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 |
|---|---|---|---|---|---|---|---|---|---|---|
TRPO | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
def flat_grad(grads):
grad_flatten = []
for grad in grads:
grad_flatten.append(grad.view(-1))
grad_flatten = torch.cat(grad_flatten)
return grad_flatten
def flat_hessian(hessians):
hessians_flatten = []... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | g6ling/Pytorch-Cartpole | TRPO | false | 15,388 | [
"MIT"
] | 116 | ecb7b622cfefe825ac95388cceb6752413d90a2a | https://github.com/g6ling/Pytorch-Cartpole/tree/ecb7b622cfefe825ac95388cceb6752413d90a2a |
ResNetV2 | import torch
import torch.nn.functional as F
import torch.nn as nn
from collections import OrderedDict
def conv3x3(cin, cout, stride=1, groups=1, bias=False):
return StdConv2d(cin, cout, kernel_size=3, stride=stride, padding=1,
bias=bias, groups=groups)
def conv1x1(cin, cout, stride=1, bias=False):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | RicJM/weighted_c2d | ResNetV2 | false | 15,389 | [
"MIT"
] | 49 | 38053869b77c1544349c53ba6f3c1325254aa413 | https://github.com/RicJM/weighted_c2d/tree/38053869b77c1544349c53ba6f3c1325254aa413 |
Capsule | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class Capsule(nn.Module):
def __init__(self, cfg):
super(Capsule, self).__init__()
self.input_dim_capsule = cfg.input_dim_capsule
self.dim_capsule = cfg.dim_capsule
self.num_capsule = cfg.num_capsule
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | fmc123653/DeepKE | Capsule | false | 15,390 | [
"MIT"
] | 676 | 4d30e51368681c7cb73e2ecacf9b922b441cbe99 | https://github.com/fmc123653/DeepKE/tree/4d30e51368681c7cb73e2ecacf9b922b441cbe99 |
BalancedLoss | import torch
from torch import nn
import torch.nn.functional as F
class BalancedLoss(nn.Module):
def __init__(self, neg_weight=1.0):
super(BalancedLoss, self).__init__()
self.neg_weight = neg_weight
def forward(self, input, target):
pos_mask = target == 0
neg_mask = target ==... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch ... | gabrielsluz/vince | BalancedLoss | false | 15,391 | [
"Apache-2.0"
] | 61 | f4e17a2cf70c080a7e01e46d15537e33224c869b | https://github.com/gabrielsluz/vince/tree/f4e17a2cf70c080a7e01e46d15537e33224c869b |
GAE | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
class GAE(nn.Module):
def __init__(self, num_inputs, num_outputs):
super(GAE, self).__init__()
self.num_inputs = num_inputs
self.num_outputs = num_outputs
self.fc = nn.Linear(num_inputs, 128)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | g6ling/Pytorch-Cartpole | GAE | false | 15,392 | [
"MIT"
] | 116 | ecb7b622cfefe825ac95388cceb6752413d90a2a | https://github.com/g6ling/Pytorch-Cartpole/tree/ecb7b622cfefe825ac95388cceb6752413d90a2a |
TemperatureHolder | import torch
from torch import nn
class TemperatureHolder(nn.Module):
"""Module that holds a temperature as a learnable value.
Args:
initial_log_temperature (float): Initial value of log(temperature).
"""
def __init__(self, initial_log_temperature=0):
super().__init__()
self.... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_... | g-votte/pfrl | TemperatureHolder | false | 15,393 | [
"MIT"
] | 824 | 4c30c1d73f0941a2b649b62937eec346bb55a95e | https://github.com/g-votte/pfrl/tree/4c30c1d73f0941a2b649b62937eec346bb55a95e |
ConvCompress | import torch
from torch import nn
class ConvCompress(nn.Module):
def __init__(self, dim, ratio=4):
super().__init__()
self.conv = nn.Conv1d(dim, dim, ratio, stride=ratio)
def forward(self, mem):
mem = mem.transpose(1, 2)
compressed_mem = self.conv(mem)
return compress... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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... | fwka92/compressive-transformer-pytorch | ConvCompress | false | 15,394 | [
"MIT"
] | 108 | e51faba52a8c1ec6a8b966e5b912e6ecc3840f57 | https://github.com/fwka92/compressive-transformer-pytorch/tree/e51faba52a8c1ec6a8b966e5b912e6ecc3840f57 |
ImageToTensor | import torch
import numpy as np
import torch.optim
import torch.nn as nn
import torch.nn.utils
import torch.autograd
class BaseMetric:
""" Base class for all the metrics """
def __init__(self, name):
self.name = name
def calculate(self, batch_info):
""" Calculate value of a metric based ... | 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 numpy as np
import torch.optim
import torch.nn as nn
import torch.nn.utils
import torch.autograd
assert_size_stride = torch._C._dynam... | galatolofederico/vel | ImageToTensor | false | 15,395 | [
"MIT"
] | 273 | 0473648cffb3f34fb784d12dbb25844ab58ffc3c | https://github.com/galatolofederico/vel/tree/0473648cffb3f34fb784d12dbb25844ab58ffc3c |
PreNormTransformerDecoderLayer | import torch
import torch.nn as nn
class PreNormTransformerDecoderLayer(nn.TransformerDecoderLayer):
"""
A variant of :class:`torch.nn.TransformerDecoderLayer` where layer
normalization is included inside the residual branch, and performed before
self-attention and feedforward layers.
Refer docum... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | funnyzhou/REFERS | PreNormTransformerDecoderLayer | false | 15,396 | [
"MIT"
] | 46 | 392eddf13cbf3c3a7dc0bf8bfffd108ca4a65a19 | https://github.com/funnyzhou/REFERS/tree/392eddf13cbf3c3a7dc0bf8bfffd108ca4a65a19 |
CausalConv1d | import torch
from torch import nn
class CausalConv1d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=2, dilation=2):
super(CausalConv1d, self).__init__()
self.padding = dilation
self.causal_conv = nn.Conv1d(in_channels, out_channels, kernel_size,
padding=... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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... | gaotianyu1350/new_fewrel_bertpair | CausalConv1d | false | 15,397 | [
"MIT"
] | 180 | 27184050d476fc93576948fb26680d508a2824bb | https://github.com/gaotianyu1350/new_fewrel_bertpair/tree/27184050d476fc93576948fb26680d508a2824bb |
OneHotEncode | import torch
import torch.optim
import torch.nn as nn
import torch.nn.utils
import torch.autograd
def one_hot_encoding(input_tensor, num_labels):
""" One-hot encode labels from input """
xview = input_tensor.view(-1, 1)
onehot = torch.zeros(xview.size(0), num_labels, device=input_tensor.
device, 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
import torch.optim
import torch.nn as nn
import torch.nn.utils
import torch.autograd
assert_size_stride = torch._C._dynamo.guards.assert_siz... | galatolofederico/vel | OneHotEncode | false | 15,399 | [
"MIT"
] | 273 | 0473648cffb3f34fb784d12dbb25844ab58ffc3c | https://github.com/galatolofederico/vel/tree/0473648cffb3f34fb784d12dbb25844ab58ffc3c |
TimeBlock | import torch
import torch.nn as nn
import torch.nn.functional as F
class TimeBlock(nn.Module):
"""
Neural network block that applies a temporal convolution to each node of
a graph in isolation.
"""
def __init__(self, in_channels, out_channels, kernel_size=3):
"""
:param in_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._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | garygsw/STGCN-PyTorch | TimeBlock | false | 15,400 | [
"MIT"
] | 220 | 83ae49e566c779444efd21fc03cce54a765ee9f7 | https://github.com/garygsw/STGCN-PyTorch/tree/83ae49e566c779444efd21fc03cce54a765ee9f7 |
DenseBlock | import torch
from torch import nn
from torch.nn import functional as F
class CausalConv1d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=2, dilation=2):
super(CausalConv1d, self).__init__()
self.padding = dilation
self.causal_conv = nn.Conv1d(in_channels, out_channe... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | gaotianyu1350/new_fewrel_bertpair | DenseBlock | false | 15,401 | [
"MIT"
] | 180 | 27184050d476fc93576948fb26680d508a2824bb | https://github.com/gaotianyu1350/new_fewrel_bertpair/tree/27184050d476fc93576948fb26680d508a2824bb |
DiagGaussianActionHead | import torch
import numpy as np
import torch.optim
import torch.nn as nn
import torch.nn.init as init
import torch.nn.utils
import torch.autograd
class DiagGaussianActionHead(nn.Module):
"""
Action head where actions are normally distibuted uncorrelated variables with specific means and variances.
Means ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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 numpy as np
import torch.optim
import torch.nn as nn
import torch.nn.init... | galatolofederico/vel | DiagGaussianActionHead | false | 15,402 | [
"MIT"
] | 273 | 0473648cffb3f34fb784d12dbb25844ab58ffc3c | https://github.com/galatolofederico/vel/tree/0473648cffb3f34fb784d12dbb25844ab58ffc3c |
DotAttention | import torch
import torch.nn as nn
import torch.nn.functional as F
class DotAttention(nn.Module):
def __init__(self, dropout=0.0):
super(DotAttention, self).__init__()
self.dropout = dropout
def forward(self, Q, K, V, mask_out=None, head_mask=None):
"""
一般输入信息 X 时，假设 K = V = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | fmc123653/DeepKE | DotAttention | false | 15,403 | [
"MIT"
] | 676 | 4d30e51368681c7cb73e2ecacf9b922b441cbe99 | https://github.com/fmc123653/DeepKE/tree/4d30e51368681c7cb73e2ecacf9b922b441cbe99 |
CosSim | import torch
import torch.nn as nn
class CosSim(nn.Module):
def __init__(self, nfeat, nclass, codebook=None, learn_cent=True):
super(CosSim, self).__init__()
self.nfeat = nfeat
self.nclass = nclass
self.learn_cent = learn_cent
if codebook is None:
codebook = to... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | gajrajgchouhan/orthohash | CosSim | false | 15,404 | [
"BSD-3-Clause"
] | 51 | 4e04cfe1dd32e21ba004e308d5a1ce9c8578ea2b | https://github.com/gajrajgchouhan/orthohash/tree/4e04cfe1dd32e21ba004e308d5a1ce9c8578ea2b |
PrecomputedNorm | import torch
import torch.nn as nn
class PrecomputedNorm(nn.Module):
"""Normalization using Pre-computed Mean/Std.
Args:
stats: Precomputed (mean, std).
axis: Axis setting used to calculate mean/variance.
"""
def __init__(self, stats, axis=[1, 2]):
super().__init__()
s... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | gcambara/s3prl | PrecomputedNorm | false | 15,405 | [
"MIT"
] | 856 | 33284ebde3a903ed8604d6dae85669d0174ae1d3 | https://github.com/gcambara/s3prl/tree/33284ebde3a903ed8604d6dae85669d0174ae1d3 |
PGenLayer | import torch
import torch.nn as nn
import torch.nn.functional as F
class PGenLayer(nn.Module):
def __init__(self, emb_dim, hidden_size, enc_dim):
super(PGenLayer, self).__init__()
self.emb_dim = emb_dim
self.hidden_size = hidden_size
self.enc_dim = enc_dim
self.lin = nn.Li... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | gau820827/AI-writer_Data2Doc | PGenLayer | false | 15,406 | [
"Apache-2.0"
] | 77 | 6be0ee6238158a47aa0fdfa8a34df2a47714835a | https://github.com/gau820827/AI-writer_Data2Doc/tree/6be0ee6238158a47aa0fdfa8a34df2a47714835a |
AMSoftmaxLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class AMSoftmaxLoss(nn.Module):
def __init__(self, hidden_dim, speaker_num, s=30.0, m=0.4, **kwargs):
"""
AM Softmax Loss
"""
super(AMSoftmaxLoss, self).__init__()
self.s = s
self.m = m
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.... | gcambara/s3prl | AMSoftmaxLoss | false | 15,407 | [
"MIT"
] | 856 | 33284ebde3a903ed8604d6dae85669d0174ae1d3 | https://github.com/gcambara/s3prl/tree/33284ebde3a903ed8604d6dae85669d0174ae1d3 |
TransformerDecoderBlock | import math
import torch
import torch.nn as nn
class AddAndNorm(nn.Module):
def __init__(self, d_model):
super(AddAndNorm, self).__init__()
self.layer_norm = nn.LayerNorm(d_model)
def forward(self, x, residual):
return self.layer_norm(x + residual)
class ScaledDotProductAttention(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.... | francismontalbo/attention-is-all-you-need-paper | TransformerDecoderBlock | false | 15,408 | [
"MIT"
] | 167 | 21ba3e48917da0c6808126d183bece6a9969cfd2 | https://github.com/francismontalbo/attention-is-all-you-need-paper/tree/21ba3e48917da0c6808126d183bece6a9969cfd2 |
TransformerEncoderBlock | import math
import torch
import torch.nn as nn
class AddAndNorm(nn.Module):
def __init__(self, d_model):
super(AddAndNorm, self).__init__()
self.layer_norm = nn.LayerNorm(d_model)
def forward(self, x, residual):
return self.layer_norm(x + residual)
class ScaledDotProductAttention(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.... | francismontalbo/attention-is-all-you-need-paper | TransformerEncoderBlock | false | 15,409 | [
"MIT"
] | 167 | 21ba3e48917da0c6808126d183bece6a9969cfd2 | https://github.com/francismontalbo/attention-is-all-you-need-paper/tree/21ba3e48917da0c6808126d183bece6a9969cfd2 |
Attn | import torch
import torch.nn as nn
import torch.nn.functional as F
class Attn(nn.Module):
""" The score function for the attention mechanism.
We define the score function as the general function from Luong et al.
Where score(s_{i}, h_{j}) = s_{i} * W * h_{j}
"""
def __init__(self, hidden_size):... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | gau820827/AI-writer_Data2Doc | Attn | false | 15,410 | [
"Apache-2.0"
] | 77 | 6be0ee6238158a47aa0fdfa8a34df2a47714835a | https://github.com/gau820827/AI-writer_Data2Doc/tree/6be0ee6238158a47aa0fdfa8a34df2a47714835a |
AP | import torch
import torch.nn as nn
class AttentivePooling(nn.Module):
"""
Implementation of Attentive Pooling
"""
def __init__(self, input_dim, **kwargs):
super(AttentivePooling, self).__init__()
self.W_a = nn.Linear(input_dim, input_dim)
self.W = nn.Linear(input_dim, 1)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | gcambara/s3prl | AP | false | 15,411 | [
"MIT"
] | 856 | 33284ebde3a903ed8604d6dae85669d0174ae1d3 | https://github.com/gcambara/s3prl/tree/33284ebde3a903ed8604d6dae85669d0174ae1d3 |
AttentivePoolingModule | import torch
import torch.nn as nn
class AttentivePoolingModule(nn.Module):
"""
Implementation of Attentive Pooling
"""
def __init__(self, input_dim, activation='ReLU', **kwargs):
super(AttentivePoolingModule, self).__init__()
self.W_a = nn.Linear(input_dim, input_dim)
self.W... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | gcambara/s3prl | AttentivePoolingModule | false | 15,412 | [
"MIT"
] | 856 | 33284ebde3a903ed8604d6dae85669d0174ae1d3 | https://github.com/gcambara/s3prl/tree/33284ebde3a903ed8604d6dae85669d0174ae1d3 |
ASP | import torch
import torch.nn as nn
class AttentivePooling(nn.Module):
"""
Implementation of Attentive Pooling
"""
def __init__(self, input_dim, **kwargs):
super(AttentivePooling, self).__init__()
self.W_a = nn.Linear(input_dim, input_dim)
self.W = nn.Linear(input_dim, 1)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | gcambara/s3prl | ASP | false | 15,413 | [
"MIT"
] | 856 | 33284ebde3a903ed8604d6dae85669d0174ae1d3 | https://github.com/gcambara/s3prl/tree/33284ebde3a903ed8604d6dae85669d0174ae1d3 |
RegLoss | import torch
import torch.nn as nn
class RegLoss(nn.Module):
""" RegLoss, L2 regularization on model parameters
"""
def __init__(self):
super(RegLoss, self).__init__()
def forward(self, parameters):
reg_loss = None
for W in parameters:
if reg_loss is None:
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | geekinglcq/HRec | RegLoss | false | 15,414 | [
"MIT"
] | 49 | b3a67f7721e6e73a7af37d308b5b00e9df68d495 | https://github.com/geekinglcq/HRec/tree/b3a67f7721e6e73a7af37d308b5b00e9df68d495 |
SelfAttentionPooling | import torch
import torch.nn as nn
class SelfAttentionPooling(nn.Module):
"""
Implementation of SelfAttentionPooling
Original Paper: Self-Attention Encoding and Pooling for Speaker Recognition
https://arxiv.org/pdf/2008.01077v1.pdf
"""
def __init__(self, input_dim):
super(SelfAttentio... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | gcambara/s3prl | SelfAttentionPooling | false | 15,415 | [
"MIT"
] | 856 | 33284ebde3a903ed8604d6dae85669d0174ae1d3 | https://github.com/gcambara/s3prl/tree/33284ebde3a903ed8604d6dae85669d0174ae1d3 |
SoftmaxLoss | import torch
import torch.nn as nn
class SoftmaxLoss(nn.Module):
def __init__(self, hidden_dim, speaker_num, **kwargs):
"""
Softmax Loss
"""
super(SoftmaxLoss, self).__init__()
self.fc = nn.Linear(hidden_dim, speaker_num)
self.loss = nn.CrossEntropyLoss()
def ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | gcambara/s3prl | SoftmaxLoss | false | 15,416 | [
"MIT"
] | 856 | 33284ebde3a903ed8604d6dae85669d0174ae1d3 | https://github.com/gcambara/s3prl/tree/33284ebde3a903ed8604d6dae85669d0174ae1d3 |
MLP | from torch.nn import Module
import torch
import torch.nn as nn
from torch.nn.modules.module import Module
class MLP(Module):
"""
A Simple two layers MLP to make SGC a bit better.
"""
def __init__(self, nfeat, nhid, nclass, dp=0.2):
super(MLP, self).__init__()
self.W1 = nn.Linear(nfeat... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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
import torch.nn as nn
from torch.nn.modules.module i... | gear/gfnn | MLP | false | 15,417 | [
"MIT"
] | 46 | 36667861caacba921469d43917d002896e832c3f | https://github.com/gear/gfnn/tree/36667861caacba921469d43917d002896e832c3f |
KGCN | from torch.nn import Module
import math
import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
class GraphConvolution(Module):
"""
Simple GCN layer
"""
def __init__(self, in_features, out_features, bias=Tr... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn import Module
i... | gear/gfnn | KGCN | false | 15,418 | [
"MIT"
] | 46 | 36667861caacba921469d43917d002896e832c3f | https://github.com/gear/gfnn/tree/36667861caacba921469d43917d002896e832c3f |
L2NormLoss | import torch
import torch.utils.data
import torch.nn as nn
class L2NormLoss(nn.Module):
def __init__(self):
super(L2NormLoss, self).__init__()
def forward(self, x1, x2, y1, y2):
dist_in = torch.norm(x1 - x2, dim=1, keepdim=True)
dist_out = torch.norm(y1 - y2, dim=1, keepdim=True)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dy... | gfiumara/MSU-LatentAFIS | L2NormLoss | false | 15,419 | [
"MIT"
] | 53 | 682464b0bc4501977f1304c51e2638c0ee89d87c | https://github.com/gfiumara/MSU-LatentAFIS/tree/682464b0bc4501977f1304c51e2638c0ee89d87c |
AttLayer | import torch
import torch.nn as nn
import torch.nn.functional as fn
class AttLayer(nn.Module):
"""Calculate the attention signal(weight) according the input tensor.
Args:
infeatures (torch.FloatTensor): A 3D input tensor with shape of[batch_size, M, embed_dim].
Returns:
torch.FloatTensor... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | geekinglcq/HRec | AttLayer | false | 15,420 | [
"MIT"
] | 49 | b3a67f7721e6e73a7af37d308b5b00e9df68d495 | https://github.com/geekinglcq/HRec/tree/b3a67f7721e6e73a7af37d308b5b00e9df68d495 |
VisErrorLossV2 | import torch
import torch.nn.functional as F
from torch import nn
class VisErrorLossV2(nn.Module):
def __init__(self):
super(VisErrorLossV2, self).__init__()
def compute_l1_weighted_loss(self, hm_targets, hm_preds, vismap, ohem=1.0):
"""
:param hm_targets: [batch size, keypoint numbe... | 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.functi... | gathierry/FashionAI-KeyPointsDetectionOfApparel | VisErrorLossV2 | false | 15,421 | [
"Apache-2.0"
] | 174 | 2e0942b42b4a9cd974cdddc151675738dc8a8cb4 | https://github.com/gathierry/FashionAI-KeyPointsDetectionOfApparel/tree/2e0942b42b4a9cd974cdddc151675738dc8a8cb4 |
RobertaOutput | from _paritybench_helpers import _mock_config
import torch
from torch import nn
import torch.utils.checkpoint
class RobertaOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(c... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | IntelLabs/Model-Compression-Research-Package | RobertaOutput | false | 15,422 | [
"Apache-2.0"
] | 58 | 69aecbf5cc73b10fab88a13d8ca6d8314d284c0b | https://github.com/IntelLabs/Model-Compression-Research-Package/tree/69aecbf5cc73b10fab88a13d8ca6d8314d284c0b |
Net | import torch
import torch.nn as nn
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1_1 = nn.Conv2d(1, 8, 5, 2, 0)
self.conv2_1 = nn.Conv2d(8, 16, 3, 1, 0)
self.conv2_2 = nn.Conv2d(16, 16, 3, 1, 0)
self.conv3_1 = nn.Conv2d(16, 24, 3, 1, 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... | fengjixuchui/EmbeddedSystem | Net | false | 15,423 | [
"MIT"
] | 228 | ae17e41bb120922a99f2d91818c381e38e868040 | https://github.com/fengjixuchui/EmbeddedSystem/tree/ae17e41bb120922a99f2d91818c381e38e868040 |
Delta | import torch
import torch.nn as nn
from torchaudio import transforms
class Delta(nn.Module):
def __init__(self, order=2, **kwargs):
super(Delta, self).__init__()
self.order = order
self.compute_delta = transforms.ComputeDeltas(**kwargs)
def forward(self, x):
feats = [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
from torchaudio import transforms
assert_size_stride = tor... | gcambara/s3prl | Delta | false | 15,424 | [
"MIT"
] | 856 | 33284ebde3a903ed8604d6dae85669d0174ae1d3 | https://github.com/gcambara/s3prl/tree/33284ebde3a903ed8604d6dae85669d0174ae1d3 |
Glu | import torch
import torch.nn as nn
class Glu(nn.Module):
def __init__(self, dim):
super(Glu, self).__init__()
self.dim = dim
def forward(self, x):
x_in, x_gate = x.chunk(2, dim=self.dim)
return x_in * x_gate.sigmoid()
def get_inputs():
return [torch.rand([4, 4, 4, 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | gheyret/EfficientConformer | Glu | false | 15,425 | [
"Apache-2.0"
] | 101 | b28a0aaa3b182f72abaccbeb12df0402adf96097 | https://github.com/gheyret/EfficientConformer/tree/b28a0aaa3b182f72abaccbeb12df0402adf96097 |
VisErrorLoss | import torch
import torch.nn.functional as F
from torch import nn
class VisErrorLoss(nn.Module):
def __init__(self):
super(VisErrorLoss, self).__init__()
def compute_l1_weighted_loss(self, hm_targets, hm_preds, vismap, ohem=1.0):
"""
:param hm_targets: [batch size, keypoint number, h... | 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.functi... | gathierry/FashionAI-KeyPointsDetectionOfApparel | VisErrorLoss | false | 15,426 | [
"Apache-2.0"
] | 174 | 2e0942b42b4a9cd974cdddc151675738dc8a8cb4 | https://github.com/gathierry/FashionAI-KeyPointsDetectionOfApparel/tree/2e0942b42b4a9cd974cdddc151675738dc8a8cb4 |
GroupedMultiHeadAttention | import torch
import torch.nn as nn
import torch.nn.functional as F
class Linear(nn.Linear):
def __init__(self, in_features, out_features, bias=True):
super(Linear, self).__init__(in_features=in_features, out_features=
out_features, bias=bias)
self.noise = None
self.vn_std = 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.triton_helpers import math as tl_math
import torch.... | gheyret/EfficientConformer | GroupedMultiHeadAttention | false | 15,427 | [
"Apache-2.0"
] | 101 | b28a0aaa3b182f72abaccbeb12df0402adf96097 | https://github.com/gheyret/EfficientConformer/tree/b28a0aaa3b182f72abaccbeb12df0402adf96097 |
RelativeThreshold_RegLoss | import torch
import torch.nn as nn
import torch.nn.init
class RelativeThreshold_RegLoss(nn.Module):
def __init__(self, threshold, size_average=True):
super(RelativeThreshold_RegLoss, self).__init__()
self.size_average = size_average
self.eps = 1e-07
self.threshold = threshold
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
import torch.nn.init
assert_size_stride = torch._C.... | ginobilinie/medSynthesisV1 | RelativeThreshold_RegLoss | false | 15,428 | [
"MIT"
] | 166 | 1fd202c5928466ef9b11cfebc4490341899312e7 | https://github.com/ginobilinie/medSynthesisV1/tree/1fd202c5928466ef9b11cfebc4490341899312e7 |
Conv1d | import torch
import torch.nn as nn
import torch.nn.functional as F
class Conv1d(nn.Conv1d):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding='same', dilation=1, groups=1, bias=True):
super(Conv1d, self).__init__(in_channels=in_channels, out_channels=
out_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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | gheyret/EfficientConformer | Conv1d | false | 15,429 | [
"Apache-2.0"
] | 101 | b28a0aaa3b182f72abaccbeb12df0402adf96097 | https://github.com/gheyret/EfficientConformer/tree/b28a0aaa3b182f72abaccbeb12df0402adf96097 |
GCN | import torch
import torch.nn.functional as F
from torch import nn
import torch.nn.parallel
class Conv2D(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, padding=
'same', stride=1, dilation=1, groups=1):
super(Conv2D, self).__init__()
assert type(kernel_size) in [int,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn.functional as F
from torch import nn
import torch.nn.parallel
as... | gist-ailab/uoais | GCN | false | 15,430 | [
"BSD-2-Clause"
] | 52 | fb42d9a96cd54daad61c956d8d9d65dd0ebef4c7 | https://github.com/gist-ailab/uoais/tree/fb42d9a96cd54daad61c956d8d9d65dd0ebef4c7 |
maxPool23DUinit | import torch
import torch.nn as nn
import torch.nn.init
class maxPool23DUinit(nn.Module):
def __init__(self, kernel_size, stride, padding=1, dilation=1, nd=2):
super(maxPool23DUinit, self).__init__()
assert nd == 1 or nd == 2 or nd == 3, 'nd is not correctly specified!!!!, it should be {1,2,3}'
... | 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.nn.init
assert_size_stride = torch._C._dynamo.guards.a... | ginobilinie/medSynthesisV1 | maxPool23DUinit | false | 15,431 | [
"MIT"
] | 166 | 1fd202c5928466ef9b11cfebc4490341899312e7 | https://github.com/ginobilinie/medSynthesisV1/tree/1fd202c5928466ef9b11cfebc4490341899312e7 |
residualUnit | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
import torch.nn.init
class conv23DUnit(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, groups=1, bias=True, dilation=1, nd=2):
super(conv2... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ginobilinie/medSynthesisV1 | residualUnit | false | 15,432 | [
"MIT"
] | 166 | 1fd202c5928466ef9b11cfebc4490341899312e7 | https://github.com/ginobilinie/medSynthesisV1/tree/1fd202c5928466ef9b11cfebc4490341899312e7 |
PACRRConvMax2dModule | import torch
class PACRRConvMax2dModule(torch.nn.Module):
def __init__(self, shape, n_filters, k, channels):
super().__init__()
self.shape = shape
if shape != 1:
self.pad = torch.nn.ConstantPad2d((0, shape - 1, 0, shape - 1), 0)
else:
self.pad = None
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C... | gitter-badger/FlexNeuART | PACRRConvMax2dModule | false | 15,433 | [
"Apache-2.0"
] | 101 | f69e5421bdebe9db0d993b5470dace61872f90df | https://github.com/gitter-badger/FlexNeuART/tree/f69e5421bdebe9db0d993b5470dace61872f90df |
VisErrorLossV3 | import torch
import torch.nn.functional as F
from torch import nn
class VisErrorLossV3(nn.Module):
def __init__(self):
super(VisErrorLossV3, self).__init__()
def compute_l1_weighted_loss(self, hm_targets, hm_preds, vismap, ohem=1.0):
"""
:param hm_targets: [batch size, keypoint numbe... | 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.functi... | gathierry/FashionAI-KeyPointsDetectionOfApparel | VisErrorLossV3 | false | 15,434 | [
"Apache-2.0"
] | 174 | 2e0942b42b4a9cd974cdddc151675738dc8a8cb4 | https://github.com/gathierry/FashionAI-KeyPointsDetectionOfApparel/tree/2e0942b42b4a9cd974cdddc151675738dc8a8cb4 |
ClusterAssignment | import torch
import torch.nn as nn
from torch.nn import Parameter
from typing import Optional
class ClusterAssignment(nn.Module):
def __init__(self, cluster_number: 'int', embedding_dimension: 'int',
alpha: 'float'=1.0, cluster_centers: 'Optional[torch.Tensor]'=None
) ->None:
"""
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.nn import Parameter
from typing import Optional
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | giorgosVardakas/pt-dec | ClusterAssignment | false | 15,435 | [
"MIT"
] | 200 | c29b9634eb74c828efd9d2b87c613cdb0ddd1dd5 | https://github.com/giorgosVardakas/pt-dec/tree/c29b9634eb74c828efd9d2b87c613cdb0ddd1dd5 |
SqueezeAndExcitationModule | import torch
import torch.nn as nn
import torch.nn.functional as F
class Swish(nn.Module):
def __init__(self):
super(Swish, self).__init__()
def forward(self, x):
return x * x.sigmoid()
class Conv1d(nn.Conv1d):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | gheyret/EfficientConformer | SqueezeAndExcitationModule | false | 15,436 | [
"Apache-2.0"
] | 101 | b28a0aaa3b182f72abaccbeb12df0402adf96097 | https://github.com/gheyret/EfficientConformer/tree/b28a0aaa3b182f72abaccbeb12df0402adf96097 |
_Extraction | import torch
from torch import Tensor
import torch.onnx.operators
def create_max_segment_mask(tensor: 'Tensor', max_segment_length):
"""
Create max-segment mask.
Args:
tensor:
:math: (N, T, *) where T is target dimension
Returns:
- max-segment mask:
:math:`(N,... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch import Tensor
imp... | godweiyang/ParaGen | _Extraction | false | 15,437 | [
"Apache-2.0"
] | 50 | 9665d1244ea38a41fc06b4e0a7f6411985e2221f | https://github.com/godweiyang/ParaGen/tree/9665d1244ea38a41fc06b4e0a7f6411985e2221f |
MultiHeadLinearAttention | import torch
import torch.nn as nn
import torch.nn.functional as F
class Linear(nn.Linear):
def __init__(self, in_features, out_features, bias=True):
super(Linear, self).__init__(in_features=in_features, out_features=
out_features, bias=bias)
self.noise = None
self.vn_std = No... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | gheyret/EfficientConformer | MultiHeadLinearAttention | false | 15,438 | [
"Apache-2.0"
] | 101 | b28a0aaa3b182f72abaccbeb12df0402adf96097 | https://github.com/gheyret/EfficientConformer/tree/b28a0aaa3b182f72abaccbeb12df0402adf96097 |
PowerLaw_Compressed_Loss | import torch
import torch.nn as nn
import torch.utils.data
class PowerLaw_Compressed_Loss(nn.Module):
def __init__(self, power=0.3, complex_loss_ratio=0.113):
super(PowerLaw_Compressed_Loss, self).__init__()
self.power = power
self.complex_loss_ratio = complex_loss_ratio
self.crit... | 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... | giuliacassara/VoiceSplit | PowerLaw_Compressed_Loss | false | 15,439 | [
"Apache-2.0"
] | 84 | 1aa98dce9460db7ec6c5449eb7f92e3902f71a2a | https://github.com/giuliacassara/VoiceSplit/tree/1aa98dce9460db7ec6c5449eb7f92e3902f71a2a |
AUXModule | import torch
import torch.nn as nn
import torch.nn.functional as F
class AUXModule(nn.Module):
def __init__(self, in_features, out_features):
super().__init__()
self.linear = nn.Linear(in_features, out_features)
def forward(self, x):
x = F.adaptive_max_pool2d(x, output_size=(1, 1))
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | gorogoroyasu/mlcomp | AUXModule | false | 15,440 | [
"Apache-2.0"
] | 166 | fc6572ca5b226b35df97f13badd4420b30468a3b | https://github.com/gorogoroyasu/mlcomp/tree/fc6572ca5b226b35df97f13badd4420b30468a3b |
HuggingfaceClassifier | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.onnx.operators
def get_activation_fn(activation):
"""
Get activation function by name
Args:
activation: activation function name
Returns:
- activation function
"""
if activation == 'relu':
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn.functional as... | godweiyang/ParaGen | HuggingfaceClassifier | false | 15,441 | [
"Apache-2.0"
] | 50 | 9665d1244ea38a41fc06b4e0a7f6411985e2221f | https://github.com/godweiyang/ParaGen/tree/9665d1244ea38a41fc06b4e0a7f6411985e2221f |
SimpleTextClassifier | import torch
import torch.nn as nn
import torch.nn.functional as F
class SimpleTextClassifier(nn.Module):
"""Text Classifier with 1 hidden layer
"""
def __init__(self, num_labels, vocab_size):
super(SimpleTextClassifier, self).__init__()
self.linear1 = nn.Linear(vocab_size, 128)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | goodmike31/pytorch_active_learning | SimpleTextClassifier | false | 15,442 | [
"MIT"
] | 629 | 1224efad1f8022efa933cd36e30f78ed06eaaea7 | https://github.com/goodmike31/pytorch_active_learning/tree/1224efad1f8022efa933cd36e30f78ed06eaaea7 |
LocalMultiHeadAttention | import torch
import torch.nn as nn
import torch.nn.functional as F
class Linear(nn.Linear):
def __init__(self, in_features, out_features, bias=True):
super(Linear, self).__init__(in_features=in_features, out_features=
out_features, bias=bias)
self.noise = None
self.vn_std = No... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | gheyret/EfficientConformer | LocalMultiHeadAttention | false | 15,443 | [
"Apache-2.0"
] | 101 | b28a0aaa3b182f72abaccbeb12df0402adf96097 | https://github.com/gheyret/EfficientConformer/tree/b28a0aaa3b182f72abaccbeb12df0402adf96097 |
NormedMSE | import torch
import torch.nn as nn
import torch.utils.data
class NormedMSE(nn.MSELoss):
def forward(self, inp, tgt, *args, **kwargs):
"""
Args:
inp: (*, C)
tgt: (*, C)
Will normalize the input before the loss
"""
inp = nn.functional.normalize(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
import torch.nn as nn
import... | gongda0e/AVT | NormedMSE | false | 15,444 | [
"Apache-2.0"
] | 102 | d6a7032b86416e852c76cc04a20ccabe34f111dc | https://github.com/gongda0e/AVT/tree/d6a7032b86416e852c76cc04a20ccabe34f111dc |
output | import math
import torch
import torch.nn as nn
class output(nn.Module):
def __init__(self, scope=512):
super(output, self).__init__()
self.conv1 = nn.Conv2d(32, 1, 1)
self.sigmoid1 = nn.Sigmoid()
self.conv2 = nn.Conv2d(32, 4, 1)
self.sigmoid2 = nn.Sigmoid()
self.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... | glc12125/EAST | output | false | 15,445 | [
"MIT"
] | 366 | cec7ae98f9c21a475b935f74f4c3969f3a989bd4 | https://github.com/glc12125/EAST/tree/cec7ae98f9c21a475b935f74f4c3969f3a989bd4 |
VirtualBatchNorm | import torch
from torch import nn
class VirtualBatchNorm(nn.Module):
"""
Applies Virtual Batch Normalization over a 4D input (a mini-batch
of 2D inputs with additional channel dimension) as described in
paper `Improved Techniques for Training GANs`:
https://arxiv.org/abs/1606.03498
.. math::
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | goktug97/estorch | VirtualBatchNorm | false | 15,446 | [
"MIT"
] | 53 | aa7318b0662faadece1ac9eb241b895d028d613d | https://github.com/goktug97/estorch/tree/aa7318b0662faadece1ac9eb241b895d028d613d |
SimmatModule | import torch
class SimmatModule(torch.nn.Module):
def __init__(self, padding=-1):
super().__init__()
self.padding = padding
self._hamming_index_loaded = None
self._hamming_index = None
def forward(self, query_embed, doc_embed, query_tok, doc_tok):
simmat = []
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | gitter-badger/FlexNeuART | SimmatModule | false | 15,447 | [
"Apache-2.0"
] | 101 | f69e5421bdebe9db0d993b5470dace61872f90df | https://github.com/gitter-badger/FlexNeuART/tree/f69e5421bdebe9db0d993b5470dace61872f90df |
NaiveGroupNorm | from torch.nn import Module
import torch
from torch.nn import Parameter
from torch.nn import init
import torch.nn.parallel
class NaiveGroupNorm(Module):
"""NaiveGroupNorm implements Group Normalization with the high-level matrix operations in PyTorch.
It is a temporary solution to export GN by ONNX before the... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch.nn import Module
from torch.nn import Parameter
from torch.nn import... | gist-ailab/uoais | NaiveGroupNorm | false | 15,448 | [
"BSD-2-Clause"
] | 52 | fb42d9a96cd54daad61c956d8d9d65dd0ebef4c7 | https://github.com/gist-ailab/uoais/tree/fb42d9a96cd54daad61c956d8d9d65dd0ebef4c7 |
FocalLoss | import torch
import torch.nn as nn
class FocalLoss(nn.Module):
"""
Softmax and sigmoid focal loss.
copy from https://github.com/lonePatient/TorchBlocks
"""
def __init__(self, num_labels, activation_type='softmax', gamma=2.0,
alpha=0.25, epsilon=1e-09):
super(FocalLoss, self).__ini... | 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
... | gitabtion/BertBasedCscModels | FocalLoss | false | 15,449 | [
"Apache-2.0"
] | 158 | 1daf505d109c5922eeedb6674edbb1b73db21e45 | https://github.com/gitabtion/BertBasedCscModels/tree/1daf505d109c5922eeedb6674edbb1b73db21e45 |
LinearClassifier | import logging
import random
import torch
import torch.nn.functional as F
import torch.nn as nn
from typing import List
import torch.onnx.operators
from functools import wraps
def singleton(cls):
"""
Singleton decorator
Args:
cls: singleton class
Returns:
- an instance of a singleton... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | godweiyang/ParaGen | LinearClassifier | false | 15,450 | [
"Apache-2.0"
] | 50 | 9665d1244ea38a41fc06b4e0a7f6411985e2221f | https://github.com/godweiyang/ParaGen/tree/9665d1244ea38a41fc06b4e0a7f6411985e2221f |
ResidualConvUnit | import torch
from torch import nn
class ResidualConvUnit(nn.Module):
"""Residual convolution module.
"""
def __init__(self, features):
"""Init.
Args:
features (int): number of features
"""
super().__init__()
self.conv1 = nn.Conv2d(features, features, ke... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | google/dynamic-video-depth | ResidualConvUnit | false | 15,451 | [
"Apache-2.0"
] | 144 | 7dab8f9e156fa35735301695ea020aee7221fb31 | https://github.com/google/dynamic-video-depth/tree/7dab8f9e156fa35735301695ea020aee7221fb31 |
DownsampleB | import torch
import torch.nn
from torch import nn
class DownsampleB(nn.Module):
def __init__(self, nIn, nOut, stride):
super(DownsampleB, self).__init__()
self.avg = nn.AvgPool2d(stride)
self.expand_ratio = nOut // nIn
def forward(self, x):
x = self.avg(x)
return torc... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.g... | gpleiss/aum | DownsampleB | false | 15,452 | [
"MIT"
] | 45 | 3c710662d74cdad9b299f541170070c0cb292042 | https://github.com/gpleiss/aum/tree/3c710662d74cdad9b299f541170070c0cb292042 |
Conv2dBlock | import torch
from torch import nn
class Conv2dBlock(nn.Module):
def __init__(self, input_dim, output_dim, kernel_size, stride, padding=
0, dilation=1, norm='weight', activation='relu', pad_type='zero',
use_bias=True, *args, **karg):
super(Conv2dBlock, self).__init__()
self.conv = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | google/dynamic-video-depth | Conv2dBlock | false | 15,453 | [
"Apache-2.0"
] | 144 | 7dab8f9e156fa35735301695ea020aee7221fb31 | https://github.com/google/dynamic-video-depth/tree/7dab8f9e156fa35735301695ea020aee7221fb31 |
CenterIntersection | import torch
import torch.nn as nn
import torch.nn.functional as F
class CenterIntersection(nn.Module):
def __init__(self, dim):
super(CenterIntersection, self).__init__()
self.dim = dim
self.layers = nn.Parameter(torch.zeros(self.dim * 2 + 2, self.dim))
nn.init.xavier_uniform_(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.... | google-research/smore | CenterIntersection | false | 15,454 | [
"Apache-2.0"
] | 78 | e4ba95a7466ef7d018987bce7688b77bf2ea7e4f | https://github.com/google-research/smore/tree/e4ba95a7466ef7d018987bce7688b77bf2ea7e4f |
ConvLayer | import torch
class ConvLayer(torch.nn.Module):
"""
A small wrapper around nn.Conv2d, so as to make the code cleaner and allow for experimentation with padding
"""
def __init__(self, in_channels, out_channels, kernel_size, stride):
super().__init__()
self.conv2d = torch.nn.Conv2d(i... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
assert_size_s... | gordicaleksa/pytorch-nst-feedforward | ConvLayer | false | 15,455 | [
"MIT"
] | 50 | 00c96e8e3f1b0b7fb4c14254fd0c6f1281a29598 | https://github.com/gordicaleksa/pytorch-nst-feedforward/tree/00c96e8e3f1b0b7fb4c14254fd0c6f1281a29598 |
RingLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from torch.nn.modules.loss import CrossEntropyLoss
class RingLoss(nn.Module):
def __init__(self, type='auto', loss_weight=1.0, softmax_loss_weight=1.0):
"""
:param type: type of loss ('l1',... | 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... | gorogoroyasu/mlcomp | RingLoss | false | 15,456 | [
"Apache-2.0"
] | 166 | fc6572ca5b226b35df97f13badd4420b30468a3b | https://github.com/gorogoroyasu/mlcomp/tree/fc6572ca5b226b35df97f13badd4420b30468a3b |
ClipL1 | import torch
import torch.nn as nn
class ClipL1(nn.Module):
""" Clip L1 loss
From: https://github.com/HolmesShuan/AIM2020-Real-Super-Resolution/
ClipL1 Loss combines Clip function and L1 loss. self.clip_min sets the
gradients of well-trained pixels to zeros and clip_max works as a noise filter.
da... | 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
... | grofit/traiNNer | ClipL1 | false | 15,457 | [
"Apache-2.0"
] | 78 | 12d006fd44ed304e4178839c53b1f3d95ca25dcb | https://github.com/grofit/traiNNer/tree/12d006fd44ed304e4178839c53b1f3d95ca25dcb |
CharbonnierLoss | import torch
import torch.nn as nn
def get_outnorm(x: 'torch.Tensor', out_norm: 'str'='') ->torch.Tensor:
""" Common function to get a loss normalization value. Can
normalize by either the batch size ('b'), the number of
channels ('c'), the image size ('i') or combinations
('bi', 'bci', et... | 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... | grofit/traiNNer | CharbonnierLoss | false | 15,458 | [
"Apache-2.0"
] | 78 | 12d006fd44ed304e4178839c53b1f3d95ca25dcb | https://github.com/grofit/traiNNer/tree/12d006fd44ed304e4178839c53b1f3d95ca25dcb |
GramMatrix | import torch
import torch.nn as nn
def get_outnorm(x: 'torch.Tensor', out_norm: 'str'='') ->torch.Tensor:
""" Common function to get a loss normalization value. Can
normalize by either the batch size ('b'), the number of
channels ('c'), the image size ('i') or combinations
('bi', 'bci', et... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | grofit/traiNNer | GramMatrix | false | 15,459 | [
"Apache-2.0"
] | 78 | 12d006fd44ed304e4178839c53b1f3d95ca25dcb | https://github.com/grofit/traiNNer/tree/12d006fd44ed304e4178839c53b1f3d95ca25dcb |
BoxOffsetIntersection | import torch
import torch.nn as nn
import torch.nn.functional as F
class BoxOffsetIntersection(nn.Module):
def __init__(self, dim):
super(BoxOffsetIntersection, self).__init__()
self.dim = dim
self.layers = nn.Parameter(torch.zeros(self.dim * 2 + 2, self.dim))
nn.init.xavier_unifo... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | google-research/smore | BoxOffsetIntersection | false | 15,460 | [
"Apache-2.0"
] | 78 | e4ba95a7466ef7d018987bce7688b77bf2ea7e4f | https://github.com/google-research/smore/tree/e4ba95a7466ef7d018987bce7688b77bf2ea7e4f |
AttentionBranch | import torch
import torch.nn as nn
class AttentionBranch(nn.Module):
"""Attention Branch."""
def __init__(self, nf, k_size=3):
super(AttentionBranch, self).__init__()
self.k1 = nn.Conv2d(nf, nf, kernel_size=k_size, padding=(k_size - 1
) // 2, bias=False)
self.lrelu = nn.Le... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | grofit/traiNNer | AttentionBranch | false | 15,461 | [
"Apache-2.0"
] | 78 | 12d006fd44ed304e4178839c53b1f3d95ca25dcb | https://github.com/grofit/traiNNer/tree/12d006fd44ed304e4178839c53b1f3d95ca25dcb |
VisErrorLossV13 | import torch
import torch.nn.functional as F
from torch import nn
class VisErrorLossV13(nn.Module):
def __init__(self):
super(VisErrorLossV13, self).__init__()
def compute_l1_weighted_loss(self, hm_targets, hm_preds, vismap, ohem=1.0):
"""
:param hm_targets: [batch size, keypoint num... | 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.functi... | gathierry/FashionAI-KeyPointsDetectionOfApparel | VisErrorLossV13 | false | 15,462 | [
"Apache-2.0"
] | 174 | 2e0942b42b4a9cd974cdddc151675738dc8a8cb4 | https://github.com/gathierry/FashionAI-KeyPointsDetectionOfApparel/tree/2e0942b42b4a9cd974cdddc151675738dc8a8cb4 |
DistmultCenterSet | import torch
import torch.nn as nn
import torch.nn.functional as F
class DistmultCenterSet(nn.Module):
def __init__(self, dim, aggr=torch.max, nonlinear=True):
super(DistmultCenterSet, self).__init__()
self.dim = dim
self.layers = nn.Parameter(torch.zeros(self.dim * 4 + 4, 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
import torch.nn as nn
assert_... | google-research/smore | DistmultCenterSet | false | 15,463 | [
"Apache-2.0"
] | 78 | e4ba95a7466ef7d018987bce7688b77bf2ea7e4f | https://github.com/google-research/smore/tree/e4ba95a7466ef7d018987bce7688b77bf2ea7e4f |
AngleSimpleLinear | import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.nn import Parameter
class AngleSimpleLinear(nn.Module):
"""Computes cos of angles between input vectors and weights vectors"""
def __init__(self, in_features, out_features):
super(AngleSimpleLinear, 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
from torch._inductor.runtime.... | grib0ed0v/face_recognition.pytorch | AngleSimpleLinear | false | 15,464 | [
"Apache-2.0"
] | 158 | 05cb9b30e8220445fcb27988926d88f330091c12 | https://github.com/grib0ed0v/face_recognition.pytorch/tree/05cb9b30e8220445fcb27988926d88f330091c12 |
ConvBlock | import torch
class ConvBlock(torch.nn.Module):
def __init__(self, input_size, output_size, kernel_size, stride,
padding, bias=True):
super(ConvBlock, self).__init__()
self.conv = torch.nn.Conv2d(input_size, output_size, kernel_size,
stride, padding, bias=bias)
self.act... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cu... | grofit/traiNNer | ConvBlock | false | 15,465 | [
"Apache-2.0"
] | 78 | 12d006fd44ed304e4178839c53b1f3d95ca25dcb | https://github.com/grofit/traiNNer/tree/12d006fd44ed304e4178839c53b1f3d95ca25dcb |
CenterLoss | import torch
import torch.nn.functional as F
import torch.nn as nn
class CenterLoss(nn.Module):
"""Implements the Center loss from https://ydwen.github.io/papers/WenECCV16.pdf"""
def __init__(self, num_classes, embed_size, cos_dist=True):
super().__init__()
self.cos_dist = cos_dist
se... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | grib0ed0v/face_recognition.pytorch | CenterLoss | false | 15,466 | [
"Apache-2.0"
] | 158 | 05cb9b30e8220445fcb27988926d88f330091c12 | https://github.com/grib0ed0v/face_recognition.pytorch/tree/05cb9b30e8220445fcb27988926d88f330091c12 |
L1CosineSim | import torch
import torch.nn as nn
class L1CosineSim(nn.Module):
""" L1 loss with Cosine similarity.
Can be used to replace L1 pixel loss, but includes a cosine similarity term
to ensure color correctness of the RGB vectors of each pixel.
lambda is a constant factor that adjusts the contribution of th... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | grofit/traiNNer | L1CosineSim | false | 15,467 | [
"Apache-2.0"
] | 78 | 12d006fd44ed304e4178839c53b1f3d95ca25dcb | https://github.com/grofit/traiNNer/tree/12d006fd44ed304e4178839c53b1f3d95ca25dcb |
PA | import torch
import torch.nn as nn
class PA(nn.Module):
"""PA is pixel attention"""
def __init__(self, nf):
super(PA, self).__init__()
self.conv = nn.Conv2d(nf, nf, 1)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
y = self.conv(x)
y = self.sigmoid(y)
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | grofit/traiNNer | PA | false | 15,468 | [
"Apache-2.0"
] | 78 | 12d006fd44ed304e4178839c53b1f3d95ca25dcb | https://github.com/grofit/traiNNer/tree/12d006fd44ed304e4178839c53b1f3d95ca25dcb |
PACnv | import torch
import torch.nn as nn
class PACnv(nn.Module):
def __init__(self, nf, k_size=3):
super(PACnv, self).__init__()
self.k2 = nn.Conv2d(nf, nf, 1)
self.sigmoid = nn.Sigmoid()
self.k3 = nn.Conv2d(nf, nf, kernel_size=k_size, padding=(k_size - 1
) // 2, bias=False)... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | grofit/traiNNer | PACnv | false | 15,469 | [
"Apache-2.0"
] | 78 | 12d006fd44ed304e4178839c53b1f3d95ca25dcb | https://github.com/grofit/traiNNer/tree/12d006fd44ed304e4178839c53b1f3d95ca25dcb |
FrobeniusNormLoss | import torch
import torch.nn as nn
def get_outnorm(x: 'torch.Tensor', out_norm: 'str'='') ->torch.Tensor:
""" Common function to get a loss normalization value. Can
normalize by either the batch size ('b'), the number of
channels ('c'), the image size ('i') or combinations
('bi', 'bci', et... | 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... | grofit/traiNNer | FrobeniusNormLoss | false | 15,470 | [
"Apache-2.0"
] | 78 | 12d006fd44ed304e4178839c53b1f3d95ca25dcb | https://github.com/grofit/traiNNer/tree/12d006fd44ed304e4178839c53b1f3d95ca25dcb |
PAM_Module | from torch.nn import Module
import torch
from math import sqrt as sqrt
from itertools import product as product
from torch.nn import Conv2d
from torch.nn import Parameter
from torch.nn import Softmax
from torch.nn.modules.module import Module
class PAM_Module(Module):
""" Position attention module"""
def __i... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | gpdsec/HSD | PAM_Module | false | 15,471 | [
"MIT"
] | 58 | 8abcf78db5f313266a3bb3f85b9424927fe59a2d | https://github.com/gpdsec/HSD/tree/8abcf78db5f313266a3bb3f85b9424927fe59a2d |
OFLoss | import torch
import torch.nn as nn
def get_outnorm(x: 'torch.Tensor', out_norm: 'str'='') ->torch.Tensor:
""" Common function to get a loss normalization value. Can
normalize by either the batch size ('b'), the number of
channels ('c'), the image size ('i') or combinations
('bi', 'bci', et... | 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
... | grofit/traiNNer | OFLoss | false | 15,472 | [
"Apache-2.0"
] | 78 | 12d006fd44ed304e4178839c53b1f3d95ca25dcb | https://github.com/grofit/traiNNer/tree/12d006fd44ed304e4178839c53b1f3d95ca25dcb |
Deconvolution | import torch
import torch.nn as nn
import torch.utils.model_zoo
class Deconvolution(nn.Module):
def __init__(self, C, stride):
super(Deconvolution, self).__init__()
if stride == 2:
kernel_size = 3
output_padding = 1
elif stride == 4:
kernel_size = 5
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.model_zoo
assert_size_stride = torch._C... | guoyongcs/HNAS | Deconvolution | false | 15,473 | [
"MIT"
] | 60 | 2b34e1b637bb03d23ca6559c1b5d1245d9744348 | https://github.com/guoyongcs/HNAS/tree/2b34e1b637bb03d23ca6559c1b5d1245d9744348 |
RelativeL1 | import torch
import torch.nn as nn
class RelativeL1(nn.Module):
""" Relative L1 loss.
Comparing to the regular L1, introducing the division by |c|+epsilon
better models the human vision system’s sensitivity to variations
in the dark areas. (where epsilon = 0.01, to prevent values of 0 in the
denom... | 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
... | grofit/traiNNer | RelativeL1 | false | 15,474 | [
"Apache-2.0"
] | 78 | 12d006fd44ed304e4178839c53b1f3d95ca25dcb | https://github.com/grofit/traiNNer/tree/12d006fd44ed304e4178839c53b1f3d95ca25dcb |
ConvUpSample | import torch
import torch.nn as nn
class ConvUpSample(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
padding=0, scale_factor=2, mode='nearest'):
super(ConvUpSample, self).__init__()
self.upsample = nn.Upsample(scale_factor=scale_factor, mode=mode)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | hadonga/PMF_MOD | ConvUpSample | false | 15,475 | [
"MIT"
] | 65 | 1875be9bd019a7e8a121d92831fa3cbd557e2ca1 | https://github.com/hadonga/PMF_MOD/tree/1875be9bd019a7e8a121d92831fa3cbd557e2ca1 |
TestUpsampleNearest2d | import torch
import torch.nn as nn
import torch.nn.functional as F
class TestUpsampleNearest2d(nn.Module):
"""Module for UpsampleNearest2d conversion testing
"""
def __init__(self, inp=10, out=16, kernel_size=3, bias=True):
super(TestUpsampleNearest2d, self).__init__()
self.conv2d = nn.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... | gqgs/pytorch2keras | TestUpsampleNearest2d | false | 15,476 | [
"MIT"
] | 733 | 9cd26e9e6698e1f07e455dbb94c15ecff53fb788 | https://github.com/gqgs/pytorch2keras/tree/9cd26e9e6698e1f07e455dbb94c15ecff53fb788 |
Swish | import torch
import torch.nn as nn
def swish_func(x, beta=1.0, inplace=False):
"""
"Swish: a Self-Gated Activation Function"
Searching for Activation Functions (https://arxiv.org/abs/1710.05941)
If beta=1 applies the Sigmoid Linear Unit (SiLU) function element-wise
If beta=0, Swish becomes the sc... | 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... | grofit/traiNNer | Swish | false | 15,477 | [
"Apache-2.0"
] | 78 | 12d006fd44ed304e4178839c53b1f3d95ca25dcb | https://github.com/grofit/traiNNer/tree/12d006fd44ed304e4178839c53b1f3d95ca25dcb |
Linear | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.model_zoo
class Linear(nn.Module):
def __init__(self, stride):
super(Linear, self).__init__()
self.scale = stride
def forward(self, x):
return F.interpolate(x, scale_factor=self.scale, mode='linear'... | 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.model_zoo
assert_size_stride = torch._C._dynamo.... | guoyongcs/HNAS | Linear | false | 15,478 | [
"MIT"
] | 60 | 2b34e1b637bb03d23ca6559c1b5d1245d9744348 | https://github.com/guoyongcs/HNAS/tree/2b34e1b637bb03d23ca6559c1b5d1245d9744348 |
UpscaleBlock | import torch
import torch.nn as nn
class UpscaleBlock(nn.Module):
""" Upscaling Block using Pixel Shuffle to increase image dimensions. Used in Generator Network"""
"""
Pixel shuffle layer
(Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional
Neural Network,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | grofit/traiNNer | UpscaleBlock | false | 15,479 | [
"Apache-2.0"
] | 78 | 12d006fd44ed304e4178839c53b1f3d95ca25dcb | https://github.com/grofit/traiNNer/tree/12d006fd44ed304e4178839c53b1f3d95ca25dcb |
soft_L1 | import torch
import torch.utils.data
import torch.nn as nn
class soft_L1(nn.Module):
def __init__(self):
super(soft_L1, self).__init__()
def forward(self, input, target, eps=0.0):
ret = torch.abs(input - target) - eps
ret = torch.clamp(ret, min=0.0, max=100.0)
return ret
de... | 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.utils.dat... | haidongz-usc/Curriculum-DeepSDF | soft_L1 | false | 15,480 | [
"MIT"
] | 65 | ca216dda8edc6435139a6f657c45800791be94a7 | https://github.com/haidongz-usc/Curriculum-DeepSDF/tree/ca216dda8edc6435139a6f657c45800791be94a7 |
TVLoss | import torch
from torch.nn import functional as F
import torch.nn as nn
def get_image_gradients(image: 'torch.Tensor', step: 'int'=1):
"""Returns image gradients (dy, dx) for each color channel, using
the finite-difference approximation.
Places the gradient [ie. I(x+1,y) - I(x,y)] on the base pixel (x, y)... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import functional as F
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cud... | grofit/traiNNer | TVLoss | false | 15,481 | [
"Apache-2.0"
] | 78 | 12d006fd44ed304e4178839c53b1f3d95ca25dcb | https://github.com/grofit/traiNNer/tree/12d006fd44ed304e4178839c53b1f3d95ca25dcb |
EnergyConservingLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class EnergyConservingLoss(nn.L1Loss):
"""Energy conserving loss.
A two term loss that enforces energy conservation after
:cite:`Rethage2018`.
The loss can be described as:
.. math::
\\ell(x, y, m) = L = \\{l_1,\\dots,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 math as tl_math
import torch.nn as nn
... | hagenw/audtorch | EnergyConservingLoss | false | 15,482 | [
"MIT"
] | 81 | d82ae7f7f8c7edb7b7180b83442224e9a68483bd | https://github.com/hagenw/audtorch/tree/d82ae7f7f8c7edb7b7180b83442224e9a68483bd |
minibatch_std_concat_layer | import copy
import torch
import torch.nn as nn
def mean(tensor, dim=None, keepdim=False):
if dim is None:
return torch.mean(tensor)
else:
if isinstance(dim, int):
dim = [dim]
dim = sorted(dim)
for d in dim:
tensor = tensor.mean(dim=d, keepdim=True)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | grofit/traiNNer | minibatch_std_concat_layer | false | 15,483 | [
"Apache-2.0"
] | 78 | 12d006fd44ed304e4178839c53b1f3d95ca25dcb | https://github.com/grofit/traiNNer/tree/12d006fd44ed304e4178839c53b1f3d95ca25dcb |
AdMSoftmaxLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class AdMSoftmaxLoss(nn.Module):
def __init__(self, in_features, out_features, s=30.0, m=0.4):
"""
AM Softmax Loss
"""
super(AdMSoftmaxLoss, self).__init__()
self.s = s
self.m = m
self.in_fe... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | gcambara/s3prl | AdMSoftmaxLoss | false | 15,484 | [
"MIT"
] | 856 | 33284ebde3a903ed8604d6dae85669d0174ae1d3 | https://github.com/gcambara/s3prl/tree/33284ebde3a903ed8604d6dae85669d0174ae1d3 |
Nullifier | import torch
import torch.nn as nn
class Nullifier(nn.Container):
def __init__(self):
super(Nullifier, self).__init__()
def forward(self, inTensor):
outTensor = inTensor.clone()
outTensor.fill_(0.0)
return outTensor
def get_inputs():
return [torch.rand([4, 4, 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | haoruilee/DeepSets | Nullifier | false | 15,485 | [
"Apache-2.0"
] | 213 | b405dd6b51a34fb1ef622e25e6685b417b7b7cbb | https://github.com/haoruilee/DeepSets/tree/b405dd6b51a34fb1ef622e25e6685b417b7b7cbb |
MMTM | import torch
import torch.nn as nn
def init_weights(m):
None
if type(m) == nn.Linear:
None
else:
None
class MMTM(nn.Module):
def __init__(self, dim_visual, dim_skeleton, ratio):
super(MMTM, self).__init__()
dim = dim_visual + dim_skeleton
dim_out = int(2 * di... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | haamoon/mmtm | MMTM | false | 15,486 | [
"MIT"
] | 70 | 1c81cfefad5532cfb39193b8af3840ac3346e897 | https://github.com/haamoon/mmtm/tree/1c81cfefad5532cfb39193b8af3840ac3346e897 |
MaskedInstanceNorm1d | import torch
import torch.cuda
from torch import nn
import torch.distributed
import torch.utils.data
import torch.optim
class MaskedInstanceNorm1d(nn.Module):
"""Instance norm + masking."""
MAX_CNT = 100000.0
def __init__(self, d_channel: 'int', unbiased: 'bool'=True, affine:
'bool'=False):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.cuda
from torch... | hamjam/NeMo | MaskedInstanceNorm1d | false | 15,487 | [
"Apache-2.0"
] | 4,145 | b3484d32e1317666151f931bfa39867d88ed8658 | https://github.com/hamjam/NeMo/tree/b3484d32e1317666151f931bfa39867d88ed8658 |
ConvGLU | import torch
import torch.cuda
from torch import nn
import torch.distributed
import torch.utils.data
import torch.optim
def str2act(txt):
"""Translates text to neural network activation"""
return {'sigmoid': nn.Sigmoid(), 'relu': nn.ReLU(), 'none': nn.
Sequential(), 'lrelu': nn.LeakyReLU(0.2), 'selu':... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.cuda
from torch import nn
import torch.distributed
import torch.uti... | hamjam/NeMo | ConvGLU | false | 15,488 | [
"Apache-2.0"
] | 4,145 | b3484d32e1317666151f931bfa39867d88ed8658 | https://github.com/hamjam/NeMo/tree/b3484d32e1317666151f931bfa39867d88ed8658 |
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