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 |
|---|---|---|---|---|---|---|---|---|---|---|
ScaledDotProduct | import math
import torch
from torch import nn
class ScaledDotProduct(nn.Module):
def __init__(self, attentionHeadSize, dropOutProb=0.1):
super(ScaledDotProduct, self).__init__()
self.attentionHeadSize = attentionHeadSize
self.dropout = nn.Dropout(dropOutProb)
def forward(self, Q, K, ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | simonepreite/QABERT | ScaledDotProduct | false | 4,336 | [
"MIT"
] | 0 | ed3e49f6619f3ff660068291231909693cb8f5d5 | https://github.com/simonepreite/QABERT/tree/ed3e49f6619f3ff660068291231909693cb8f5d5 |
FeedForward | import math
import torch
from torch import nn
class GELU(nn.Module):
def __init__(self):
super(GELU, self).__init__()
def forward(self, tensor):
geluPow = tensor + 0.044715 * torch.pow(tensor, 3)
geluTanh = torch.tanh(math.sqrt(2 / math.pi) * geluPow)
geluResult = 1 + geluTan... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
from to... | simonepreite/QABERT | FeedForward | false | 4,337 | [
"MIT"
] | 0 | ed3e49f6619f3ff660068291231909693cb8f5d5 | https://github.com/simonepreite/QABERT/tree/ed3e49f6619f3ff660068291231909693cb8f5d5 |
RenormSoftmax | import torch
import numpy as np
import torch.nn as nn
class RenormSoftmax(nn.Module):
def __init__(self, dim=-1, norm=np.pi / 40):
super().__init__()
self.softmax = nn.Softmax(dim=dim)
self.dim = dim
self.norm = norm
def forward(self, x):
N = x.shape[self.dim]
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import numpy as np
imp... | simonverret/deep_continuation | RenormSoftmax | false | 4,338 | [
"MIT"
] | 0 | 986bfba7f6806dc4869a023ff1fc1d0d18324b25 | https://github.com/simonverret/deep_continuation/tree/986bfba7f6806dc4869a023ff1fc1d0d18324b25 |
BertAttention | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
import torch.utils.data
class BertLayerNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-05):
"""Construct a layernorm module in the TF style (epsilon inside the square root)."""
super(BertLayerNorm... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | shubham-gupta-iitr/mmmlX | BertAttention | false | 4,339 | [
"Apache-2.0"
] | 0 | 3485e6191e0e45bf1c8168e4e928a36ab9264d22 | https://github.com/shubham-gupta-iitr/mmmlX/tree/3485e6191e0e45bf1c8168e4e928a36ab9264d22 |
MLP | import torch
from torch import Tensor
from torch import nn
class GELU(nn.Module):
"""Quick GELU"""
def forward(self, x: 'Tensor') ->Tensor:
return x * torch.sigmoid(1.702 * x)
class MLP(nn.Module):
def __init__(self, c1, ch, c2=None):
super().__init__()
self.c_fc = nn.Linear(c1... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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 Tensor
from torch import nn
assert_size_stride = torch._C._dyn... | sithu31296/multimodal | MLP | false | 4,340 | [
"MIT"
] | 0 | 78f57956cc84273579eb9e2e2be2a58fa1f38814 | https://github.com/sithu31296/multimodal/tree/78f57956cc84273579eb9e2e2be2a58fa1f38814 |
RefModel2d | import torch
import torch.nn.functional as F
class RefModel2d(torch.nn.Module):
"""The 2D reference model."""
def __init__(self):
super().__init__()
self.l1 = torch.nn.Conv2d(2, 2, 3, stride=2, bias=False, padding=1,
padding_mode='reflect')
self.l2 = torch.nn.BatchNorm2d(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.... | shuohan/pytorch-layers | RefModel2d | false | 4,341 | [
"MIT"
] | 0 | 020846fd02d501cf477552179c19ba4b5e9a0695 | https://github.com/shuohan/pytorch-layers/tree/020846fd02d501cf477552179c19ba4b5e9a0695 |
TripletLoss | import torch
from torch import Tensor
from torch import nn
from torch.nn import functional as F
def euclidean_dist(x: 'Tensor', y: 'Tensor') ->Tensor:
xx, yy = torch.meshgrid((x ** 2).sum(1), (y ** 2).sum(1))
return xx + yy - 2 * (x @ y.t())
class TripletLoss(nn.Module):
"""
Modified from Tong Xiao'... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 Tensor
from... | sithu31296/re_identification | TripletLoss | false | 4,342 | [
"MIT"
] | 0 | 28c2cf32c6c8c9d79330e1419a7156fe10d8ac95 | https://github.com/sithu31296/re_identification/tree/28c2cf32c6c8c9d79330e1419a7156fe10d8ac95 |
RefModel2d2 | import torch
import torch.nn.functional as F
class RefModel2d2(torch.nn.Module):
"""The 2D reference model."""
def __init__(self):
super().__init__()
self.l1 = torch.nn.Conv2d(2, 2, 3, padding=1, stride=2,
padding_mode='circular', bias=False)
self.l2 = torch.nn.Identity()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | shuohan/pytorch-layers | RefModel2d2 | false | 4,343 | [
"MIT"
] | 0 | 020846fd02d501cf477552179c19ba4b5e9a0695 | https://github.com/shuohan/pytorch-layers/tree/020846fd02d501cf477552179c19ba4b5e9a0695 |
PositionAttentionModule | 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 | PositionAttentionModule | false | 4,344 | [
"MIT"
] | 0 | 7d6814b2d90909adf81c62f3f8a89e30a59d6481 | https://github.com/rushirajsherlocked/External-Attention-pytorch/tree/7d6814b2d90909adf81c62f3f8a89e30a59d6481 |
Actor | import torch
import torch.nn.functional as F
import torch.nn as nn
class Actor(nn.Module):
def __init__(self, state_dim, action_dim, max_action, nhid):
super(Actor, self).__init__()
self.l1 = nn.Linear(state_dim, nhid)
self.l2 = nn.Linear(nhid, nhid)
self.l3 = nn.Linear(nhid, acti... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | simondlevy/pytorch-drl | Actor | false | 4,345 | [
"MIT"
] | 0 | b197bb93c2cc698971f98095d4e0180811c52042 | https://github.com/simondlevy/pytorch-drl/tree/b197bb93c2cc698971f98095d4e0180811c52042 |
DeepContinuor | import torch
import torch.nn as nn
import torch.nn.functional as F
class DeepContinuor(nn.Module):
def __init__(self, x_dim, h_dim, y_dim):
super().__init__()
self.layer1 = nn.Linear(x_dim, h_dim)
self.layer2 = nn.Linear(h_dim, h_dim)
self.layer3 = nn.Linear(h_dim, h_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
import torch.nn as nn
assert_... | simonverret/deep_continuation | DeepContinuor | false | 4,346 | [
"MIT"
] | 0 | 986bfba7f6806dc4869a023ff1fc1d0d18324b25 | https://github.com/simonverret/deep_continuation/tree/986bfba7f6806dc4869a023ff1fc1d0d18324b25 |
Normalizer | import torch
import torch.nn as nn
class Normalizer(nn.Module):
def __init__(self, dim=-1, norm=1.0):
super().__init__()
self.dim = dim
self.norm = norm
self.softplus = nn.Softplus()
def forward(self, x):
out = self.softplus(x)
return out / torch.abs(out.detac... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.gu... | simonverret/deep_continuation | Normalizer | false | 4,347 | [
"MIT"
] | 0 | 986bfba7f6806dc4869a023ff1fc1d0d18324b25 | https://github.com/simonverret/deep_continuation/tree/986bfba7f6806dc4869a023ff1fc1d0d18324b25 |
BasicModel_MaxPool_ReLU | import torch
import torch.nn as nn
class BasicModel_MaxPool_ReLU(nn.Module):
def __init__(self, inplace=False) ->None:
super().__init__()
self.maxpool = nn.MaxPool1d(3)
self.relu = nn.ReLU(inplace=inplace)
def forward(self, x):
return self.relu(self.maxpool(x)).sum(dim=1)
d... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | sagnik/captum | BasicModel_MaxPool_ReLU | false | 4,348 | [
"BSD-3-Clause"
] | 0 | d6b663745ee6c01f072a4358233dec381324c283 | https://github.com/sagnik/captum/tree/d6b663745ee6c01f072a4358233dec381324c283 |
MultiHeadAttention | import math
import torch
from torch import nn
class ScaledDotProduct(nn.Module):
def __init__(self, attentionHeadSize, dropOutProb=0.1):
super(ScaledDotProduct, self).__init__()
self.attentionHeadSize = attentionHeadSize
self.dropout = nn.Dropout(dropOutProb)
def forward(self, Q, 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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | simonepreite/QABERT | MultiHeadAttention | false | 4,349 | [
"MIT"
] | 0 | ed3e49f6619f3ff660068291231909693cb8f5d5 | https://github.com/simonepreite/QABERT/tree/ed3e49f6619f3ff660068291231909693cb8f5d5 |
NormLayer | import torch
import torch.nn as nn
class NormLayer(nn.Module):
def __init__(self, mean, std, n=None, eps=1e-08) ->None:
super().__init__()
self.mean = mean
self.std = std
self.eps = eps
def forward(self, x):
return (x - self.mean) / (self.std + self.eps)
def get_inp... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | sagnik/captum | NormLayer | false | 4,350 | [
"BSD-3-Clause"
] | 0 | d6b663745ee6c01f072a4358233dec381324c283 | https://github.com/sagnik/captum/tree/d6b663745ee6c01f072a4358233dec381324c283 |
LinearMaxPoolLinearModel | import torch
import torch.nn as nn
class LinearMaxPoolLinearModel(nn.Module):
def __init__(self) ->None:
super().__init__()
self.lin1 = nn.Linear(4, 4, bias=False)
self.lin1.weight = nn.Parameter(torch.eye(4, 4))
self.pool1 = nn.MaxPool1d(4)
self.lin2 = nn.Linear(1, 1, bia... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | sagnik/captum | LinearMaxPoolLinearModel | false | 4,351 | [
"BSD-3-Clause"
] | 0 | d6b663745ee6c01f072a4358233dec381324c283 | https://github.com/sagnik/captum/tree/d6b663745ee6c01f072a4358233dec381324c283 |
BasicLinearReLULinear | import torch
import torch.nn as nn
class BasicLinearReLULinear(nn.Module):
def __init__(self, in_features, out_features=5, bias=False):
super().__init__()
self.fc1 = nn.Linear(in_features, out_features, bias=bias)
self.relu1 = nn.ReLU()
self.fc2 = nn.Linear(out_features, 1, bias=b... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | sagnik/captum | BasicLinearReLULinear | false | 4,352 | [
"BSD-3-Clause"
] | 0 | d6b663745ee6c01f072a4358233dec381324c283 | https://github.com/sagnik/captum/tree/d6b663745ee6c01f072a4358233dec381324c283 |
ConcatPositionalEncoding | import torch
import torch.nn as nn
class ConcatPositionalEncoding(nn.Module):
def __init__(self, d_model=256, max_len=512):
super().__init__()
self.timing_table = nn.Parameter(torch.FloatTensor(max_len, d_model //
2))
nn.init.normal_(self.timing_table)
self.norm = nn.L... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | skulick/self-attentive-parser | ConcatPositionalEncoding | false | 4,353 | [
"MIT"
] | 0 | 04a91e80cc05bcfe8f48145517f58e85f0c8ade6 | https://github.com/skulick/self-attentive-parser/tree/04a91e80cc05bcfe8f48145517f58e85f0c8ade6 |
PartitionedReLU | import torch
import torch.nn as nn
class PartitionedReLU(nn.ReLU):
def forward(self, x):
if isinstance(x, tuple):
x_c, x_p = x
else:
x_c, x_p = torch.chunk(x, 2, dim=-1)
return super().forward(x_c), super().forward(x_p)
def get_inputs():
return [torch.rand([4... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | skulick/self-attentive-parser | PartitionedReLU | false | 4,354 | [
"MIT"
] | 0 | 04a91e80cc05bcfe8f48145517f58e85f0c8ade6 | https://github.com/skulick/self-attentive-parser/tree/04a91e80cc05bcfe8f48145517f58e85f0c8ade6 |
LogLoss | import torch
from torch.nn import MSELoss
class LogLoss(MSELoss):
def __init__(self):
super(LogLoss, self).__init__()
self.loss = torch.nn.MSELoss()
self.loss2 = torch.nn.MSELoss()
def forward(self, input, target):
tgt = torch.atan(target)
inp = torch.atan(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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch.nn import MSELoss... | slaveuser/testRepo20181123 | LogLoss | false | 4,355 | [
"MIT"
] | 0 | 0651de19b3b7d02f8c9094b8b24346ccc2e30480 | https://github.com/slaveuser/testRepo20181123/tree/0651de19b3b7d02f8c9094b8b24346ccc2e30480 |
GlobalLayerNorm | import torch
import torch.nn as nn
from itertools import product as product
class GlobalLayerNorm(nn.Module):
def __init__(self, channel_size):
super(GlobalLayerNorm, self).__init__()
self.gamma = nn.Parameter(torch.Tensor(1, channel_size, 1))
self.beta = nn.Parameter(torch.Tensor(1, chan... | 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 itertools import product as product
assert_size_stri... | slapshin/TalkNet_ASD | GlobalLayerNorm | false | 4,356 | [
"MIT"
] | 0 | 343fac5c8d2bef2b98244e3acf20ac322711a4c7 | https://github.com/slapshin/TalkNet_ASD/tree/343fac5c8d2bef2b98244e3acf20ac322711a4c7 |
PartitionedLinear | import torch
import torch.nn as nn
class PartitionedLinear(nn.Module):
def __init__(self, in_features, out_features, bias=True):
super().__init__()
self.linear_c = nn.Linear(in_features // 2, out_features // 2, bias)
self.linear_p = nn.Linear(in_features // 2, out_features // 2, bias)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | skulick/self-attentive-parser | PartitionedLinear | false | 4,357 | [
"MIT"
] | 0 | 04a91e80cc05bcfe8f48145517f58e85f0c8ade6 | https://github.com/skulick/self-attentive-parser/tree/04a91e80cc05bcfe8f48145517f58e85f0c8ade6 |
Normalize | import torch
import torch.nn as nn
class Normalize(nn.Module):
def __init__(self):
super(Normalize, self).__init__()
def forward(self, bottom):
qn = torch.norm(bottom, p=2, dim=1).unsqueeze(dim=1) + 1e-12
top = bottom.div(qn)
return top
def get_inputs():
return [torch.r... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | slyviacassell/Multi-taks-UNITE | Normalize | false | 4,358 | [
"MIT"
] | 0 | a010a92c94c0ee0f1ffed27df6d89da58d6d34c5 | https://github.com/slyviacassell/Multi-taks-UNITE/tree/a010a92c94c0ee0f1ffed27df6d89da58d6d34c5 |
GlobalAveragePool2d | import torch
import torch.nn as nn
class GlobalAveragePool2d(nn.Module):
def __init__(self):
super(GlobalAveragePool2d, self).__init__()
def forward(self, x: 'torch.Tensor'):
assert len(x.size()) >= 2
x_size = x.size()
out = x.view(*x_size[:-2], -1)
out = out.mean(dim... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | slyviacassell/Multi-taks-UNITE | GlobalAveragePool2d | false | 4,359 | [
"MIT"
] | 0 | a010a92c94c0ee0f1ffed27df6d89da58d6d34c5 | https://github.com/slyviacassell/Multi-taks-UNITE/tree/a010a92c94c0ee0f1ffed27df6d89da58d6d34c5 |
PointwiseConvolutionLayer | import torch
class PointwiseConvolutionLayer(torch.nn.Module):
def __init__(self, N, F, F_prime):
super().__init__()
self.f1 = torch.nn.Linear(F, 128)
self.f2 = torch.nn.Linear(128, F_prime)
def forward(self, f_bar_batch):
output = torch.nn.functional.softplus(self.f1(f_bar_b... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
as... | slgao/FU-DeepLearningCourse | PointwiseConvolutionLayer | false | 4,360 | [
"MIT"
] | 0 | 2300e8bdaa2afb4c73535d5de80874f6103af6f2 | https://github.com/slgao/FU-DeepLearningCourse/tree/2300e8bdaa2afb4c73535d5de80874f6103af6f2 |
ArcFaceLinear | from torch.nn import Module
import math
import torch
import torch.distributed
import torch.nn.functional as F
class ArcFaceLinear(Module):
def __init__(self, embedding_size, num_classes):
super(ArcFaceLinear, self).__init__()
self.weight = torch.nn.Parameter(data=torch.FloatTensor(num_classes,
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | smivv/kaggle-bengali | ArcFaceLinear | false | 4,361 | [
"Apache-2.0"
] | 0 | ab6a2153b657b4f4210551f7f4a674920d66a272 | https://github.com/smivv/kaggle-bengali/tree/ab6a2153b657b4f4210551f7f4a674920d66a272 |
Encoder | import math
import torch
from torch import nn
class NormLayer(nn.Module):
"""
Implementation of Layer Normalization (https://arxiv.org/abs/1607.06450)
It consists of Batch Normalization Transform to speed up learning with mean and std computed according to the above paper
normWeights:
weights for this 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.... | simonepreite/QABERT | Encoder | false | 4,362 | [
"MIT"
] | 0 | ed3e49f6619f3ff660068291231909693cb8f5d5 | https://github.com/simonepreite/QABERT/tree/ed3e49f6619f3ff660068291231909693cb8f5d5 |
InnerProductDecoder | import torch
import torch.nn
import torch.nn.modules.loss
import torch.nn.functional as F
import torch.nn as nn
class InnerProductDecoder(nn.Module):
"""Decoder for using inner product for prediction."""
def __init__(self, dropout, act=torch.sigmoid):
super(InnerProductDecoder, self).__init__()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn
import torch.nn.modules.loss
import torch.nn as nn
assert_size_s... | spatial-Transcriptomics/DeepST | InnerProductDecoder | false | 4,363 | [
"MIT"
] | 0 | 47ce64b06b62395cd2983939d4bf2419f558a562 | https://github.com/spatial-Transcriptomics/DeepST/tree/47ce64b06b62395cd2983939d4bf2419f558a562 |
Encoder | import torch
import torch.nn.functional as F
class Encoder(torch.nn.Module):
"""Documentation for Encoder
"""
def __init__(self, input_dim, hidden_dim, latent_dim):
super(Encoder, self).__init__()
self.e1 = torch.nn.Linear(input_dim, hidden_dim)
self.e2 = torch.nn.Linear(hidden_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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cu... | slgao/FU-DeepLearningCourse | Encoder | false | 4,364 | [
"MIT"
] | 0 | 2300e8bdaa2afb4c73535d5de80874f6103af6f2 | https://github.com/slgao/FU-DeepLearningCourse/tree/2300e8bdaa2afb4c73535d5de80874f6103af6f2 |
PartitionedMultiHeadAttention | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class PartitionedMultiHeadAttention(nn.Module):
def __init__(self, d_model, n_head, d_qkv, attention_dropout=0.1,
initializer_range=0.02):
super().__init__()
self.w_qkv_c = nn.Parameter(torch.Tensor(n_head, d_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.... | skulick/self-attentive-parser | PartitionedMultiHeadAttention | false | 4,365 | [
"MIT"
] | 0 | 04a91e80cc05bcfe8f48145517f58e85f0c8ade6 | https://github.com/skulick/self-attentive-parser/tree/04a91e80cc05bcfe8f48145517f58e85f0c8ade6 |
CausalConv1d | import torch
import torch.nn as nn
class CausalConv1d(nn.Conv1d):
def __init__(self, in_channels, out_channels, kernel_size=2, dilation=1,
**kwargs):
super(CausalConv1d, self).__init__(in_channels, out_channels,
kernel_size, padding=dilation * (kernel_size - 1), dilation=
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | soumyac1999/instrumental-music-translation | CausalConv1d | false | 4,366 | [
"MIT"
] | 0 | f0d5edfdf34ef7bc9b329c426089f61d3468caa8 | https://github.com/soumyac1999/instrumental-music-translation/tree/f0d5edfdf34ef7bc9b329c426089f61d3468caa8 |
RefModel3d2 | import torch
import torch.nn.functional as F
class RefModel3d2(torch.nn.Module):
"""The 3D reference model."""
def __init__(self):
super().__init__()
self.l1 = torch.nn.Conv3d(2, 2, 3, padding=1, stride=2,
padding_mode='replicate', bias=False)
self.l2 = torch.nn.GroupNorm(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | shuohan/pytorch-layers | RefModel3d2 | false | 4,367 | [
"MIT"
] | 0 | 020846fd02d501cf477552179c19ba4b5e9a0695 | https://github.com/shuohan/pytorch-layers/tree/020846fd02d501cf477552179c19ba4b5e9a0695 |
RefModel3d | import torch
import torch.nn.functional as F
class RefModel3d(torch.nn.Module):
"""The 3D reference model."""
def __init__(self):
super().__init__()
self.l1 = torch.nn.Conv3d(2, 2, 1, bias=True)
self.l2 = torch.nn.InstanceNorm3d(2, affine=True)
self.l3 = torch.nn.ReLU()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | shuohan/pytorch-layers | RefModel3d | false | 4,368 | [
"MIT"
] | 0 | 020846fd02d501cf477552179c19ba4b5e9a0695 | https://github.com/shuohan/pytorch-layers/tree/020846fd02d501cf477552179c19ba4b5e9a0695 |
HardSwish | import torch
import torch.nn as nn
class HardSwish(nn.Module):
"""hardswish activation func (see MobileNetV3)"""
def __init__(self):
super(HardSwish, self).__init__()
def forward(self, x):
return x * nn.ReLU6(inplace=True)(x + 3.0) / 6.0
def get_inputs():
return [torch.rand([4, 4, ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | stepbuystep/LightNAS | HardSwish | false | 4,369 | [
"Apache-2.0"
] | 0 | 030d0e13e0c85354ed711e36fc4b91b1541f95e5 | https://github.com/stepbuystep/LightNAS/tree/030d0e13e0c85354ed711e36fc4b91b1541f95e5 |
DDPGActor | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
def fanin_init(size, fanin=None):
"""
Initilise network weights
"""
fanin = fanin or size[0]
v = 1.0 / np.sqrt(fanin)
return torch.Tensor(size).uniform_(-v, v)
class DDPGActor(nn.Module):
"""
Pytorc... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Nikhil-Paleti/sawyer_analysis_reinforcement_learning | DDPGActor | false | 4,370 | [
"MIT"
] | 0 | dc774c9a162fabb98493b69d7656cb14cb37f094 | https://github.com/Nikhil-Paleti/sawyer_analysis_reinforcement_learning/tree/dc774c9a162fabb98493b69d7656cb14cb37f094 |
attentionLayer | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import MultiheadAttention
from itertools import product as product
class attentionLayer(nn.Module):
def __init__(self, d_model, nhead, dropout=0.1):
super(attentionLayer, self).__init__()
self.self_attn = MultiheadAt... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | slapshin/TalkNet_ASD | attentionLayer | false | 4,371 | [
"MIT"
] | 0 | 343fac5c8d2bef2b98244e3acf20ac322711a4c7 | https://github.com/slapshin/TalkNet_ASD/tree/343fac5c8d2bef2b98244e3acf20ac322711a4c7 |
BananaResNet | import torch
import torch.nn as nn
import torch.nn.functional as F
class BananaResNet(nn.Module):
def __init__(self, state_size, action_size):
super(BananaResNet, self).__init__()
self.blk1fc1 = nn.Linear(state_size, 128)
self.blk1fc2 = nn.Linear(128, 128)
self.blk1fc3 = nn.Linear... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | slash-fury/DRL-Navigation | BananaResNet | false | 4,372 | [
"MIT"
] | 0 | 5989dca62590b611ab39ac8722a22d897c65cc88 | https://github.com/slash-fury/DRL-Navigation/tree/5989dca62590b611ab39ac8722a22d897c65cc88 |
HardSigmoid | import torch
import torch.nn as nn
class HardSigmoid(nn.Module):
"""hardsigmoid activation func used in squeeze-and-excitation module (see MobileNetV3)"""
def __init__(self):
super(HardSigmoid, self).__init__()
def forward(self, x):
return nn.ReLU6(inplace=True)(x + 3.0) / 6.0
def get_... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | stepbuystep/LightNAS | HardSigmoid | false | 4,373 | [
"Apache-2.0"
] | 0 | 030d0e13e0c85354ed711e36fc4b91b1541f95e5 | https://github.com/stepbuystep/LightNAS/tree/030d0e13e0c85354ed711e36fc4b91b1541f95e5 |
HeatmapLoss | import torch
import torch.utils.data
class HeatmapLoss(torch.nn.Module):
"""
loss for detection heatmap
"""
def __init__(self):
super(HeatmapLoss, self).__init__()
def forward(self, pred, gt):
l = (pred - gt) ** 2
l = l.mean(dim=3).mean(dim=2).mean(dim=1)
return l... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_... | seeinggreen/pyslr | HeatmapLoss | false | 4,374 | [
"BSD-3-Clause"
] | 0 | 17009582f70aed09a9174ce47f9414f715173018 | https://github.com/seeinggreen/pyslr/tree/17009582f70aed09a9174ce47f9414f715173018 |
GCN | from torch.nn import Module
import math
import torch
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
import torch.nn as nn
import torch.nn.functional as F
class GCLayer(Module):
def __init__(self, dim_in, dim_out):
super(GCLayer, self).__init__()
self.dim_in = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | spacemanidol/CS512DM | GCN | false | 4,375 | [
"MIT"
] | 0 | fa664ceb7526e27b9cccd372b65b15c587095c49 | https://github.com/spacemanidol/CS512DM/tree/fa664ceb7526e27b9cccd372b65b15c587095c49 |
DilatedResConv | import torch
import torch.nn as nn
import torch.nn.functional as F
class DilatedResConv(nn.Module):
def __init__(self, channels, dilation=1, activation='relu', padding=1,
kernel_size=3, left_pad=0):
super().__init__()
in_channels = channels
if activation == 'relu':
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
import torch.nn as nn
import ... | soumyac1999/instrumental-music-translation | DilatedResConv | false | 4,376 | [
"MIT"
] | 0 | f0d5edfdf34ef7bc9b329c426089f61d3468caa8 | https://github.com/soumyac1999/instrumental-music-translation/tree/f0d5edfdf34ef7bc9b329c426089f61d3468caa8 |
VitMlpHead | import torch
def get_args():
parser = argparse.ArgumentParser()
group = parser.add_argument_group(title='input data')
group.add_argument('--input', type=str, required=True, help=
'Path to input JSON')
group.add_argument('--json-keys', nargs='+', default=['text'], help=
'space separate ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | sourcery-ai-bot/Megatron-LM | VitMlpHead | false | 4,377 | [
"MIT"
] | 0 | f27f44e2c49d1cb39b2288bef6f7d837e11094cb | https://github.com/sourcery-ai-bot/Megatron-LM/tree/f27f44e2c49d1cb39b2288bef6f7d837e11094cb |
Attention | import torch
from torch import nn
import torch.nn.functional as F
class Attention(nn.Module):
"""
Applies an attention mechanism on the output features from the decoder.
"""
def __init__(self, dim):
super(Attention, self).__init__()
self.dim = dim
self.linear1 = nn.Linear(dim ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | salmon7ish/Video-Captioning | Attention | false | 4,378 | [
"MIT"
] | 0 | 08359b1824195a7f5eac5b58982efd19ebc6db01 | https://github.com/salmon7ish/Video-Captioning/tree/08359b1824195a7f5eac5b58982efd19ebc6db01 |
PartitionedTransformerEncoderLayer | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class FeatureDropoutFunction(torch.autograd.function.InplaceFunction):
@staticmethod
def forward(ctx, input, p=0.5, train=False, inplace=False):
if p < 0 or p > 1:
raise ValueError(
'dropout pro... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | skulick/self-attentive-parser | PartitionedTransformerEncoderLayer | false | 4,379 | [
"MIT"
] | 0 | 04a91e80cc05bcfe8f48145517f58e85f0c8ade6 | https://github.com/skulick/self-attentive-parser/tree/04a91e80cc05bcfe8f48145517f58e85f0c8ade6 |
mlp_model | import torch
import torch.nn as nn
class mlp_model(nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim):
super(mlp_model, self).__init__()
self.fc1 = nn.Linear(input_dim, hidden_dim)
self.relu1 = nn.ReLU()
self.fc2 = nn.Linear(hidden_dim, 128)
self.relu2 = 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_... | st186/complementary_label_learning | mlp_model | false | 4,380 | [
"MIT"
] | 0 | 5d22ea638e9e6c087cc5bba7797c1c201679ba12 | https://github.com/st186/complementary_label_learning/tree/5d22ea638e9e6c087cc5bba7797c1c201679ba12 |
PrimaryCaps | import torch
import torch.nn as nn
def squash(x, dim=2):
v_length_sq = x.pow(2).sum(dim=dim, keepdim=True)
v_length = torch.sqrt(v_length_sq)
scaling_factor = v_length_sq / (1 + v_length_sq) / v_length
return x * scaling_factor
class PrimaryCaps(nn.Module):
"""
PrimaryCaps layers.
"""
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | spikefairway/CapsNet-PyTorch | PrimaryCaps | false | 4,381 | [
"MIT"
] | 0 | 76aaabaad01283333a5f73a564cb1461449b4449 | https://github.com/spikefairway/CapsNet-PyTorch/tree/76aaabaad01283333a5f73a564cb1461449b4449 |
DownRightShiftedConv2d | import torch
import torch.nn as nn
class DownRightShiftedConv2d(nn.Conv2d):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.shift_pad = nn.ConstantPad2d((self.kernel_size[1] - 1, 0, self
.kernel_size[0] - 1, 0), 0.0)
def forward(self, x):
x = 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | stankevich-mipt/pixiv-tags-to-image | DownRightShiftedConv2d | false | 4,382 | [
"MIT"
] | 0 | 220a157956296c8a5b183ffe219e7c1929342c39 | https://github.com/stankevich-mipt/pixiv-tags-to-image/tree/220a157956296c8a5b183ffe219e7c1929342c39 |
OhemLoss | import torch
import torch.nn as nn
class OhemLoss(nn.Module):
def __init__(self):
super(OhemLoss, self).__init__()
self.criteria = nn.BCELoss()
def forward(self, label_p, label_t):
label_p = label_p.view(-1)
label_t = label_t.view(-1)
loss = self.criteria(label_p, lab... | 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... | suifengwangshi/MotifC | OhemLoss | false | 4,383 | [
"Apache-2.0"
] | 0 | 34117a6bfb7dacd5a84da3abd5b8a339ae73cc76 | https://github.com/suifengwangshi/MotifC/tree/34117a6bfb7dacd5a84da3abd5b8a339ae73cc76 |
EncoderBlock | import torch
import torch.nn as nn
from collections import OrderedDict
class EncoderBlock(nn.Module):
def __init__(self, n_in, n_out, n_layers):
super().__init__()
self.n_in = n_in
self.n_out = n_out
self.n_hid = self.n_out
self.n_layers = n_layers
self.post_gain =... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | stankevich-mipt/pixiv-tags-to-image | EncoderBlock | false | 4,384 | [
"MIT"
] | 0 | 220a157956296c8a5b183ffe219e7c1929342c39 | https://github.com/stankevich-mipt/pixiv-tags-to-image/tree/220a157956296c8a5b183ffe219e7c1929342c39 |
DownShiftedConv2d | import torch
import torch.nn as nn
class DownShiftedConv2d(nn.Conv2d):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.shift_pad = nn.ConstantPad2d((int((self.kernel_size[1] - 1) //
2), int((self.kernel_size[1] - 1) // 2), self.kernel_size[0] -
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | stankevich-mipt/pixiv-tags-to-image | DownShiftedConv2d | false | 4,385 | [
"MIT"
] | 0 | 220a157956296c8a5b183ffe219e7c1929342c39 | https://github.com/stankevich-mipt/pixiv-tags-to-image/tree/220a157956296c8a5b183ffe219e7c1929342c39 |
StatsPool | import torch
import warnings
import torch.nn as nn
from typing import Optional
import torch.optim
import torch.nn.functional as F
class StatsPool(nn.Module):
"""Statistics pooling
Compute temporal mean and (unbiased) standard deviation
and returns their concatenation.
Reference
---------
htt... | 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
assert_size_stride = torch._C._dynamo.... | suissemaxx/pyannote-audio-develop_colab | StatsPool | false | 4,386 | [
"MIT"
] | 0 | e9499372a1771c21e1604424a6dd041337111093 | https://github.com/suissemaxx/pyannote-audio-develop_colab/tree/e9499372a1771c21e1604424a6dd041337111093 |
ConvRelu | import torch
import torch.nn as nn
class ConvRelu(nn.Module):
def __init__(self, in_, out):
super().__init__()
self.conv = nn.Conv2d(in_, out, 3, padding=1)
self.activation = nn.LeakyReLU(inplace=True)
def forward(self, x):
x = self.conv(x)
x = self.activation(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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | sudonull1/Crack-Segmentation | ConvRelu | false | 4,387 | [
"MIT"
] | 0 | 640f86839ce5d79b48916b176caf8ad83c7355ae | https://github.com/sudonull1/Crack-Segmentation/tree/640f86839ce5d79b48916b176caf8ad83c7355ae |
fire | import torch
from itertools import product as product
import torch.nn as nn
class fire(nn.Module):
def __init__(self, inplanes, squeeze_planes, expand_planes, st=1):
super(fire, self).__init__()
self.conv1 = nn.Conv2d(inplanes, squeeze_planes, kernel_size=1,
stride=1)
self.rel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from itertools import product... | suiguoxin/Pytorch_Retinaface | fire | false | 4,388 | [
"MIT"
] | 0 | d9393bad43103635261b4ec5b03f20e79931d0da | https://github.com/suiguoxin/Pytorch_Retinaface/tree/d9393bad43103635261b4ec5b03f20e79931d0da |
Attn | import torch
from torch import nn
class Attn(torch.nn.Module):
"""
Attention:
feature_dim: dimension of feature embedding
method: method to calculate attention, (general, dot, concat)
input_dim: dimension of input embedding, default is the same as feature_dim; method dot is only availa... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | stillarrow/NRT-Lite | Attn | false | 4,389 | [
"MIT"
] | 0 | ba0f091ebfeae19325ce713e11bc426ff63402cd | https://github.com/stillarrow/NRT-Lite/tree/ba0f091ebfeae19325ce713e11bc426ff63402cd |
TransformerEncoderLayer | import torch
from torch import nn
def fill_with_neg_inf(t):
"""FP16-compatible function that fills a tensor with -inf."""
return t.float().fill_(float('-inf')).type_as(t)
def buffered_future_mask(tensor1, tensor2, device):
dim1 = dim2 = tensor1.size()
if tensor2 is not None:
dim2 = tensor2.s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | sreekanth-sreekumar/daiz-woz-nlp-project | TransformerEncoderLayer | false | 4,390 | [
"MIT"
] | 0 | 9971f752aee6a850e265f15e97a3a1ef2dacd323 | https://github.com/sreekanth-sreekumar/daiz-woz-nlp-project/tree/9971f752aee6a850e265f15e97a3a1ef2dacd323 |
IdentityPadding | import torch
import torch.nn as nn
import torch.nn.functional as F
class IdentityPadding(nn.Module):
def __init__(self, num_filters, channels_in, stride):
super(IdentityPadding, self).__init__()
self.identity = nn.MaxPool2d(1, stride=stride)
self.num_zeros = num_filters - channels_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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | sunqcc/Pytorch-HW-CIFAR10 | IdentityPadding | false | 4,391 | [
"MIT"
] | 0 | 33a55a5a832474083820b65c46f809ac98f8b109 | https://github.com/sunqcc/Pytorch-HW-CIFAR10/tree/33a55a5a832474083820b65c46f809ac98f8b109 |
SoftCrossEntropyLoss2d | import torch
import torch.nn.functional as F
from torch import nn
class SoftCrossEntropyLoss2d(nn.Module):
def forward(self, inputs, targets):
loss = 0
inputs = -F.log_softmax(inputs, dim=1)
for index in range(inputs.size()[0]):
loss += F.conv2d(inputs[range(index, index + 1)]... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | sudohainguyen/GLNet-pytorch | SoftCrossEntropyLoss2d | false | 4,392 | [
"Apache-2.0"
] | 0 | 91454831fac6e27f894d55d320dd3bcec946ac0f | https://github.com/sudohainguyen/GLNet-pytorch/tree/91454831fac6e27f894d55d320dd3bcec946ac0f |
TransformerDecoderLayer | import torch
from torch import nn
import torch.nn.functional as F
def _get_activation_fn(activation):
if activation == 'relu':
return F.relu
raise RuntimeError('activation shud be relu, not {}'.format(activation))
class TransformerDecoderLayer(nn.Module):
def __init__(self, d_model, nhead, 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.... | salmon7ish/Video-Captioning | TransformerDecoderLayer | false | 4,393 | [
"MIT"
] | 0 | 08359b1824195a7f5eac5b58982efd19ebc6db01 | https://github.com/salmon7ish/Video-Captioning/tree/08359b1824195a7f5eac5b58982efd19ebc6db01 |
AvgPoolPadding | import torch
import torch.nn as nn
import torch.nn.functional as F
class AvgPoolPadding(nn.Module):
def __init__(self, num_filters, channels_in, stride):
super(AvgPoolPadding, self).__init__()
self.identity = nn.AvgPool2d(stride, stride=stride)
self.num_zeros = num_filters - channels_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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | sunqcc/Pytorch-HW-CIFAR10 | AvgPoolPadding | false | 4,394 | [
"MIT"
] | 0 | 33a55a5a832474083820b65c46f809ac98f8b109 | https://github.com/sunqcc/Pytorch-HW-CIFAR10/tree/33a55a5a832474083820b65c46f809ac98f8b109 |
GrayScaleToRGB | import torch
import torch.utils.data
class GrayScaleToRGB(torch.nn.Module):
"""
Applies the transformation on an image to convert grayscale to rgb
"""
def __init__(self):
super().__init__()
def forward(self, sample):
return sample.repeat(3, 1, 1)
def get_inputs():
return [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.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_... | saifullah3396/doc_robustness | GrayScaleToRGB | false | 4,395 | [
"Apache-2.0"
] | 0 | 80207fb44709d4b97de826331c074784be9c75ca | https://github.com/saifullah3396/doc_robustness/tree/80207fb44709d4b97de826331c074784be9c75ca |
SineActivation | import torch
import torch.nn as nn
def t2v(tau, f, weight_linear, bias_linear, weight_periodic, bias_periodic,
arg=None):
if arg:
v1 = f(torch.matmul(tau, weight_linear) + bias_linear, arg)
else:
v1 = f(torch.matmul(tau, weight_linear) + bias_linear)
v2 = torch.matmul(tau, weight_perio... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | sungreong/PyTimeSeries | SineActivation | false | 4,396 | [
"MIT"
] | 0 | d5321c1226fc7fb6a45fec7009843894be417594 | https://github.com/sungreong/PyTimeSeries/tree/d5321c1226fc7fb6a45fec7009843894be417594 |
GraphConvolution | from torch.nn import Module
import torch
from torch import nn
import torch.autograd
from torch.nn.modules.module import Module
class GraphConvolution(Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907.
"""
def __init__(self, state_dim, name='', out_state_dim=None):
sup... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
from torch import nn
import torch.autograd
from torc... | sumanmichael/Palmira_pb | GraphConvolution | false | 4,397 | [
"MIT"
] | 0 | 8ca9f370ccd9bba694317be648ce5e4f4c55d0e7 | https://github.com/sumanmichael/Palmira_pb/tree/8ca9f370ccd9bba694317be648ce5e4f4c55d0e7 |
GraphResConvolution | from torch.nn import Module
import torch
from torch import nn
import torch.autograd
from torch.nn.modules.module import Module
class GraphConvolution(Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907.
"""
def __init__(self, state_dim, name='', out_state_dim=None):
sup... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | sumanmichael/Palmira_pb | GraphResConvolution | false | 4,398 | [
"MIT"
] | 0 | 8ca9f370ccd9bba694317be648ce5e4f4c55d0e7 | https://github.com/sumanmichael/Palmira_pb/tree/8ca9f370ccd9bba694317be648ce5e4f4c55d0e7 |
CosineActivation | import torch
import torch.nn as nn
def t2v(tau, f, weight_linear, bias_linear, weight_periodic, bias_periodic,
arg=None):
if arg:
v1 = f(torch.matmul(tau, weight_linear) + bias_linear, arg)
else:
v1 = f(torch.matmul(tau, weight_linear) + bias_linear)
v2 = torch.matmul(tau, weight_perio... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | sungreong/PyTimeSeries | CosineActivation | false | 4,399 | [
"MIT"
] | 0 | d5321c1226fc7fb6a45fec7009843894be417594 | https://github.com/sungreong/PyTimeSeries/tree/d5321c1226fc7fb6a45fec7009843894be417594 |
GlobalAvgPool2d | import torch
import torch.nn as nn
class GlobalAvgPool2d(nn.Module):
def forward(self, inputs):
return inputs.mean(-1).mean(-1)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | synxlin/mini-torchpack | GlobalAvgPool2d | false | 4,400 | [
"MIT"
] | 0 | 3ea5bca75992941e4346102d99e789a88417d7c1 | https://github.com/synxlin/mini-torchpack/tree/3ea5bca75992941e4346102d99e789a88417d7c1 |
CharbonnierLoss | import torch
import torch.utils.data
import torch.nn as nn
class CharbonnierLoss(nn.Module):
"""Charbonnier Loss (L1)"""
def __init__(self, eps=1e-06):
super(CharbonnierLoss, self).__init__()
self.eps = eps
def forward(self, x, y):
diff = x - y
loss = torch.sum(torch.sqrt... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
impo... | sutkarsh/EDVR | CharbonnierLoss | false | 4,401 | [
"Apache-2.0"
] | 0 | cd9f2d46edbb00333d8ffb31aebc52cfbda4b6e3 | https://github.com/sutkarsh/EDVR/tree/cd9f2d46edbb00333d8ffb31aebc52cfbda4b6e3 |
ConvLayer | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.nn.functional as f
class ConvLayer(nn.Conv3d):
def __init__(self, network_config, config, name, in_shape, groups=1):
self.name = name
self.layer_config = config
self.network_config = network_conf... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | superrrpotato/Spike-Train-Predict | ConvLayer | false | 4,402 | [
"MIT"
] | 0 | 0a924e5af11c2fc58cf9049a73fff00970a3c967 | https://github.com/superrrpotato/Spike-Train-Predict/tree/0a924e5af11c2fc58cf9049a73fff00970a3c967 |
Policy | import torch
import torch.nn as nn
class Policy(nn.Module):
def __init__(self, num_inputs, num_outputs):
super(Policy, self).__init__()
self.affine1 = nn.Linear(num_inputs, 64)
self.affine2 = nn.Linear(64, 64)
self.action_mean = nn.Linear(64, num_outputs)
self.action_mean.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | SaminYeasar/pytorch-trpo | Policy | false | 4,403 | [
"MIT"
] | 0 | 653a3357cf0461c175fb741604c0cd4ad1f4b841 | https://github.com/SaminYeasar/pytorch-trpo/tree/653a3357cf0461c175fb741604c0cd4ad1f4b841 |
Gate | import torch
from torch import nn
class Gate(nn.Module):
def __init__(self, input_size, dropout=0.2):
""" To determine the importance of passage parts and
attend to the ones relevant to the question, this Gate was added
to the input of RNNCell in both Gated Attention-based Recurre... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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... | tailerr/R-NET-pytorch | Gate | false | 4,404 | [
"MIT"
] | 0 | a6ed4a02b0cf68bade9e9a43a93ec290a3b6fabd | https://github.com/tailerr/R-NET-pytorch/tree/a6ed4a02b0cf68bade9e9a43a93ec290a3b6fabd |
DAInsHead | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
from torchvision.transforms import functional as F
from torch.nn import functional as F
class DAInsHead(nn.Module):
"""
Adds a simple Instance-level Domain Classifier head
"""
def __init__(self, in_channels):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | shreyasrajesh/DA-Object-Detection | DAInsHead | false | 4,405 | [
"MIT"
] | 0 | b1919fdf49a9f1589c48c63e0a3122852e5557ce | https://github.com/shreyasrajesh/DA-Object-Detection/tree/b1919fdf49a9f1589c48c63e0a3122852e5557ce |
StyleResidual | import torch
from torch import nn
import torch.utils.data
import torch.optim
class StyleResidual(nn.Module):
"""Styling."""
def __init__(self, d_channel: 'int', d_style: 'int', kernel_size: 'int'=1):
super().__init__()
self.rs = nn.Conv1d(in_channels=d_style, out_channels=d_channel,
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.utils.data
import torch.optim
assert_size_stri... | taufique74/nemotest | StyleResidual | false | 4,406 | [
"Apache-2.0"
] | 0 | 812f201913cb9922bedc1b225dff844ffc765bf1 | https://github.com/taufique74/nemotest/tree/812f201913cb9922bedc1b225dff844ffc765bf1 |
TorchGloVeLoss | import torch
import torch.nn as nn
import torch.utils.data
class TorchGloVeLoss(nn.Module):
def __init__(self):
super().__init__()
self.reduction = 'sum'
def forward(self, diffs, weights):
return torch.sum(0.5 * torch.mul(weights, diffs ** 2))
def get_inputs():
return [torch.ra... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guard... | tayfuntuna/cs224u | TorchGloVeLoss | false | 4,407 | [
"Apache-2.0"
] | 0 | 4368090c679d869f21ed2393b9ca0ef217b5c404 | https://github.com/tayfuntuna/cs224u/tree/4368090c679d869f21ed2393b9ca0ef217b5c404 |
TorchGloVeModel | import torch
import torch.nn as nn
import torch.utils.data
from torch.nn.init import xavier_uniform_
class TorchGloVeModel(nn.Module):
def __init__(self, n_words, embed_dim):
super().__init__()
self.n_words = n_words
self.embed_dim = embed_dim
self.W = self._init_weights(self.n_wo... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
from torch.nn.init import xavier_u... | tayfuntuna/cs224u | TorchGloVeModel | false | 4,408 | [
"Apache-2.0"
] | 0 | 4368090c679d869f21ed2393b9ca0ef217b5c404 | https://github.com/tayfuntuna/cs224u/tree/4368090c679d869f21ed2393b9ca0ef217b5c404 |
PoswiseFeedForwardNet | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.nn.functional as F
class PoswiseFeedForwardNet(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.conv1 = nn.Conv1d(in_channels=self.config.d_hidn, out_channels
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | star14ms/transformer-evolution | PoswiseFeedForwardNet | false | 4,409 | [
"Apache-2.0"
] | 0 | 95b57485f59a0cee4528af62e5010002e6a3448a | https://github.com/star14ms/transformer-evolution/tree/95b57485f59a0cee4528af62e5010002e6a3448a |
WL1Loss | import torch
import torch.nn as nn
class WL1Loss(nn.Module):
def __init__(self):
super(WL1Loss, self).__init__()
def forward(self, pred, target, weight):
return torch.mean(weight * torch.abs(pred - target))
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4]), 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
... | tccoin/UM-545-Machine-Learning | WL1Loss | false | 4,410 | [
"MIT"
] | 0 | 0854d7ad7e546c009edeb4a4d3e507ce95b99cf8 | https://github.com/tccoin/UM-545-Machine-Learning/tree/0854d7ad7e546c009edeb4a4d3e507ce95b99cf8 |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import tanh
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.a1 = nn.Conv2d(5, 16, kernel_size=3, padding=1)
self.a2 = nn.Conv2d(16, 16, kernel_size=3, padding=1)
self.a3 = nn.C... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | srivarshan-s/Neural-Chess-2D | Net | false | 4,411 | [
"MIT"
] | 0 | 81ec7eb9b4c3c82dc7f6ba5bd4313bd6ede9994e | https://github.com/srivarshan-s/Neural-Chess-2D/tree/81ec7eb9b4c3c82dc7f6ba5bd4313bd6ede9994e |
PointerNetwork | import torch
from torch import nn
class PointerNetwork(nn.Module):
def __init__(self, input_size, model_dim, attn_size=75, dropout=0.2):
""" Pointer Network
Args:
input_size(int): size of input
Input:
- **H** of shape `(passage_legth... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | tailerr/R-NET-pytorch | PointerNetwork | false | 4,412 | [
"MIT"
] | 0 | a6ed4a02b0cf68bade9e9a43a93ec290a3b6fabd | https://github.com/tailerr/R-NET-pytorch/tree/a6ed4a02b0cf68bade9e9a43a93ec290a3b6fabd |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
def set_init(layers):
for layer in layers:
nn.init.normal_(layer.weight, mean=0.0, std=0.1)
nn.init.constant_(layer.bias, 0.0)
class Net(nn.Module):
def __init__(self, s_dim, a_dim):
super(Net, self).__init__()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | taomo/pytorch-A3C-1 | Net | false | 4,413 | [
"MIT"
] | 0 | 8e26720c75ca8b7e987b267e5e0e652d0c5d23cf | https://github.com/taomo/pytorch-A3C-1/tree/8e26720c75ca8b7e987b267e5e0e652d0c5d23cf |
GlobalWeightedAvgPool2d | import torch
from torch import nn
class GlobalWeightedAvgPool2d(nn.Module):
"""
Global Weighted Average Pooling from paper "Global Weighted Average
Pooling Bridges Pixel-level Localization and Image-level Classification"
"""
def __init__(self, features: 'int', flatten=False):
super().__in... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch im... | theNero93/dfdc_deepfake_challenge | GlobalWeightedAvgPool2d | false | 4,414 | [
"MIT"
] | 0 | ef275206efc6f1b0b7984b370a14bd8db61d1ec1 | https://github.com/theNero93/dfdc_deepfake_challenge/tree/ef275206efc6f1b0b7984b370a14bd8db61d1ec1 |
My_SmoothL1Loss | import torch
class My_SmoothL1Loss(torch.nn.Module):
def __init__(self):
super(My_SmoothL1Loss, self).__init__()
def forward(self, x, y):
total_loss = 0
assert x.shape == y.shape
z = (x - y).float()
mse_mask = (torch.abs(z) < 0.01).float()
l1_mask = (torch.abs... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
assert_size_stride = t... | theleokul/AWR-Adaptive-Weighting-Regression | My_SmoothL1Loss | false | 4,415 | [
"MIT"
] | 0 | a6c224302bab474db8b774a2d009c9497e32f6bd | https://github.com/theleokul/AWR-Adaptive-Weighting-Regression/tree/a6c224302bab474db8b774a2d009c9497e32f6bd |
CategoricalDQN | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.nn.functional as F
class CategoricalDQN(nn.Module):
def __init__(self, num_inputs, num_actions, args):
super(CategoricalDQN, self).__init__()
self.num_inputs = num_inputs
self.num_actions = num_a... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | tegg89/categorical_dqn | CategoricalDQN | false | 4,416 | [
"MIT"
] | 0 | 647c24ee4734450551fc446d3225f57dadd82d48 | https://github.com/tegg89/categorical_dqn/tree/647c24ee4734450551fc446d3225f57dadd82d48 |
UNet | import torch
from torch.functional import F
import torch.nn as nn
import torch.nn.functional as F
class down(nn.Module):
"""
A class for creating neural network blocks containing layers:
Average Pooling --> Convlution + Leaky ReLU --> Convolution + Leaky ReLU
This is used in the UNet Class 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.functional import ... | samuelpietri/Super-SloMo | UNet | false | 4,417 | [
"MIT"
] | 0 | e20eaa5550c30737be42b61f8e82e731cfd17457 | https://github.com/samuelpietri/Super-SloMo/tree/e20eaa5550c30737be42b61f8e82e731cfd17457 |
SelfAttention2d | import torch
from torch import nn
class SelfAttention2d(nn.Module):
def __init__(self, c_in, n_head=1, dropout_rate=0.1):
super().__init__()
assert c_in % n_head == 0
self.norm = nn.GroupNorm(1, c_in)
self.n_head = n_head
self.qkv_proj = nn.Conv2d(c_in, c_in * 3, 1)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | technillogue/v-diffusion-pytorch | SelfAttention2d | false | 4,418 | [
"MIT"
] | 0 | 3aa8c7f32adbde1d1ea3a9650004ffafabe5221b | https://github.com/technillogue/v-diffusion-pytorch/tree/3aa8c7f32adbde1d1ea3a9650004ffafabe5221b |
BCEWithLogitsLoss | import torch
from torch import nn as nn
from torch.utils import data as data
from torch import autograd as autograd
import torch.onnx
class BCEWithLogitsLoss(nn.Module):
def __init__(self, loss_weight=1.0, **kwargs):
super(BCEWithLogitsLoss, self).__init__()
self.bce_wlogits_loss = nn.BCEWithLogi... | 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 ... | theleokul/Real-ESRGAN | BCEWithLogitsLoss | false | 4,419 | [
"BSD-3-Clause"
] | 0 | 0afbc090d012d729e6cb3ff47a80018d53bce3f6 | https://github.com/theleokul/Real-ESRGAN/tree/0afbc090d012d729e6cb3ff47a80018d53bce3f6 |
Emo16 | import torch
import numpy as np
from torch import nn
import torch.nn.functional as F
class Emo16(nn.Module):
def __init__(self, input_size: 'int', num_channels: 'int'=40):
"""
Speech emotion recognition model proposed in:
`Trigeorgis, G., Ringeval, F., Brueckner, R., Marchi, E.,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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
from torch... | tfyd/myEnd2you | Emo16 | false | 4,421 | [
"BSD-3-Clause"
] | 0 | 455d5404a19dd4867cb5db4f30705041d425d2b3 | https://github.com/tfyd/myEnd2you/tree/455d5404a19dd4867cb5db4f30705041d425d2b3 |
ReluWithStats | import torch
import torch.nn as nn
import torch.nn.functional as F
class ReluWithStats(nn.Module):
def __init__(self):
super(ReluWithStats, self).__init__()
self.collect_preact = True
self.avg_preacts = []
def forward(self, preact):
if self.collect_preact:
self.av... | 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
... | thudzj/SPAT | ReluWithStats | false | 4,422 | [
"MIT"
] | 0 | 65632c157f40c05c9aee59080e26457bed5b484c | https://github.com/thudzj/SPAT/tree/65632c157f40c05c9aee59080e26457bed5b484c |
LayerNorm | import torch
import torch.nn as nn
class LayerNorm(nn.LayerNorm):
def __init__(self, normalized_shape, eps=1e-05, elementwise_affine=True):
"""Layer Norm."""
super(LayerNorm, self).__init__(normalized_shape, eps=eps,
elementwise_affine=elementwise_affine)
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_... | thetobysiu/transfer-pytorch-dc-tts | LayerNorm | false | 4,423 | [
"MIT"
] | 0 | 20d0c381970a01f0e343c65aeac2f325be436a7e | https://github.com/thetobysiu/transfer-pytorch-dc-tts/tree/20d0c381970a01f0e343c65aeac2f325be436a7e |
FFNNClassifier | from torch.nn import Module
import torch
from torch import FloatTensor
from torch.nn import Linear
from torch.nn.functional import tanh
from torch.nn.functional import log_softmax
from torch.autograd import Variable
class FFNNClassifier(Module):
def __init__(self, n_inputs, n_hidden, n_outputs):
super(FF... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | theofpa/ci-torcs | FFNNClassifier | false | 4,424 | [
"MIT"
] | 0 | fcd1e9822301f1ad8f633468ed6276059afa94b9 | https://github.com/theofpa/ci-torcs/tree/fcd1e9822301f1ad8f633468ed6276059afa94b9 |
_SepConv1d | import torch
from torch import nn
class _SepConv1d(nn.Module):
"""A simple separable convolution implementation.
The separable convlution is a method to reduce number of the parameters
in the deep learning network for slight decrease in predictions quality.
"""
def __init__(self, ni, no, 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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | thupchnsky/ModifiedBasesAnalysis | _SepConv1d | false | 4,425 | [
"MIT"
] | 0 | 904fab75eb5fdc67a050b3862d1432ecce8cf691 | https://github.com/thupchnsky/ModifiedBasesAnalysis/tree/904fab75eb5fdc67a050b3862d1432ecce8cf691 |
Highway | import torch
import torch.nn as nn
import torch.nn.utils
class Highway(nn.Module):
"""it is not fun"""
def __init__(self, e_word_size, drop_rate=0.3):
super(Highway, self).__init__()
self.w_proj = nn.Linear(e_word_size, e_word_size)
self.w_gate = nn.Linear(e_word_size, e_word_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
import ... | thophan92/cs224n-winter2019 | Highway | false | 4,426 | [
"MIT"
] | 0 | f3f8041b35e949e73167135d662a2bd93e7406de | https://github.com/thophan92/cs224n-winter2019/tree/f3f8041b35e949e73167135d662a2bd93e7406de |
GroupLinear | import torch
import torch.optim
import torch.nn as nn
import torch.nn.functional as f
class GroupLinear(nn.Module):
def __init__(self, groups, channels, map_size, dropout=None):
super(GroupLinear, self).__init__()
self.groups = groups
self.channels = channels
self.map_size = map_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
import torch.optim
import torch.nn as nn
assert_size_stride = torch._C._dynamo.g... | tiruns/grad_proj | GroupLinear | false | 4,427 | [
"MIT"
] | 0 | 8882ff1e3205e346e972d963480c57dbf5aef407 | https://github.com/tiruns/grad_proj/tree/8882ff1e3205e346e972d963480c57dbf5aef407 |
Net | import torch
from torch import 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, padding=1)
self.conv2 = nn.Conv2d(16, 32, 3, padding=1)
self.conv3 = nn.Conv2d(32, 64, 3, padding=1)
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.... | thejammerr/DriveAlert | Net | false | 4,428 | [
"MIT"
] | 0 | bac025c2e2919aeb67ef717e90d3049403ecdef5 | https://github.com/thejammerr/DriveAlert/tree/bac025c2e2919aeb67ef717e90d3049403ecdef5 |
Actor | import torch
import numpy as np
import torch.nn.functional as F
from torch import 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, fc... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | tjkemp/ubik-agent | Actor | false | 4,429 | [
"MIT"
] | 0 | 34e4dd0d6319b8f5c5dba0cd9e087490720b723b | https://github.com/tjkemp/ubik-agent/tree/34e4dd0d6319b8f5c5dba0cd9e087490720b723b |
StableBCELoss | import torch
import torch.nn as nn
class StableBCELoss(nn.Module):
def __init__(self):
super(StableBCELoss, self).__init__()
def forward(self, input, target):
input = input.float().view(-1)
target = target.float().view(-1)
neg_abs = -input.abs()
loss = input.clamp(min... | 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
... | toandaominh1997/understanding_cloud_organization | StableBCELoss | false | 4,431 | [
"MIT"
] | 0 | 7da991ff3da557c18f4585c1b956ed799c104c7c | https://github.com/toandaominh1997/understanding_cloud_organization/tree/7da991ff3da557c18f4585c1b956ed799c104c7c |
AngleMultipleLinear | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Parameter
def normalize(x, dim, p=2, eps=1e-12):
if torch.onnx.is_in_onnx_export():
return OnnxLpNormalization.apply(x, dim, p, eps)
else:
return F.normalize(x, dim=dim)
class OnnxLpNor... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | sovrasov/mmaction2 | AngleMultipleLinear | false | 4,432 | [
"Apache-2.0"
] | 0 | 055625bf6d6e06e9f811cc4f8b0332c18cebc98c | https://github.com/sovrasov/mmaction2/tree/055625bf6d6e06e9f811cc4f8b0332c18cebc98c |
VectorQuantizer | import torch
from torch import nn
from torch.nn import functional as F
class VectorQuantizer(nn.Module):
"""
Reference:
[1] https://github.com/deepmind/sonnet/blob/v2/sonnet/src/nets/vqvae.py
"""
def __init__(self, num_embeddings: 'int', embedding_dim: 'int', beta:
'float'=0.25):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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... | threewisemonkeys-as/PyTorch-VAE | VectorQuantizer | false | 4,433 | [
"Apache-2.0"
] | 0 | 4ed0fc7581d4792b435134aa9e06d5e35a5db118 | https://github.com/threewisemonkeys-as/PyTorch-VAE/tree/4ed0fc7581d4792b435134aa9e06d5e35a5db118 |
Critic | import torch
import numpy as np
import torch.nn.functional as F
from torch import 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, f... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
from torch... | tjkemp/ubik-agent | Critic | false | 4,434 | [
"MIT"
] | 0 | 34e4dd0d6319b8f5c5dba0cd9e087490720b723b | https://github.com/tjkemp/ubik-agent/tree/34e4dd0d6319b8f5c5dba0cd9e087490720b723b |
DeepQNetwork | import torch
import torch as T
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
class DeepQNetwork(nn.Module):
def __init__(self, ALPHA):
super(DeepQNetwork, self).__init__()
self.conv1 = nn.Conv2d(1, 32, 8, stride=4, padding=1)
self.conv2 = nn.Conv2d(32, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch as T
import torc... | SuperSaiyan-God/Reinforcement-Learning | DeepQNetwork | false | 4,435 | [
"MIT"
] | 0 | b43a2997e28ec3bf437c37d060637f6deecf89c6 | https://github.com/SuperSaiyan-God/Reinforcement-Learning/tree/b43a2997e28ec3bf437c37d060637f6deecf89c6 |
Model | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, inputdim):
super(Model, self).__init__()
self.layer1 = nn.Linear(inputdim, 16)
torch.nn.init.xavier_uniform_(self.layer1.weight)
self.layer2 = nn.Linear(16, 32)
torch.nn.init.xavier_uniform_(self... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | terry97-guel/POENet-ActiveLearning | Model | false | 4,436 | [
"MIT"
] | 0 | 78e959c8c5eacc5b2dc4e3334ed609d182ce7b6c | https://github.com/terry97-guel/POENet-ActiveLearning/tree/78e959c8c5eacc5b2dc4e3334ed609d182ce7b6c |
wide_basic | import torch
import torch.nn as nn
def get_norm(n_filters, norm):
if norm is None:
return Identity()
elif norm == 'batch':
return nn.BatchNorm2d(n_filters, momentum=0.9)
elif norm == 'instance':
return nn.InstanceNorm2d(n_filters, affine=True)
elif norm == 'layer':
retu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | tianyi21/JEM | wide_basic | false | 4,437 | [
"Apache-2.0"
] | 0 | 59b4bb87be1b1643731540133df557edd7780a88 | https://github.com/tianyi21/JEM/tree/59b4bb87be1b1643731540133df557edd7780a88 |
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