0 | fe1d31db9f3268622016babe944201f6ff81ed56 | https://github.com/qlindazm/asv-subtools/tree/fe1d31db9f3268622016babe944201f6ff81ed56 |
AttentiveStatsPool | import torch
import torch.nn
import torch.nn as nn
class AttentiveStatsPool(nn.Module):
def __init__(self, in_dim, bottleneck_dim):
super().__init__()
self.linear1 = nn.Conv1d(in_dim, bottleneck_dim, kernel_size=1)
self.linear2 = nn.Conv1d(bottleneck_dim, in_dim, kernel_size=1)
def 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.... | qlindazm/asv-subtools | AttentiveStatsPool | false | 4,229 | [
"Apache-2.0"
] | 0 | fe1d31db9f3268622016babe944201f6ff81ed56 | https://github.com/qlindazm/asv-subtools/tree/fe1d31db9f3268622016babe944201f6ff81ed56 |
OutlookAttention | import math
import torch
from torch import nn
from torch.nn import functional as F
class OutlookAttention(nn.Module):
def __init__(self, dim, num_heads=1, kernel_size=3, padding=1, stride=1,
qkv_bias=False, attn_drop=0.1):
super().__init__()
self.dim = dim
self.num_heads = num_hea... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | rushirajsherlocked/External-Attention-pytorch | OutlookAttention | false | 4,230 | [
"MIT"
] | 0 | 7d6814b2d90909adf81c62f3f8a89e30a59d6481 | https://github.com/rushirajsherlocked/External-Attention-pytorch/tree/7d6814b2d90909adf81c62f3f8a89e30a59d6481 |
Critic | import torch
import torch.nn as nn
class Critic(nn.Module):
def __init__(self, obs_dim: 'int'):
super().__init__()
self.fc1 = nn.Linear(obs_dim, 64)
self.fc2 = nn.Linear(64, 64)
self.fc3 = nn.Linear(64, 1)
def forward(self, x):
x = torch.tanh(self.fc1(x))
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.triton_helpers import libdevice
import torch.nn as ... | raznem/rlex | Critic | false | 4,231 | [
"MIT"
] | 0 | d24b964d80067becc81d86f6ce87e5be413b7049 | https://github.com/raznem/rlex/tree/d24b964d80067becc81d86f6ce87e5be413b7049 |
TdnnAffine | import torch
import torch.nn.functional as F
import torch.nn
def to_device(device_object, tensor):
"""
Select device for non-parameters tensor w.r.t model or tensor which has been specified a device.
"""
if isinstance(device_object, torch.nn.Module):
next(device_object.parameters()).device
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
... | qlindazm/asv-subtools | TdnnAffine | false | 4,232 | [
"Apache-2.0"
] | 0 | fe1d31db9f3268622016babe944201f6ff81ed56 | https://github.com/qlindazm/asv-subtools/tree/fe1d31db9f3268622016babe944201f6ff81ed56 |
ChannelAttentionModule | import torch
import numpy as np
from torch import nn
from torch.nn import init
class SimplifiedScaledDotProductAttention(nn.Module):
"""
Scaled dot-product attention
"""
def __init__(self, d_model, h, dropout=0.1):
"""
:param d_model: Output dimensionality of the model
: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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | rushirajsherlocked/External-Attention-pytorch | ChannelAttentionModule | false | 4,233 | [
"MIT"
] | 0 | 7d6814b2d90909adf81c62f3f8a89e30a59d6481 | https://github.com/rushirajsherlocked/External-Attention-pytorch/tree/7d6814b2d90909adf81c62f3f8a89e30a59d6481 |
LDEPooling | import torch
import torch.nn
class LDEPooling(torch.nn.Module):
"""A novel learnable dictionary encoding layer.
Reference: Weicheng Cai, etc., "A NOVEL LEARNABLE DICTIONARY ENCODING LAYER FOR END-TO-END
LANGUAGE IDENTIFICATION", icassp, 2018
"""
def __init__(self, input_dim, c_num=64,... | 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
assert... | qlindazm/asv-subtools | LDEPooling | false | 4,234 | [
"Apache-2.0"
] | 0 | fe1d31db9f3268622016babe944201f6ff81ed56 | https://github.com/qlindazm/asv-subtools/tree/fe1d31db9f3268622016babe944201f6ff81ed56 |
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