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 |
|---|---|---|---|---|---|---|---|---|---|---|
FTest | import torch
import torch.nn as nn
class FTest(nn.Module):
def __init__(self):
super(FTest, self).__init__()
def forward(self, x, y):
x = x - y - 8.3
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4]), 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... | goldbattle/onnx2keras | FTest | false | 12,461 | [
"MIT"
] | 0 | dcf52041299ce4216552d1132ec86eb4debd5303 | https://github.com/goldbattle/onnx2keras/tree/dcf52041299ce4216552d1132ec86eb4debd5303 |
Linear | from torch.nn import Module
import torch
import torch.nn.functional as F
from torch.nn.modules.module import Module
from torch.nn.parameter import Parameter
import torch.nn.modules.loss
class Linear(Module):
"""
to embedding feature
"""
def __init__(self, in_features, out_features, dropout=0.0, act=F... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn import Module
i... | goodman1204/CAN-pytorch | Linear | false | 12,462 | [
"MIT"
] | 0 | 73d9486c93dd069101c750f94a0750fff0500abb | https://github.com/goodman1204/CAN-pytorch/tree/73d9486c93dd069101c750f94a0750fff0500abb |
FTanhTest | import torch
import torch.nn as nn
class FTanhTest(nn.Module):
"""
Test for nn.functional types
"""
def __init__(self):
super(FTanhTest, self).__init__()
def forward(self, x):
from torch.nn import functional as F
return F.tanh(x)
def get_inputs():
return [torch.rand... | 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_... | goldbattle/onnx2keras | FTanhTest | false | 12,463 | [
"MIT"
] | 0 | dcf52041299ce4216552d1132ec86eb4debd5303 | https://github.com/goldbattle/onnx2keras/tree/dcf52041299ce4216552d1132ec86eb4debd5303 |
FSELUTest | import torch
import torch.nn as nn
class FSELUTest(nn.Module):
"""
Test for nn.functional types
"""
def __init__(self):
super(FSELUTest, self).__init__()
def forward(self, x):
from torch.nn import functional as F
return F.selu(x)
def get_inputs():
return [torch.rand... | 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_... | goldbattle/onnx2keras | FSELUTest | false | 12,464 | [
"MIT"
] | 0 | dcf52041299ce4216552d1132ec86eb4debd5303 | https://github.com/goldbattle/onnx2keras/tree/dcf52041299ce4216552d1132ec86eb4debd5303 |
HouseHolderFlow | import torch
import torch.utils.data
import torch.nn as nn
class HouseHolderFlow(nn.Module):
def forward(self, v, z):
"""
:param v: batch_size (B) x latent_size (L)
:param z: batch_size (B) x latent_size (L)
:return: z_new = z - 2* v v_T / norm(v,2) * z
"""
vvT = t... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dyn... | gpoesia/variational-item-response-theory-public | HouseHolderFlow | false | 12,465 | [
"MIT"
] | 0 | 6a0db81068695422dddec8832ce353879c5acb82 | https://github.com/gpoesia/variational-item-response-theory-public/tree/6a0db81068695422dddec8832ce353879c5acb82 |
Critic | import torch
import torch.nn as nn
import torch.nn.functional as F
class Critic(nn.Module):
def __init__(self, hidden_size, num_inputs, action_space):
super(Critic, self).__init__()
self.action_space = action_space
num_outputs = action_space.shape[0]
self.linear1 = nn.Linear(num_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.... | gntoni/pytorch-ddpg-naf | Critic | false | 12,466 | [
"MIT"
] | 0 | d208d0c0c38a9d2d2041f1e7e95695359eba430e | https://github.com/gntoni/pytorch-ddpg-naf/tree/d208d0c0c38a9d2d2041f1e7e95695359eba430e |
ItemInferenceNetwork | import torch
import torch.utils.data
import torch.nn as nn
class ItemInferenceNetwork(nn.Module):
def __init__(self, num_item, item_feat_dim):
super().__init__()
self.mu_lookup = nn.Embedding(num_item, item_feat_dim)
self.logvar_lookup = nn.Embedding(num_item, item_feat_dim)
def forw... | 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | gpoesia/variational-item-response-theory-public | ItemInferenceNetwork | false | 12,467 | [
"MIT"
] | 0 | 6a0db81068695422dddec8832ce353879c5acb82 | https://github.com/gpoesia/variational-item-response-theory-public/tree/6a0db81068695422dddec8832ce353879c5acb82 |
KLDivergence | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.optim
def kl_divergence(y, target, mask=None, reduce=True):
loss = (target * torch.log(target) - target * F.log_softmax(y, 1)).sum(1)
if mask is not None:
loss = mask * loss
if reduce:
return loss.mean()
el... | 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... | gsaiabhishek/AUTOMATA | KLDivergence | false | 12,468 | [
"MIT"
] | 0 | e944992a7bf3a50bc8951a303294b3a798822176 | https://github.com/gsaiabhishek/AUTOMATA/tree/e944992a7bf3a50bc8951a303294b3a798822176 |
CrossEntropy | import torch
from torch.nn.functional import cross_entropy
import torch.nn as nn
import torch.optim
class CrossEntropy(nn.Module):
def __init__(self, reduce):
super().__init__()
self.reduce = reduce
def forward(self, y, target, mask=None, *args, **kwargs):
return cross_entropy(y, tar... | 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
... | gsaiabhishek/AUTOMATA | CrossEntropy | false | 12,469 | [
"MIT"
] | 0 | e944992a7bf3a50bc8951a303294b3a798822176 | https://github.com/gsaiabhishek/AUTOMATA/tree/e944992a7bf3a50bc8951a303294b3a798822176 |
Copy | import torch
from torch import nn
class Copy(nn.Module):
def __init__(self, hidden_size, copy_weight=1.0):
"""Calculate copy attention"""
super().__init__()
self.Wcopy = nn.Linear(hidden_size, hidden_size)
self.copy_weight = copy_weight
def forward(self, enc_out_hs, dec_hs):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | gusalsdmlwlq/DAMD | Copy | false | 12,470 | [
"Apache-2.0"
] | 0 | e98feaf5d9f251132e655bbc5fdb2c080cbed90e | https://github.com/gusalsdmlwlq/DAMD/tree/e98feaf5d9f251132e655bbc5fdb2c080cbed90e |
Actor | import torch
import torch.nn as nn
import torch.nn.functional as F
class Actor(nn.Module):
def __init__(self, hidden_size, num_inputs, action_space):
super(Actor, self).__init__()
self.action_space = action_space
num_outputs = action_space.shape[0]
self.linear1 = nn.Linear(num_inp... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | gntoni/pytorch-ddpg-naf | Actor | false | 12,471 | [
"MIT"
] | 0 | d208d0c0c38a9d2d2041f1e7e95695359eba430e | https://github.com/gntoni/pytorch-ddpg-naf/tree/d208d0c0c38a9d2d2041f1e7e95695359eba430e |
Baseline | import torch
import torch.utils
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
class Baseline(nn.Module):
"""
Baseline network
"""
@staticmethod
def weight_init(m):
if isinstance(m, nn.Linear):
init.kaiming_normal_(m.wei... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils
import tor... | dikers/DeepHyper | Baseline | false | 12,472 | [
"Apache-2.0"
] | 0 | 827a8f3077e18b71cf448a2e56e49670428b1bfd | https://github.com/dikers/DeepHyper/tree/827a8f3077e18b71cf448a2e56e49670428b1bfd |
Network | import torch
import torch.nn.functional as F
from torch import nn
class Network(nn.Module):
"""Actor (Policy) Model."""
def __init__(self, state_size, action_size, seed, fc1_units=128,
fc2_units=128):
"""Initialize parameters and build model.
Params
======
state_si... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | gray-li/HalfRainbowDQN | Network | false | 12,473 | [
"MIT"
] | 0 | 43e2b12945c14e0e39eea3bbf56c7af785c48720 | https://github.com/gray-li/HalfRainbowDQN/tree/43e2b12945c14e0e39eea3bbf56c7af785c48720 |
Attn | import torch
import torch.nn.functional as F
from torch import nn
class Attn(nn.Module):
def __init__(self, hidden_size):
super().__init__()
self.hidden_size = hidden_size
self.attn = nn.Linear(self.hidden_size * 2, hidden_size)
self.v = nn.Linear(hidden_size, 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.... | gusalsdmlwlq/DAMD | Attn | false | 12,474 | [
"Apache-2.0"
] | 0 | e98feaf5d9f251132e655bbc5fdb2c080cbed90e | https://github.com/gusalsdmlwlq/DAMD/tree/e98feaf5d9f251132e655bbc5fdb2c080cbed90e |
MedianPool2d | import torch
import torch.optim
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.modules.utils import _pair
from torch.nn.modules.utils import _quadruple
class MedianPool2d(nn.Module):
""" Median pool (usable as median filter when stride=1) module.
Args:
kernel_size: size of pooli... | 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.optim
import torch.nn as nn
from torch.nn.modules.utils impo... | guzor/rgdb-semantic-segmentation | MedianPool2d | false | 12,475 | [
"MIT"
] | 0 | d9f3d8f1b2cb7357f64914bb873513dd16fad6df | https://github.com/guzor/rgdb-semantic-segmentation/tree/d9f3d8f1b2cb7357f64914bb873513dd16fad6df |
PlanarFlow | import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
class PlanarFlow(nn.Module):
"""Planar normalizing flow [Rezende & Mohamed 2015].
Provides a tighter bound on the ELBO by giving more expressive
power to the approximate distribution, such as by introducing
cova... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.utils.data
import torch.nn as nn
assert_size_stri... | gpoesia/variational-item-response-theory-public | PlanarFlow | false | 12,476 | [
"MIT"
] | 0 | 6a0db81068695422dddec8832ce353879c5acb82 | https://github.com/gpoesia/variational-item-response-theory-public/tree/6a0db81068695422dddec8832ce353879c5acb82 |
CharbonnierLoss | import functools
import torch
import torch.nn as nn
from torch.nn import functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Returns:
Tensor: Reduced lo... | 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 functools
import torc... | hejm37/mmediting | CharbonnierLoss | false | 12,477 | [
"Apache-2.0"
] | 0 | d4086aaf8a36ae830f1714aad585900d24ad1156 | https://github.com/hejm37/mmediting/tree/d4086aaf8a36ae830f1714aad585900d24ad1156 |
Conv2dWithConstraint | import torch
from torch import nn
class Conv2dWithConstraint(nn.Conv2d):
def __init__(self, *args, max_norm=1, **kwargs):
self.max_norm = max_norm
super(Conv2dWithConstraint, self).__init__(*args, **kwargs)
def forward(self, x):
self.weight.data = torch.renorm(self.weight.data, p=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.triton_helpers import libdevice
from torch import n... | gzoumpourlis/braindecode | Conv2dWithConstraint | false | 12,478 | [
"BSD-3-Clause"
] | 0 | 6bd595a146d0854541ff02b4483c011a394fdf0a | https://github.com/gzoumpourlis/braindecode/tree/6bd595a146d0854541ff02b4483c011a394fdf0a |
MeanSquared | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.optim
def mean_squared(y, target, mask=None, reduce=True):
y = y.softmax(1)
loss = F.mse_loss(y, target, reduction='none').mean(1)
if mask is not None:
loss = mask * loss
if reduce:
return loss.mean()
e... | 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... | gsaiabhishek/AUTOMATA | MeanSquared | false | 12,479 | [
"MIT"
] | 0 | e944992a7bf3a50bc8951a303294b3a798822176 | https://github.com/gsaiabhishek/AUTOMATA/tree/e944992a7bf3a50bc8951a303294b3a798822176 |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.fc1 = nn.Linear(28 * 28, 64)
self.fc2 = nn.Linear(64, 24)
self.fc3 = nn.Linear(24, 10)
def forward(self, x):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | graciofilipe/deep-learning-v2-pytorch | Net | false | 12,480 | [
"MIT"
] | 0 | b1aa2189c99ecd1b79deb6c499bae9d1fa52fa19 | https://github.com/graciofilipe/deep-learning-v2-pytorch/tree/b1aa2189c99ecd1b79deb6c499bae9d1fa52fa19 |
DiscShiftLoss | import torch
import torch.nn as nn
class DiscShiftLoss(nn.Module):
"""Disc shift loss.
Args:
loss_weight (float, optional): Loss weight. Defaults to 1.0.
"""
def __init__(self, loss_weight=0.1):
super(DiscShiftLoss, self).__init__()
self.loss_weight = loss_weight
... | 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... | hejm37/mmediting | DiscShiftLoss | false | 12,481 | [
"Apache-2.0"
] | 0 | d4086aaf8a36ae830f1714aad585900d24ad1156 | https://github.com/hejm37/mmediting/tree/d4086aaf8a36ae830f1714aad585900d24ad1156 |
TwoLayerNet | import torch
import torch.quantization
import torch.onnx
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class TwoLayerNet(torch.nn.Module):
def __init__(self, D_in, H, D_out):
"""
In the constructor we instantiate two nn.Linear modules and ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.quantization
imp... | harrydrippin/tutorials | TwoLayerNet | false | 12,482 | [
"BSD-3-Clause"
] | 0 | a8def2dfd44b4b8e22c36a3e4470f37b59ebedfb | https://github.com/harrydrippin/tutorials/tree/a8def2dfd44b4b8e22c36a3e4470f37b59ebedfb |
CharbonnierCompLoss | import functools
import torch
import torch.nn as nn
from torch.nn import functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Returns:
Tensor: Reduced lo... | 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 functools
import torc... | hejm37/mmediting | CharbonnierCompLoss | false | 12,483 | [
"Apache-2.0"
] | 0 | d4086aaf8a36ae830f1714aad585900d24ad1156 | https://github.com/hejm37/mmediting/tree/d4086aaf8a36ae830f1714aad585900d24ad1156 |
SoftCrossEntropyLoss2d | import torch
import torch.nn as nn
import torch.nn.functional as F
class SoftCrossEntropyLoss2d(nn.Module):
def __init__(self):
super(SoftCrossEntropyLoss2d, self).__init__()
def forward(self, inputs, targets):
loss = 0
inputs = -F.log_softmax(inputs, dim=1)
for index in rang... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | hainguyen15/GLNet | SoftCrossEntropyLoss2d | false | 12,484 | [
"MIT"
] | 0 | dc5d2d000a37e9415f742ed04b7e99973a068279 | https://github.com/hainguyen15/GLNet/tree/dc5d2d000a37e9415f742ed04b7e99973a068279 |
L1CompositionLoss | import functools
import torch
import torch.nn as nn
from torch.nn import functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Returns:
Tensor: Reduced lo... | 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 functools
impor... | hejm37/mmediting | L1CompositionLoss | false | 12,485 | [
"Apache-2.0"
] | 0 | d4086aaf8a36ae830f1714aad585900d24ad1156 | https://github.com/hejm37/mmediting/tree/d4086aaf8a36ae830f1714aad585900d24ad1156 |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self, _input_size: 'int', _output_size: 'int',
_hidden_layers: 'int', _hidden_size: 'int'):
super(Net, self).__init__()
self.input = nn.Linear(_input_size, _hidden_size)
self.hidd... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | hantonelli/AprendizajePorRefuerzos | Net | false | 12,486 | [
"MIT"
] | 0 | eeffa4aa36fa5c14739206e4c4bd0a1bd76f6af1 | https://github.com/hantonelli/AprendizajePorRefuerzos/tree/eeffa4aa36fa5c14739206e4c4bd0a1bd76f6af1 |
PlainRefiner | import torch
import torch.nn as nn
class PlainRefiner(nn.Module):
"""Simple refiner from Deep Image Matting.
Args:
conv_channels (int): Number of channels produced by the three main
convolutional layer.
loss_refine (dict): Config of the loss of the refiner. Default: 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
import torch.nn as nn
assert_... | hejm37/mmediting | PlainRefiner | false | 12,487 | [
"Apache-2.0"
] | 0 | d4086aaf8a36ae830f1714aad585900d24ad1156 | https://github.com/hejm37/mmediting/tree/d4086aaf8a36ae830f1714aad585900d24ad1156 |
VarianceNorm2d | import torch
import torch.nn as nn
class VarianceNorm2d(nn.Module):
def __init__(self, num_features, bias=False):
super().__init__()
self.num_features = num_features
self.bias = bias
self.alpha = nn.Parameter(torch.zeros(num_features))
self.alpha.data.normal_(1, 0.02)
... | 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_... | henryaddison/score_sde_pytorch | VarianceNorm2d | false | 12,488 | [
"Apache-2.0"
] | 0 | be07c3a3346bf8ceadabf6a3b436db5d5c3d0252 | https://github.com/henryaddison/score_sde_pytorch/tree/be07c3a3346bf8ceadabf6a3b436db5d5c3d0252 |
GRelu | import torch
import torch.nn as nn
import torch.nn.functional as F
class GRelu(nn.Module):
"""Generic ReLU."""
def __init__(self, leak=0.0, max=float('inf'), sub=0.0):
super().__init__()
self.leak = leak
self.max = max
self.sub = sub
def forward(self, x):
"""Check... | 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... | hdmamin/ml_htools | GRelu | false | 12,489 | [
"MIT"
] | 0 | 9b8e8fbb561c4ae7c6ee282c8b5fc7876935dd50 | https://github.com/hdmamin/ml_htools/tree/9b8e8fbb561c4ae7c6ee282c8b5fc7876935dd50 |
MeanPoolConv | import torch
import torch.nn as nn
class MeanPoolConv(nn.Module):
def __init__(self, input_dim, output_dim, kernel_size=3, biases=True):
super().__init__()
self.conv = nn.Conv2d(input_dim, output_dim, kernel_size, stride=1,
padding=kernel_size // 2, bias=biases)
def forward(self,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | henryaddison/score_sde_pytorch | MeanPoolConv | false | 12,490 | [
"Apache-2.0"
] | 0 | be07c3a3346bf8ceadabf6a3b436db5d5c3d0252 | https://github.com/henryaddison/score_sde_pytorch/tree/be07c3a3346bf8ceadabf6a3b436db5d5c3d0252 |
Mnist_CNN | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.quantization
import torch.onnx
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class Mnist_CNN(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = 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
import ... | harrydrippin/tutorials | Mnist_CNN | false | 12,491 | [
"BSD-3-Clause"
] | 0 | a8def2dfd44b4b8e22c36a3e4470f37b59ebedfb | https://github.com/harrydrippin/tutorials/tree/a8def2dfd44b4b8e22c36a3e4470f37b59ebedfb |
AttentionPool2d | import torch
import torch.nn.functional as F
from torch import nn
class AttentionPool2d(nn.Module):
def __init__(self, spacial_dim: 'int', embed_dim: 'int', num_heads:
'int', output_dim: 'int'=None):
super().__init__()
self.positional_embedding = nn.Parameter(torch.randn(spacial_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.... | graceduansu/CLIP | AttentionPool2d | false | 12,492 | [
"MIT"
] | 0 | 14605e2118f43312cc00bf549aec388f5ddf802b | https://github.com/graceduansu/CLIP/tree/14605e2118f43312cc00bf549aec388f5ddf802b |
QREmbeddingBag | import torch
import numpy as np
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.functional as F
from torch.nn.parameter import Parameter
class QREmbeddingBag(nn.Module):
"""Computes sums or means over two 'bags' of embed... | 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 numpy as np
import torch.nn as nn
import torch.nn.parallel
import torch.... | hekaplex/resnet_dl | QREmbeddingBag | false | 12,493 | [
"Apache-2.0"
] | 0 | fc8d4dcc0adffbe22d01d333e6cf5db955f2f011 | https://github.com/hekaplex/resnet_dl/tree/fc8d4dcc0adffbe22d01d333e6cf5db955f2f011 |
SRCNN | import logging
import torch
import torch.nn as nn
def get_root_logger(log_file=None, log_level=logging.INFO):
"""Get the root logger.
The logger will be initialized if it has not been initialized. By default a
StreamHandler will be added. If `log_file` is specified, a FileHandler will
also be added. ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | hejm37/mmediting | SRCNN | false | 12,494 | [
"Apache-2.0"
] | 0 | d4086aaf8a36ae830f1714aad585900d24ad1156 | https://github.com/hejm37/mmediting/tree/d4086aaf8a36ae830f1714aad585900d24ad1156 |
MLP | import torch
from torch import nn
import torch.nn.functional as F
from torch.utils.data import *
class MLP(nn.Module):
def __init__(self):
super(MLP, self).__init__()
self.fc1 = nn.Linear(784, 512)
self.fc2 = nn.Linear(512, 128)
self.fc3 = nn.Linear(128, 10)
def forward(self,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | heheda12345/nnfusion | MLP | false | 12,495 | [
"MIT"
] | 0 | 8cf153c1adae094fa891021bd6da70aeeee112ba | https://github.com/heheda12345/nnfusion/tree/8cf153c1adae094fa891021bd6da70aeeee112ba |
Conv2dBlock | import torch
import torch.nn.functional as F
from torch import nn
class AdaptiveInstanceNorm2d(nn.Module):
def __init__(self, num_features, eps=1e-05, momentum=0.1):
super(AdaptiveInstanceNorm2d, self).__init__()
self.num_features = num_features
self.eps = eps
self.momentum = mome... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | hendraet/research-GANwriting | Conv2dBlock | false | 12,496 | [
"MIT"
] | 0 | e62a16529db3037169d9b33ecba5735c99e73bc3 | https://github.com/hendraet/research-GANwriting/tree/e62a16529db3037169d9b33ecba5735c99e73bc3 |
MatrixConv2dResblock | import torch
import torch.nn as nn
import torch.autograd
class MatrixConv2dResblock(nn.Module):
def __init__(self, weight_shape, stride=1, padding=0, with_batchnorm=
False, act_func='ReLU'):
super(MatrixConv2dResblock, self).__init__()
self.conv = nn.Conv2d(weight_shape[3], weight_shape[0... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | hirayamy/nngen | MatrixConv2dResblock | false | 12,497 | [
"Apache-2.0"
] | 0 | 63f72be83e4bb1a697a969fb6a14d0335ec0316f | https://github.com/hirayamy/nngen/tree/63f72be83e4bb1a697a969fb6a14d0335ec0316f |
ActFirstResBlock | import torch
import torch.nn.functional as F
from torch import nn
class AdaptiveInstanceNorm2d(nn.Module):
def __init__(self, num_features, eps=1e-05, momentum=0.1):
super(AdaptiveInstanceNorm2d, self).__init__()
self.num_features = num_features
self.eps = eps
self.momentum = mome... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | hendraet/research-GANwriting | ActFirstResBlock | false | 12,498 | [
"MIT"
] | 0 | e62a16529db3037169d9b33ecba5735c99e73bc3 | https://github.com/hendraet/research-GANwriting/tree/e62a16529db3037169d9b33ecba5735c99e73bc3 |
UpsampleConv | import torch
import torch.nn as nn
class UpsampleConv(nn.Module):
def __init__(self, input_dim, output_dim, kernel_size=3, biases=True):
super().__init__()
self.conv = nn.Conv2d(input_dim, output_dim, kernel_size, stride=1,
padding=kernel_size // 2, bias=biases)
self.pixelshuf... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | henryaddison/score_sde_pytorch | UpsampleConv | false | 12,499 | [
"Apache-2.0"
] | 0 | be07c3a3346bf8ceadabf6a3b436db5d5c3d0252 | https://github.com/henryaddison/score_sde_pytorch/tree/be07c3a3346bf8ceadabf6a3b436db5d5c3d0252 |
InstanceNorm2dPlus | import torch
import torch.nn as nn
class InstanceNorm2dPlus(nn.Module):
def __init__(self, num_features, bias=True):
super().__init__()
self.num_features = num_features
self.bias = bias
self.instance_norm = nn.InstanceNorm2d(num_features, affine=False,
track_running_st... | 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_... | henryaddison/score_sde_pytorch | InstanceNorm2dPlus | false | 12,500 | [
"Apache-2.0"
] | 0 | be07c3a3346bf8ceadabf6a3b436db5d5c3d0252 | https://github.com/henryaddison/score_sde_pytorch/tree/be07c3a3346bf8ceadabf6a3b436db5d5c3d0252 |
MatrixAdd | import torch
import torch.nn as nn
import torch.autograd
class MatrixAdd(nn.Module):
def __init__(self):
super(MatrixAdd, self).__init__()
def forward(self, x, y):
z = torch.add(x, y)
return z
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def g... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.autograd
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._d... | hirayamy/nngen | MatrixAdd | false | 12,501 | [
"Apache-2.0"
] | 0 | 63f72be83e4bb1a697a969fb6a14d0335ec0316f | https://github.com/hirayamy/nngen/tree/63f72be83e4bb1a697a969fb6a14d0335ec0316f |
MSECompositionLoss | import functools
import torch
import torch.nn as nn
from torch.nn import functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Returns:
Tensor: Reduced lo... | 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 functools
import torch.nn as nn
from torch.nn import functional as F
assert_size_s... | hejm37/mmediting | MSECompositionLoss | false | 12,502 | [
"Apache-2.0"
] | 0 | d4086aaf8a36ae830f1714aad585900d24ad1156 | https://github.com/hejm37/mmediting/tree/d4086aaf8a36ae830f1714aad585900d24ad1156 |
ConvMeanPool | import torch
import torch.nn as nn
class ConvMeanPool(nn.Module):
def __init__(self, input_dim, output_dim, kernel_size=3, biases=True,
adjust_padding=False):
super().__init__()
if not adjust_padding:
conv = nn.Conv2d(input_dim, output_dim, 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | henryaddison/score_sde_pytorch | ConvMeanPool | false | 12,503 | [
"Apache-2.0"
] | 0 | be07c3a3346bf8ceadabf6a3b436db5d5c3d0252 | https://github.com/henryaddison/score_sde_pytorch/tree/be07c3a3346bf8ceadabf6a3b436db5d5c3d0252 |
MatrixConv2dMultiResblock | import torch
import torch.nn as nn
import torch.autograd
class MatrixConv2dMultiResblock(nn.Module):
def __init__(self, weight_shape, stride=1, padding=0, with_batchnorm=
False, act_func='ReLU'):
super(MatrixConv2dMultiResblock, self).__init__()
self.conv1 = nn.Conv2d(weight_shape[3], wei... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | hirayamy/nngen | MatrixConv2dMultiResblock | false | 12,504 | [
"Apache-2.0"
] | 0 | 63f72be83e4bb1a697a969fb6a14d0335ec0316f | https://github.com/hirayamy/nngen/tree/63f72be83e4bb1a697a969fb6a14d0335ec0316f |
Conv2d | from torch.autograd import Function
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
def _setup_kernel(k):
k = np.asarray(k, dtype=np.float32)
if k.ndim == 1:
k = np.outer(k, k)
k /= np.sum(k)
assert k.ndim == 2
assert k.shape[0] == k.shape[1]
retur... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.autograd import Function
import numpy as np
import torch.nn as nn
imp... | henryaddison/score_sde_pytorch | Conv2d | false | 12,505 | [
"Apache-2.0"
] | 0 | be07c3a3346bf8ceadabf6a3b436db5d5c3d0252 | https://github.com/henryaddison/score_sde_pytorch/tree/be07c3a3346bf8ceadabf6a3b436db5d5c3d0252 |
FPNSegHead | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
class FPNSegHead(nn.Module):
def __init__(self, num_in, num_mid, num_out):
super().__init__()
self.block0 = nn.Conv2d(num_in, num_mid, kernel_size=3, padding=1,
bias=False)
s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | hoangnguyen11291/Kuril-DeBlur | FPNSegHead | false | 12,506 | [
"BSD-3-Clause"
] | 0 | 7c36fc50780e3dda82eb42443d5623d34e6b02a6 | https://github.com/hoangnguyen11291/Kuril-DeBlur/tree/7c36fc50780e3dda82eb42443d5623d34e6b02a6 |
ResidualBlock | import torch
import torch.nn as nn
from functools import partial
def ncsn_conv3x3(in_planes, out_planes, stride=1, bias=True, dilation=1,
init_scale=1.0, padding=1):
"""3x3 convolution with PyTorch initialization. Same as NCSNv1/NCSNv2."""
init_scale = 1e-10 if init_scale == 0 else init_scale
conv = 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.triton_helpers import libdevice
import torch.nn as ... | henryaddison/score_sde_pytorch | ResidualBlock | false | 12,507 | [
"Apache-2.0"
] | 0 | be07c3a3346bf8ceadabf6a3b436db5d5c3d0252 | https://github.com/henryaddison/score_sde_pytorch/tree/be07c3a3346bf8ceadabf6a3b436db5d5c3d0252 |
Net | import torch
import torch.nn.functional as F
import torch.nn as nn
class Net(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(3, 16, kernel_size=3, padding=1)
self.conv2 = nn.Conv2d(16, 8, kernel_size=3, padding=1)
self.fc1 = nn.Linear(8 * 8 * 8, 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
from torch._inductor.runtime.... | hishamelreedy/Aucrobotics_QA_AutonomousInspector | Net | false | 12,508 | [
"MIT"
] | 0 | 6bad141a62827fa7a299325c69597f17b162400e | https://github.com/hishamelreedy/Aucrobotics_QA_AutonomousInspector/tree/6bad141a62827fa7a299325c69597f17b162400e |
CoordConv | import torch
import torch.nn as nn
class AddCoords(nn.Module):
def __init__(self, with_r=False):
super().__init__()
self.with_r = with_r
def forward(self, input_tensor):
"""
Args:
input_tensor: shape(batch, channel, x_dim, y_dim)
"""
batch_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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | hoseDUDEface/AdaptiveWingLoss | CoordConv | false | 12,509 | [
"Apache-2.0"
] | 0 | 9185799d87567044f437147639c3999418529684 | https://github.com/hoseDUDEface/AdaptiveWingLoss/tree/9185799d87567044f437147639c3999418529684 |
FC_Q | import torch
import torch.nn as nn
import torch.nn.functional as F
class FC_Q(nn.Module):
def __init__(self, state_dim, num_actions):
super(FC_Q, self).__init__()
self.q1 = nn.Linear(state_dim, 256)
self.q2 = nn.Linear(256, 256)
self.q3 = nn.Linear(256, num_actions)
self.i... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | hotaekjoo/SQV | FC_Q | false | 12,510 | [
"MIT"
] | 0 | d725342e7fd8548ee5fa018e5ccac4542969deed | https://github.com/hotaekjoo/SQV/tree/d725342e7fd8548ee5fa018e5ccac4542969deed |
InstanceNormLayer | import torch
import torch.nn as nn
class InstanceNormLayer(nn.Module):
"""Implements instance normalization layer."""
def __init__(self, epsilon=1e-08):
super().__init__()
self.eps = epsilon
def forward(self, x):
if len(x.shape) != 4:
raise ValueError(
... | 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_... | huaji0353/higan | InstanceNormLayer | false | 12,511 | [
"MIT"
] | 0 | a082dc2be8651725d38b8d48d7e1c7261740013d | https://github.com/huaji0353/higan/tree/a082dc2be8651725d38b8d48d7e1c7261740013d |
AddCoords | import torch
import torch.nn as nn
class AddCoords(nn.Module):
def __init__(self, with_r=False):
super().__init__()
self.with_r = with_r
def forward(self, input_tensor):
"""
Args:
input_tensor: shape(batch, channel, x_dim, y_dim)
"""
batch_size, _,... | 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... | hoseDUDEface/AdaptiveWingLoss | AddCoords | false | 12,512 | [
"Apache-2.0"
] | 0 | 9185799d87567044f437147639c3999418529684 | https://github.com/hoseDUDEface/AdaptiveWingLoss/tree/9185799d87567044f437147639c3999418529684 |
GE2ELoss | import torch
import torch.nn.functional as F
import torch.nn as nn
def calc_loss(sim_matrix):
same_idx = list(range(sim_matrix.size(0)))
pos = sim_matrix[same_idx, :, same_idx]
neg = (torch.exp(sim_matrix).sum(dim=2) + 1e-06).log_()
per_embedding_loss = -1 * (pos - neg)
loss = per_embedding_loss.s... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | helia95/SpeakerRecognition_tutorial | GE2ELoss | false | 12,513 | [
"MIT"
] | 0 | 5c00f9165fd260d50b74ab46e4d81d7cfd77ab8c | https://github.com/helia95/SpeakerRecognition_tutorial/tree/5c00f9165fd260d50b74ab46e4d81d7cfd77ab8c |
GraphConv | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class GraphConv(nn.Module):
def __init__(self, input_dim, output_dim, add_self=False,
normalize_embedding=False, dropout=0.0, bias=True):
super(GraphConv, self).__init__()
self.add_self = add_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
import torch.utils.data
assert_size_stride = torch._C._dyn... | hujilin1229/diffpool | GraphConv | false | 12,514 | [
"MIT"
] | 0 | 5b9bd73d794b63f5ea6d48e60cba090aa6e3ce72 | https://github.com/hujilin1229/diffpool/tree/5b9bd73d794b63f5ea6d48e60cba090aa6e3ce72 |
BinaryLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class BinaryLoss(nn.Module):
def __init__(self):
super(BinaryLoss, self).__init__()
def forward(self, pos_score, neg_score):
pos_loss = -F.log_softmax(pos_score)[:, 1]
neg_loss = -F.log_softmax(neg_score)[:, 0]
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | huanglianghua/mdnet-light | BinaryLoss | false | 12,515 | [
"MIT"
] | 0 | 955b61b8555a49fdf2e2310aa0756c68f955212c | https://github.com/huanglianghua/mdnet-light/tree/955b61b8555a49fdf2e2310aa0756c68f955212c |
WeightedTVLoss | import functools
import torch
from torch import nn as nn
from torch.nn import functional as F
from torch.nn import init as init
from torchvision.models import vgg as vgg
import torch.utils.data
from torch.utils import data as data
from torch import autograd as autograd
def reduce_loss(loss, reduction):
"""Reduce ... | 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 functools
from torch import nn as nn
from torch.nn import function... | hyunobae/BasicSR | WeightedTVLoss | false | 12,516 | [
"Apache-2.0"
] | 0 | f2c2fc6cf28933658816c808f55c95fa20b16483 | https://github.com/hyunobae/BasicSR/tree/f2c2fc6cf28933658816c808f55c95fa20b16483 |
PARALoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class PARALoss(nn.Module):
"""
Softmax classifier for sentence-level relation extraction.
"""
def __init__(self):
"""
Args:
sentence_encoder: encoder for sentences
num_class: number of classes
... | 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... | igorvlnascimento/open-nre | PARALoss | false | 12,517 | [
"MIT"
] | 0 | a6e42ef074d62be4d3ceb571f412d5be8c0502d7 | https://github.com/igorvlnascimento/open-nre/tree/a6e42ef074d62be4d3ceb571f412d5be8c0502d7 |
FeedForward | import torch
import torch.utils.data
import torch.nn as nn
from torch.nn.functional import relu
from torch.nn.functional import dropout
class FeedForward(nn.Module):
def __init__(self, input_size):
super(FeedForward, self).__init__()
self.fc1 = nn.Linear(input_size, 16)
self.fc2 = nn.Line... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
impor... | ibraheem-moosa/protein-bvalue-prediction | FeedForward | false | 12,518 | [
"MIT"
] | 0 | 9d0607ade30d8877ea89c5f24184d3af0580f912 | https://github.com/ibraheem-moosa/protein-bvalue-prediction/tree/9d0607ade30d8877ea89c5f24184d3af0580f912 |
SoftGate | import torch
from torch import nn as nn
from torch.nn import init as init
from torchvision.models import vgg as vgg
import torch.utils.data
from torch.utils import data as data
from torch import autograd as autograd
class SoftGate(nn.Module):
COEFF = 12.0
def forward(self, x):
return torch.sigmoid(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 import nn as nn
from torch.nn import init as init
from torchvision.models import vgg as vgg
import torch.utils.data
from torch.ut... | hyunobae/BasicSR | SoftGate | false | 12,519 | [
"Apache-2.0"
] | 0 | f2c2fc6cf28933658816c808f55c95fa20b16483 | https://github.com/hyunobae/BasicSR/tree/f2c2fc6cf28933658816c808f55c95fa20b16483 |
ResUnit | import torch
import torch.nn as nn
class ResUnit(nn.Module):
def __init__(self, ksize=3, wkdim=64):
super(ResUnit, self).__init__()
self.conv1 = nn.Conv2d(wkdim, wkdim, ksize, 1, int(ksize / 2))
self.active = nn.PReLU()
self.conv2 = nn.Conv2d(wkdim, wkdim, ksize, 1, int(ksize / 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | huang-junhong/SIRSRGAN | ResUnit | false | 12,520 | [
"Apache-2.0"
] | 0 | a774416cd45a00982141a1571cb2a8a18bb05c86 | https://github.com/huang-junhong/SIRSRGAN/tree/a774416cd45a00982141a1571cb2a8a18bb05c86 |
MultiHeadAttention | import torch
import numpy as np
class MultiHeadAttention(torch.nn.Module):
def __init__(self, input_size, output_size, num_heads,
output_attentions=False):
super(MultiHeadAttention, self).__init__()
self.output_attentions = output_attentions
self.num_heads = num_heads
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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cu... | igorvlnascimento/open-nre | MultiHeadAttention | false | 12,521 | [
"MIT"
] | 0 | a6e42ef074d62be4d3ceb571f412d5be8c0502d7 | https://github.com/igorvlnascimento/open-nre/tree/a6e42ef074d62be4d3ceb571f412d5be8c0502d7 |
TLU | import torch
from torch import nn
class TLU(nn.Module):
def __init__(self, num_features):
"""max(y, tau) = max(y - tau, 0) + tau = ReLU(y - tau) + tau"""
super(TLU, self).__init__()
self.num_features = num_features
self.tau = nn.parameter.Parameter(torch.Tensor(1, num_features, 1,... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | ildoonet/pytorch-filter-response-norm | TLU | false | 12,522 | [
"MIT"
] | 0 | e6885f2b2272fa6cde0a131d3b3a0e42b8c6d579 | https://github.com/ildoonet/pytorch-filter-response-norm/tree/e6885f2b2272fa6cde0a131d3b3a0e42b8c6d579 |
MobileBertSelfAttention | from _paritybench_helpers import _mock_config
import math
import torch
from torch import nn
import torch.utils.checkpoint
class MobileBertSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.num_attention_heads = config.num_attention_heads
self.attention_head_size... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Clemens123/transformers | MobileBertSelfAttention | false | 12,523 | [
"Apache-2.0"
] | 0 | 22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 | https://github.com/Clemens123/transformers/tree/22abe7bbc587c16ec30f9d1aa549dcbeba6e9e26 |
GANFeatLoss | import functools
import torch
from torch import nn as nn
from torch.nn import functional as F
from torch.nn import init as init
from torchvision.models import vgg as vgg
import torch.utils.data
from torch.utils import data as data
from torch import autograd as autograd
def reduce_loss(loss, reduction):
"""Reduce ... | 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 functools
from torch import nn as nn
from torch.nn import function... | hyunobae/BasicSR | GANFeatLoss | false | 12,524 | [
"Apache-2.0"
] | 0 | f2c2fc6cf28933658816c808f55c95fa20b16483 | https://github.com/hyunobae/BasicSR/tree/f2c2fc6cf28933658816c808f55c95fa20b16483 |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self, n_input, n_output):
super(Net, self).__init__()
self.fc1 = nn.Linear(n_input, 20)
self.dropout1 = nn.Dropout(0.25)
self.fc2 = nn.Linear(20, 20)
self.dropout2 = nn.Dr... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | ihsgnef/duolingo-halflife-regression | Net | false | 12,525 | [
"MIT"
] | 0 | 01c7895eee0450462b5277a055d2ae1de58f1be5 | https://github.com/ihsgnef/duolingo-halflife-regression/tree/01c7895eee0450462b5277a055d2ae1de58f1be5 |
Conv_Q | import torch
import torch.nn as nn
import torch.nn.functional as F
class Conv_Q(nn.Module):
def __init__(self, frames, num_actions):
super(Conv_Q, self).__init__()
self.c1 = nn.Conv2d(frames, 32, kernel_size=8, stride=4)
self.c2 = nn.Conv2d(32, 64, kernel_size=4, stride=2)
self.c3... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | hotaekjoo/SQV | Conv_Q | false | 12,526 | [
"MIT"
] | 0 | d725342e7fd8548ee5fa018e5ccac4542969deed | https://github.com/hotaekjoo/SQV/tree/d725342e7fd8548ee5fa018e5ccac4542969deed |
PARALossSoftmax | import torch
import torch.nn as nn
import torch.nn.functional as F
class PARALossSoftmax(nn.Module):
"""
Softmax classifier for sentence-level relation extraction.
"""
def __init__(self):
"""
Args:
sentence_encoder: encoder for sentences
num_class: number of cl... | 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
... | igorvlnascimento/open-nre | PARALossSoftmax | false | 12,527 | [
"MIT"
] | 0 | a6e42ef074d62be4d3ceb571f412d5be8c0502d7 | https://github.com/igorvlnascimento/open-nre/tree/a6e42ef074d62be4d3ceb571f412d5be8c0502d7 |
ModulatedConv2d | from torch.autograd import Function
import math
import torch
from torch import nn as nn
from torch.nn import functional as F
from torch.nn import init as init
from torchvision.models import vgg as vgg
import torch.utils.data
from torch.utils import data as data
from torch import autograd as autograd
def make_resample... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.autograd... | hyunobae/BasicSR | ModulatedConv2d | false | 12,528 | [
"Apache-2.0"
] | 0 | f2c2fc6cf28933658816c808f55c95fa20b16483 | https://github.com/hyunobae/BasicSR/tree/f2c2fc6cf28933658816c808f55c95fa20b16483 |
ToRGB | from torch.autograd import Function
import math
import torch
from torch import nn as nn
from torch.nn import functional as F
from torch.nn import init as init
from torchvision.models import vgg as vgg
import torch.utils.data
from torch.utils import data as data
from torch import autograd as autograd
def make_resample... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.autograd import Function
import math
from torch import nn as nn
from ... | hyunobae/BasicSR | ToRGB | false | 12,529 | [
"Apache-2.0"
] | 0 | f2c2fc6cf28933658816c808f55c95fa20b16483 | https://github.com/hyunobae/BasicSR/tree/f2c2fc6cf28933658816c808f55c95fa20b16483 |
ScaleNorm | import torch
from torch import nn
class ScaleNorm(nn.Module):
def __init__(self, dim, eps=1e-05):
super().__init__()
self.scale = dim ** -0.5
self.eps = eps
self.g = nn.Parameter(torch.ones(1))
def forward(self, x):
norm = torch.linalg.norm(x, 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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_... | imflash217/bumblebee | ScaleNorm | false | 12,530 | [
"MIT"
] | 0 | 09343d42634aa954cac867f7e426eee260b4df57 | https://github.com/imflash217/bumblebee/tree/09343d42634aa954cac867f7e426eee260b4df57 |
ReluSquared | import torch
from torch import nn
import torch.nn.functional as F
class ReluSquared(nn.Module):
def forward(self, input):
return F.relu(input) ** 2
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | imflash217/bumblebee | ReluSquared | false | 12,531 | [
"MIT"
] | 0 | 09343d42634aa954cac867f7e426eee260b4df57 | https://github.com/imflash217/bumblebee/tree/09343d42634aa954cac867f7e426eee260b4df57 |
gram_mse_loss | import torch
import torch.nn as nn
class gram_matrix(nn.Module):
def forward(self, input):
b, c, w, h = input.size()
F = input.view(b, c, h * w)
G = torch.bmm(F, F.transpose(1, 2))
G.div_(h * w)
return G
class gram_mse_loss(nn.Module):
def forward(self, input, targe... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | ipjessica/neural-style-transfer | gram_mse_loss | false | 12,532 | [
"MIT"
] | 0 | ae0fc5e1e69d5d52997e5cab69e880085e04723b | https://github.com/ipjessica/neural-style-transfer/tree/ae0fc5e1e69d5d52997e5cab69e880085e04723b |
ECB | import torch
from torch import nn as nn
from torch.nn import functional as F
from torch.nn import init as init
from torchvision.models import vgg as vgg
import torch.utils.data
from torch.utils import data as data
from torch import autograd as autograd
class SeqConv3x3(nn.Module):
def __init__(self, seq_type, 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 import nn as nn
from torch.nn import functional as F
from torch.nn im... | hyunobae/BasicSR | ECB | false | 12,533 | [
"Apache-2.0"
] | 0 | f2c2fc6cf28933658816c808f55c95fa20b16483 | https://github.com/hyunobae/BasicSR/tree/f2c2fc6cf28933658816c808f55c95fa20b16483 |
FullSelfAttn | import torch
import torch.nn as nn
import torch.utils.data
class FullSelfAttn(nn.Module):
""" Self attention Layer"""
def __init__(self, in_dim):
super().__init__()
self.query_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim //
2, kernel_size=1)
self.key_conv = nn.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ilyak93/SinGanF2 | FullSelfAttn | false | 12,534 | [
"MIT"
] | 0 | fa6b135ef4699626ce450afd02ed3b269e4ca16d | https://github.com/ilyak93/SinGanF2/tree/fa6b135ef4699626ce450afd02ed3b269e4ca16d |
gram_matrix | import torch
import torch.nn as nn
class gram_matrix(nn.Module):
def forward(self, input):
b, c, w, h = input.size()
F = input.view(b, c, h * w)
G = torch.bmm(F, F.transpose(1, 2))
G.div_(h * w)
return G
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_i... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | ipjessica/neural-style-transfer | gram_matrix | false | 12,535 | [
"MIT"
] | 0 | ae0fc5e1e69d5d52997e5cab69e880085e04723b | https://github.com/ipjessica/neural-style-transfer/tree/ae0fc5e1e69d5d52997e5cab69e880085e04723b |
GramMatrix | import torch
import torch.nn as nn
class GramMatrix(nn.Module):
def forward(self, input):
a, b, c, d = input.size()
features = input.view(a * b, c * d)
G = torch.mm(features, features.t())
return G.div(a * b * c * d)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | invoker4zoo/pytorch_model | GramMatrix | false | 12,536 | [
"MIT"
] | 0 | b74f005ba1be5e66fafaa2745fc7d1815979e91f | https://github.com/invoker4zoo/pytorch_model/tree/b74f005ba1be5e66fafaa2745fc7d1815979e91f |
ChannelWiseLayerNorm | import torch
import torch.nn as nn
class ChannelWiseLayerNorm(nn.LayerNorm):
"""
Channel wise layer normalization
"""
def __init__(self, *args, **kwargs):
super(ChannelWiseLayerNorm, self).__init__(*args, **kwargs)
def forward(self, x):
"""
x: BS x N x K
"""
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | intflow/FullSubNet | ChannelWiseLayerNorm | false | 12,537 | [
"MIT"
] | 0 | 193091acac4c747730db5ace33fd1b8870e7c735 | https://github.com/intflow/FullSubNet/tree/193091acac4c747730db5ace33fd1b8870e7c735 |
CumulativeMagSpectralNorm | import torch
import torch.nn as nn
class CumulativeMagSpectralNorm(nn.Module):
def __init__(self, cumulative=False, use_mid_freq_mu=False):
"""
Args:
cumulative: 是否采用累积的方式计算 mu
use_mid_freq_mu: 仅采用中心频率的 mu 来代替全局 mu
Notes:
先算均值再累加 等同于 先累加再算均值
... | 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... | intflow/FullSubNet | CumulativeMagSpectralNorm | false | 12,538 | [
"MIT"
] | 0 | 193091acac4c747730db5ace33fd1b8870e7c735 | https://github.com/intflow/FullSubNet/tree/193091acac4c747730db5ace33fd1b8870e7c735 |
GradientReversal | import torch
class GradientReversalFunction(torch.autograd.Function):
"""
Gradient Reversal Layer from:
Unsupervised Domain Adaptation by Backpropagation (Ganin & Lempitsky, 2015)
Forward pass is the identity function.
In the backward pass,
the upstream gradients are multiplied by -lambda (i.e... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | ishine/CDFSE_FastSpeech2 | GradientReversal | false | 12,539 | [
"MIT"
] | 0 | f0facd077fa3e11b2704f2e8a1d1315bd1f4f493 | https://github.com/ishine/CDFSE_FastSpeech2/tree/f0facd077fa3e11b2704f2e8a1d1315bd1f4f493 |
ConvLeaky | import torch
from torch import nn
from torch.nn import functional as F
class ConvLeaky(nn.Module):
def __init__(self, in_dim, out_dim):
super(ConvLeaky, self).__init__()
self.conv1 = nn.Conv2d(in_channels=in_dim, out_channels=out_dim,
kernel_size=3, stride=1, padding=1)
self.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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | ivan94fi/fast-sr-unet | ConvLeaky | false | 12,540 | [
"MIT"
] | 0 | 76ff5ee1ca87d8cdd06ce3ec406cfac533041d83 | https://github.com/ivan94fi/fast-sr-unet/tree/76ff5ee1ca87d8cdd06ce3ec406cfac533041d83 |
ConvTemporalGraphical | import torch
import torch.nn as nn
class ConvTemporalGraphical(nn.Module):
"""The basic module for applying a graph convolution.
Args:
in_channels (int): Number of channels in the input sequence data
out_channels (int): Number of channels produced by the convolution
A_channels (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 as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | ishine/speech2affective_gestures | ConvTemporalGraphical | false | 12,541 | [
"MIT"
] | 0 | ea99e3edd82b8ab50a6f63cff301618762b73187 | https://github.com/ishine/speech2affective_gestures/tree/ea99e3edd82b8ab50a6f63cff301618762b73187 |
SACActorNetwork | import torch
import torch.nn.functional as F
import torch.nn as nn
class SACActorNetwork(nn.Module):
def __init__(self, input_shape, output_shape, n_features, **kwargs):
super(SACActorNetwork, self).__init__()
n_input = input_shape[-1]
n_output = output_shape[0]
self._h1 = nn.Line... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | jacarvalho/mushroom-rl-benchmark | SACActorNetwork | false | 12,542 | [
"MIT"
] | 0 | 5bc2e9b1a12be33827d6edcd5c5ad49571e11275 | https://github.com/jacarvalho/mushroom-rl-benchmark/tree/5bc2e9b1a12be33827d6edcd5c5ad49571e11275 |
A2CNetwork | import torch
import torch.nn as nn
class A2CNetwork(nn.Module):
def __init__(self, input_shape, output_shape, n_features, **kwargs):
super(A2CNetwork, self).__init__()
n_input = input_shape[-1]
n_output = output_shape[0]
self._h1 = nn.Linear(n_input, n_features)
self._h2 =... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | jacarvalho/mushroom-rl-benchmark | A2CNetwork | false | 12,543 | [
"MIT"
] | 0 | 5bc2e9b1a12be33827d6edcd5c5ad49571e11275 | https://github.com/jacarvalho/mushroom-rl-benchmark/tree/5bc2e9b1a12be33827d6edcd5c5ad49571e11275 |
DropConnect | import torch
class DropConnect(torch.nn.Module):
def __init__(self, p):
super(DropConnect, self).__init__()
self.p = p
def forward(self, inputs):
batch_size = inputs.shape[0]
inputs.shape[2]
inputs.shape[3]
channel_size = inputs.shape[1]
keep_prob = 1 ... | import torch
from torch import device
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._C._dynamo.guards.assert_si... | jack-willturner/nas-without-training | DropConnect | false | 12,544 | [
"MIT"
] | 0 | d5e915b5f391f51d902f33b1d4beedfe3b09d2e0 | https://github.com/jack-willturner/nas-without-training/tree/d5e915b5f391f51d902f33b1d4beedfe3b09d2e0 |
MaxPool3x3 | import torch
import torch.nn as nn
class MaxPool3x3(nn.Module):
"""3x3 max pool with no subsampling."""
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1,
padding=1):
super(MaxPool3x3, self).__init__()
self.maxpool = nn.MaxPool2d(kernel_size, stride, padding)
... | 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... | jack-willturner/nas-without-training | MaxPool3x3 | false | 12,545 | [
"MIT"
] | 0 | d5e915b5f391f51d902f33b1d4beedfe3b09d2e0 | https://github.com/jack-willturner/nas-without-training/tree/d5e915b5f391f51d902f33b1d4beedfe3b09d2e0 |
SimpleAndModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleAndModule(torch.nn.Module):
def __init__(self):
super(SimpleAndModule, self).__init__()
def forward(self, a, b):
c = torch.logical_and(a, b)
return torch.logical_and(c, c)
def get_inputs():
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.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | briancoutinho/glow | SimpleAndModule | false | 12,546 | [
"Apache-2.0"
] | 0 | 4c919d60b3c33296c4109aec8020a1733c98f5b5 | https://github.com/briancoutinho/glow/tree/4c919d60b3c33296c4109aec8020a1733c98f5b5 |
SACCriticNetwork | import torch
import torch.nn.functional as F
import torch.nn as nn
class SACCriticNetwork(nn.Module):
def __init__(self, input_shape, output_shape, n_features, **kwargs):
super().__init__()
n_input = input_shape[-1]
n_output = output_shape[0]
self._h1 = nn.Linear(n_input, n_featur... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | jacarvalho/mushroom-rl-benchmark | SACCriticNetwork | false | 12,547 | [
"MIT"
] | 0 | 5bc2e9b1a12be33827d6edcd5c5ad49571e11275 | https://github.com/jacarvalho/mushroom-rl-benchmark/tree/5bc2e9b1a12be33827d6edcd5c5ad49571e11275 |
MLPArchitecture | import torch
import torch.nn as nn
from collections.abc import Iterable
class MLPArchitecture(nn.Module):
def __init__(self, batch_size, n_outputs, state_size):
super(MLPArchitecture, self).__init__()
if isinstance(state_size, Iterable):
assert len(state_size) == 1
state_s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from co... | ivallesp/RL_Banana_Collector | MLPArchitecture | false | 12,548 | [
"MIT"
] | 0 | cf09ffa9cff8015dd47592509ae482b99339a960 | https://github.com/ivallesp/RL_Banana_Collector/tree/cf09ffa9cff8015dd47592509ae482b99339a960 |
OneTupleModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class OneTupleModule(torch.nn.Module):
def __init__(self):
super(OneTupleModule, self).__init__()
def forward(self, x):
y = 2 * x
return y,
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs(... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | briancoutinho/glow | OneTupleModule | false | 12,549 | [
"Apache-2.0"
] | 0 | 4c919d60b3c33296c4109aec8020a1733c98f5b5 | https://github.com/briancoutinho/glow/tree/4c919d60b3c33296c4109aec8020a1733c98f5b5 |
SimpleACosModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleACosModule(torch.nn.Module):
def __init__(self):
super(SimpleACosModule, self).__init__()
def forward(self, a):
return torch.acos(a + a)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._... | briancoutinho/glow | SimpleACosModule | false | 12,550 | [
"Apache-2.0"
] | 0 | 4c919d60b3c33296c4109aec8020a1733c98f5b5 | https://github.com/briancoutinho/glow/tree/4c919d60b3c33296c4109aec8020a1733c98f5b5 |
DetachModel | import torch
import torch.jit
import torch.onnx
import torch.nn
class DetachModel(torch.nn.Module):
def __init__(self):
super(DetachModel, self).__init__()
def forward(self, a):
b = a.detach()
return b + b
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_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
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | briancoutinho/glow | DetachModel | false | 12,551 | [
"Apache-2.0"
] | 0 | 4c919d60b3c33296c4109aec8020a1733c98f5b5 | https://github.com/briancoutinho/glow/tree/4c919d60b3c33296c4109aec8020a1733c98f5b5 |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 32, kernel_size=5, padding=2)
self.conv2 = nn.Conv2d(32, 64, kernel_size=5, padding=2)
self.conv3 = nn.Conv2d(64, 64, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | inani47/Transfer_Learning | Net | false | 12,552 | [
"BSD-2-Clause"
] | 0 | 1e28614ceaa38a8034aa45c92b8265c79e64780a | https://github.com/inani47/Transfer_Learning/tree/1e28614ceaa38a8034aa45c92b8265c79e64780a |
DQNFeatureNetwork | import torch
import torch.nn.functional as F
import torch.nn as nn
class DQNFeatureNetwork(nn.Module):
def __init__(self, input_shape, output_shape, **kwargs):
super().__init__()
n_input = input_shape[0]
self._h1 = nn.Conv2d(n_input, 32, kernel_size=8, stride=4)
self._h2 = nn.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
import torch.nn as nn
assert_... | jacarvalho/mushroom-rl-benchmark | DQNFeatureNetwork | false | 12,553 | [
"MIT"
] | 0 | 5bc2e9b1a12be33827d6edcd5c5ad49571e11275 | https://github.com/jacarvalho/mushroom-rl-benchmark/tree/5bc2e9b1a12be33827d6edcd5c5ad49571e11275 |
SimpleBmmModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleBmmModule(torch.nn.Module):
def forward(self, a, b):
return (a + a).bmm(b)
def get_inputs():
return [torch.rand([4, 4, 4]), torch.rand([4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C... | briancoutinho/glow | SimpleBmmModule | false | 12,554 | [
"Apache-2.0"
] | 0 | 4c919d60b3c33296c4109aec8020a1733c98f5b5 | https://github.com/briancoutinho/glow/tree/4c919d60b3c33296c4109aec8020a1733c98f5b5 |
RepeatModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class RepeatModule(torch.nn.Module):
def __init__(self, repeats):
super(RepeatModule, self).__init__()
self.repeats = repeats
def forward(self, tensor):
tensor = tensor + tensor
return tensor.repeat(self.repeats)... | 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.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | briancoutinho/glow | RepeatModule | false | 12,555 | [
"Apache-2.0"
] | 0 | 4c919d60b3c33296c4109aec8020a1733c98f5b5 | https://github.com/briancoutinho/glow/tree/4c919d60b3c33296c4109aec8020a1733c98f5b5 |
SimpleClampModel | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleClampModel(torch.nn.Module):
def __init__(self, min, max):
super(SimpleClampModel, self).__init__()
self.min = min
self.max = max
def forward(self, input):
return torch.clamp(input, self.min, self.max... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.... | briancoutinho/glow | SimpleClampModel | false | 12,556 | [
"Apache-2.0"
] | 0 | 4c919d60b3c33296c4109aec8020a1733c98f5b5 | https://github.com/briancoutinho/glow/tree/4c919d60b3c33296c4109aec8020a1733c98f5b5 |
SimpleATanModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleATanModule(torch.nn.Module):
def __init__(self):
super(SimpleATanModule, self).__init__()
def forward(self, a):
return torch.atan(a + a)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._... | briancoutinho/glow | SimpleATanModule | false | 12,557 | [
"Apache-2.0"
] | 0 | 4c919d60b3c33296c4109aec8020a1733c98f5b5 | https://github.com/briancoutinho/glow/tree/4c919d60b3c33296c4109aec8020a1733c98f5b5 |
SimpleConvTranspose2dModule | import torch
import torch.nn.functional as F
import torch.jit
import torch.onnx
import torch.nn
class SimpleConvTranspose2dModule(torch.nn.Module):
def __init__(self, stride=1, padding=0, output_padding=0, dilation=1,
groups=1):
super(SimpleConvTranspose2dModule, self).__init__()
self.str... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.jit
import torch... | briancoutinho/glow | SimpleConvTranspose2dModule | false | 12,558 | [
"Apache-2.0"
] | 0 | 4c919d60b3c33296c4109aec8020a1733c98f5b5 | https://github.com/briancoutinho/glow/tree/4c919d60b3c33296c4109aec8020a1733c98f5b5 |
SimpleConv2dModule | import torch
import torch.nn.functional as F
import torch.jit
import torch.onnx
import torch.nn
class SimpleConv2dModule(torch.nn.Module):
def __init__(self, stride=1, padding=0, dilation=1, groups=1):
super(SimpleConv2dModule, self).__init__()
self.stride = stride
self.padding = 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._inductor.runtime import triton_helpers
import torch.jit
import torch... | briancoutinho/glow | SimpleConv2dModule | false | 12,559 | [
"Apache-2.0"
] | 0 | 4c919d60b3c33296c4109aec8020a1733c98f5b5 | https://github.com/briancoutinho/glow/tree/4c919d60b3c33296c4109aec8020a1733c98f5b5 |
SimpleASinModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleASinModule(torch.nn.Module):
def __init__(self):
super(SimpleASinModule, self).__init__()
def forward(self, a):
return torch.asin(a + a)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._... | briancoutinho/glow | SimpleASinModule | false | 12,560 | [
"Apache-2.0"
] | 0 | 4c919d60b3c33296c4109aec8020a1733c98f5b5 | https://github.com/briancoutinho/glow/tree/4c919d60b3c33296c4109aec8020a1733c98f5b5 |
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