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 |
|---|---|---|---|---|---|---|---|---|---|---|
CustomBatchNormAutograd | import torch
import torch.nn as nn
class CustomBatchNormAutograd(nn.Module):
"""
This nn.module implements a custom version of the batch norm operation for MLPs.
The operations called in self.forward track the history if the input tensors have the
flag requires_grad set to True. The backward pass does not 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.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | askliar/deep_learning | CustomBatchNormAutograd | false | 1,488 | [
"MIT"
] | 0 | e61b2391a3258d18719bf12d9ed1404620ce6c02 | https://github.com/askliar/deep_learning/tree/e61b2391a3258d18719bf12d9ed1404620ce6c02 |
CustomBatchNormManualModule | import torch
import torch.nn as nn
class CustomBatchNormManualFunction(torch.autograd.Function):
"""
This torch.autograd.Function implements a functional custom version of the batch norm operation for MLPs.
Using torch.autograd.Function allows you to write a custom backward function.
The function will be ca... | 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_... | askliar/deep_learning | CustomBatchNormManualModule | false | 1,489 | [
"MIT"
] | 0 | e61b2391a3258d18719bf12d9ed1404620ce6c02 | https://github.com/askliar/deep_learning/tree/e61b2391a3258d18719bf12d9ed1404620ce6c02 |
ScalarMix | import torch
import torch.nn as nn
class ScalarMix(nn.Module):
"""
Computes a parameterized scalar mixture of :math:`N` tensors, :math:`mixture = \\gamma * \\sum_{k}(s_k * tensor_k)`
where :math:`s = \\mathrm{softmax}(w)`, with :math:`w` and :math:`\\gamma` scalar parameters.
Args:
n_layers (... | 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
assert_size_stride = torch._C._dynamo.guards.assert... | attardi/parser | ScalarMix | false | 1,490 | [
"MIT"
] | 0 | 1978ba94ba649ad0a723d71bb2ca225c7e705702 | https://github.com/attardi/parser/tree/1978ba94ba649ad0a723d71bb2ca225c7e705702 |
TorchGloVeLoss | import torch
import torch.nn as nn
import torch.utils.data
class TorchGloVeLoss(nn.Module):
def __init__(self):
super().__init__()
self.reduction = 'sum'
def forward(self, diffs, weights):
return torch.sum(0.5 * torch.mul(weights, diffs ** 2))
def get_inputs():
return [torch.ra... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guard... | atticusg/cs224u | TorchGloVeLoss | false | 1,491 | [
"Apache-2.0"
] | 0 | 66e0f2714e246dcb8836f706ae9ff5613c51ed34 | https://github.com/atticusg/cs224u/tree/66e0f2714e246dcb8836f706ae9ff5613c51ed34 |
TorchGloVeModel | import torch
import torch.nn as nn
import torch.utils.data
from torch.nn.init import xavier_uniform_
class TorchGloVeModel(nn.Module):
def __init__(self, n_words, embed_dim):
super().__init__()
self.n_words = n_words
self.embed_dim = embed_dim
self.W = self._init_weights(self.n_wo... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
from torch.nn.init import xavier_u... | atticusg/cs224u | TorchGloVeModel | false | 1,492 | [
"Apache-2.0"
] | 0 | 66e0f2714e246dcb8836f706ae9ff5613c51ed34 | https://github.com/atticusg/cs224u/tree/66e0f2714e246dcb8836f706ae9ff5613c51ed34 |
CrossEntropyLossIIT | import torch
import torch.nn as nn
import torch.utils.data
class CrossEntropyLossIIT(nn.Module):
def __init__(self):
super().__init__()
self.loss = nn.CrossEntropyLoss(reduction='mean')
def forward(self, preds, labels):
return self.loss(preds[0], labels[:, 0]) + self.loss(preds[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._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | atticusg/cs224u | CrossEntropyLossIIT | false | 1,493 | [
"Apache-2.0"
] | 0 | 66e0f2714e246dcb8836f706ae9ff5613c51ed34 | https://github.com/atticusg/cs224u/tree/66e0f2714e246dcb8836f706ae9ff5613c51ed34 |
ClassicModel | from _paritybench_helpers import _mock_config
import torch
from torch import nn
class ClassicModel(nn.Module):
def __init__(self, config, inpt_shp):
super(ClassicModel, self).__init__()
self.depth = config.depth
self.nodes = config.n_qubits
self.config = config
self.pre_ne... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | austinbeauch/QML | ClassicModel | false | 1,494 | [
"Apache-2.0"
] | 0 | 4a2f5b1b0346a1a8bb0a0e6e638cf2225c4c213c | https://github.com/austinbeauch/QML/tree/4a2f5b1b0346a1a8bb0a0e6e638cf2225c4c213c |
LayerNorm | import torch
import torch.nn as nn
import torch.nn.functional as F
class LayerNorm(nn.Module):
def __init__(self, num_features, eps=1e-05, affine=True):
super(LayerNorm, self).__init__()
self.num_features = num_features
self.eps = eps
self.affine = affine
if self.affine:
... | 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_... | audreyeternal/cyclegan | LayerNorm | false | 1,495 | [
"MIT"
] | 0 | 8eb3ddb7fd0d9838862334766f1f7aaa5584c2da | https://github.com/audreyeternal/cyclegan/tree/8eb3ddb7fd0d9838862334766f1f7aaa5584c2da |
BinaryLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class BinaryLoss(nn.Module):
"""
Computes contrastive loss[1, 2] twice, one time for the distance between query and positive example,
and another for the distance between query and negative example. Both use l2-distance.
[1] http:/... | 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... | atypon/specter | BinaryLoss | false | 1,496 | [
"Apache-2.0"
] | 0 | bc1ee723167cf1dbf599603e09539c1823f26c17 | https://github.com/atypon/specter/tree/bc1ee723167cf1dbf599603e09539c1823f26c17 |
BertSelfAttention | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
import torch.utils.data
class BertSelfAttention(nn.Module):
def __init__(self, config):
super(BertSelfAttention, self).__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | ashutoshbaghel/tgifqa-lxmert | BertSelfAttention | false | 1,497 | [
"MIT"
] | 0 | 7969f478d20fbfbba1c0eaaf0b96891654bfcc26 | https://github.com/ashutoshbaghel/tgifqa-lxmert/tree/7969f478d20fbfbba1c0eaaf0b96891654bfcc26 |
WCE_loss | import torch
import torch.nn as nn
class WCE_loss(nn.Module):
def __init__(self):
super(WCE_loss, self).__init__()
def sum_ij(self, x):
return torch.sum(torch.sum(x, dim=3), dim=2)
def forward(self, pred, gt):
N_fg = self.sum_ij(gt)
N_bg = self.sum_ij(1 - gt)
L_f... | 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
assert_size_stride = torch._C._dynamo.guards.assert... | atch841/pytorch-one-shot-texture-segmentation | WCE_loss | false | 1,498 | [
"MIT"
] | 0 | 8b781b861d17eb1e1e7014f54f8fd39dc10dd2b8 | https://github.com/atch841/pytorch-one-shot-texture-segmentation/tree/8b781b861d17eb1e1e7014f54f8fd39dc10dd2b8 |
MLP | import torch
import torch.nn as nn
class SharedDropout(nn.Module):
"""
SharedDropout differs from the vanilla dropout strategy in that the dropout mask is shared across one dimension.
Args:
p (float):
The probability of an element to be zeroed. Default: 0.5.
batch_first (bool)... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | attardi/parser | MLP | false | 1,499 | [
"MIT"
] | 0 | 1978ba94ba649ad0a723d71bb2ca225c7e705702 | https://github.com/attardi/parser/tree/1978ba94ba649ad0a723d71bb2ca225c7e705702 |
GELU | import torch
import torch.nn as nn
class GELU(nn.Module):
def __init__(self):
super(GELU, self).__init__()
def forward(self, x):
return torch.sigmoid(1.702 * x) * x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | awgansekoele/outlier-exposure | GELU | false | 1,500 | [
"Apache-2.0"
] | 0 | 9557c7915fa466fc54951357519cfba27f7659ad | https://github.com/awgansekoele/outlier-exposure/tree/9557c7915fa466fc54951357519cfba27f7659ad |
folder | import torch
from torch import nn
import torch.nn.functional as F
import torch.nn.parallel
class folder(nn.Module):
def __init__(self):
super().__init__()
def forward(self, feature_map):
N, _, H, W = feature_map.size()
feature_map = F.unfold(feature_map, kernel_size=3, padding=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 import nn
import torch.nn.parallel
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | avinash-asink/AdelaiDet | folder | false | 1,501 | [
"BSD-2-Clause"
] | 0 | a8722579c8a724b02a36ef0f33a176ba282623fa | https://github.com/avinash-asink/AdelaiDet/tree/a8722579c8a724b02a36ef0f33a176ba282623fa |
Arc2 | from torch.nn import Module
import torch
import numpy as np
class Arc2(Module):
def __init__(self, num_bends):
super(Arc2, self).__init__()
self.num_bends = num_bends
self.register_buffer('range', torch.arange(0, float(num_bends)))
def a(self, t):
return torch.cos(np.pi * t /... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch.nn import Module
import numpy as np
assert_size_stride = torch... | avecplezir/dnn-mode-connectivity | Arc2 | false | 1,502 | [
"BSD-2-Clause"
] | 0 | 9a92ca370571f542b33060f637239172a0d08bba | https://github.com/avecplezir/dnn-mode-connectivity/tree/9a92ca370571f542b33060f637239172a0d08bba |
Normalize | import torch
import torch.nn as nn
class Normalize(nn.Module):
"""Normalize nn.Module. As at pytorch, simplified"""
def __init__(self, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225
], inplace=False, dtype=torch.float32):
super().__init__()
mean = torch.as_tensor(mean, dtype=dty... | 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... | ayasyrev/pt_utils | Normalize | false | 1,503 | [
"Apache-2.0"
] | 0 | cb29b8fb4a3981248e1055979cc773f719dccdc7 | https://github.com/ayasyrev/pt_utils/tree/cb29b8fb4a3981248e1055979cc773f719dccdc7 |
FocalLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class FocalLoss(nn.modules.loss._WeightedLoss):
def __init__(self, weight=None, gamma=2, reduction='mean'):
super(FocalLoss, self).__init__(weight, reduction=reduction)
self.gamma = gamma
self.weight = weight
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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | aydindemircioglu/knee.lat | FocalLoss | false | 1,504 | [
"MIT"
] | 0 | 555725222f860d4ad8fea7452685803d9e323d43 | https://github.com/aydindemircioglu/knee.lat/tree/555725222f860d4ad8fea7452685803d9e323d43 |
DQNet | import torch
import torch.nn as nn
from torch.nn import functional as F
class DQNet(nn.Module):
"""
Definition: DQNet(obs_size,act_size,hid_size=256)
Regular Deep Q Network with three Linear layers
"""
def __init__(self, obs_size, act_size, hid_size=256):
super().__init__()
self.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | ayjabri/DeepRL | DQNet | false | 1,505 | [
"MIT"
] | 0 | 0be095e3a3d04f60b4cdc97ed330dffc17b3024a | https://github.com/ayjabri/DeepRL/tree/0be095e3a3d04f60b4cdc97ed330dffc17b3024a |
Arc | from torch.nn import Module
import torch
import numpy as np
class Arc(Module):
def __init__(self, num_bends):
super(Arc, self).__init__()
self.num_bends = num_bends
self.register_buffer('range', torch.arange(0, float(num_bends)))
def a(self, t):
return torch.cos(np.pi * t / 2... | 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.nn import Module
import numpy as np
assert_size_stride = torch... | avecplezir/dnn-mode-connectivity | Arc | false | 1,506 | [
"BSD-2-Clause"
] | 0 | 9a92ca370571f542b33060f637239172a0d08bba | https://github.com/avecplezir/dnn-mode-connectivity/tree/9a92ca370571f542b33060f637239172a0d08bba |
DuelDQNet | import torch
import torch.nn as nn
from torch.nn import functional as F
class DuelDQNet(nn.Module):
"""
Definition: DuelDQNet(obs_size, act_size, hid_size=256)
"""
def __init__(self, obs_size, act_size, hid_size=256):
super().__init__()
self.base = nn.Linear(obs_size, hid_size)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | ayjabri/DeepRL | DuelDQNet | false | 1,507 | [
"MIT"
] | 0 | 0be095e3a3d04f60b4cdc97ed330dffc17b3024a | https://github.com/ayjabri/DeepRL/tree/0be095e3a3d04f60b4cdc97ed330dffc17b3024a |
TripletLossDoubleMargin | import torch
import torch.nn as nn
class TripletLossDoubleMargin(nn.Module):
"""
Triplet Loss with positive and negative margins, following the work of [1]
References
----------
[1] Ho, K., Keuper, J., Pfreundt, F. J., & Keuper, M. (2021, January).
Learning embeddings for image clusterin... | 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... | ax-le/MusicAE | TripletLossDoubleMargin | false | 1,508 | [
"BSD-3-Clause"
] | 0 | 9fdc268f6403226b990d9ae5c9f182ed0af82f98 | https://github.com/ax-le/MusicAE/tree/9fdc268f6403226b990d9ae5c9f182ed0af82f98 |
A2CNet | import torch
import torch.nn as nn
from torch.nn import functional as F
class A2CNet(nn.Module):
"""Double heads actor + critic network."""
def __init__(self, obs_size, act_size, hid_size=128):
super().__init__()
self.fc1 = nn.Linear(obs_size, hid_size)
self.policy = nn.Linear(hid_siz... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | ayjabri/DeepRL | A2CNet | false | 1,509 | [
"MIT"
] | 0 | 0be095e3a3d04f60b4cdc97ed330dffc17b3024a | https://github.com/ayjabri/DeepRL/tree/0be095e3a3d04f60b4cdc97ed330dffc17b3024a |
BertOutAttention | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
import torch.utils.data
class BertOutAttention(nn.Module):
def __init__(self, config, ctx_dim=None):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueErro... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | ashutoshbaghel/tgifqa-lxmert | BertOutAttention | false | 1,510 | [
"MIT"
] | 0 | 7969f478d20fbfbba1c0eaaf0b96891654bfcc26 | https://github.com/ashutoshbaghel/tgifqa-lxmert/tree/7969f478d20fbfbba1c0eaaf0b96891654bfcc26 |
DiceLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
class BinaryDiceLoss(nn.Module):
"""Dice loss of binary class
Args:
smooth: A float number to smooth loss, and avoid NaN error, default: 1
p: Denominator value: \\sum{x^p} + \\sum{y^p}, default: 2
predict: A tensor of 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 math as tl_math
import torch.nn as nn
... | aureliedj/OilseedRapeSegmentation | DiceLoss | false | 1,511 | [
"MIT"
] | 0 | 89056c3295b24354c32b6059854a3a60214c26cb | https://github.com/aureliedj/OilseedRapeSegmentation/tree/89056c3295b24354c32b6059854a3a60214c26cb |
GRUCell | import torch
import torch.nn as nn
from torch.nn import functional as F
class GRUCell(nn.Module):
"""Plain vanilla policy gradient network."""
def __init__(self, obs_size, act_size, hid_size=128):
super().__init__()
self.fc1 = nn.GRUCell(obs_size, hid_size)
self.output = nn.Linear(hid... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | ayjabri/DeepRL | GRUCell | false | 1,512 | [
"MIT"
] | 0 | 0be095e3a3d04f60b4cdc97ed330dffc17b3024a | https://github.com/ayjabri/DeepRL/tree/0be095e3a3d04f60b4cdc97ed330dffc17b3024a |
ELU_1 | import torch
import torch.nn.functional as F
import torch.nn as nn
class ELU_1(nn.ELU):
def __init__(self, *args, **kwargs):
super(ELU_1, self).__init__(*args, **kwargs)
def forward(self, input):
return F.elu(input, self.alpha, self.inplace)
def get_inputs():
return [torch.rand([4, 4, ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | bantiitnab/kaggle-TGS-salt-identification | ELU_1 | false | 1,513 | [
"MIT"
] | 0 | 8b3350278b2ee8f01ba2a0734af9514d369f3228 | https://github.com/bantiitnab/kaggle-TGS-salt-identification/tree/8b3350278b2ee8f01ba2a0734af9514d369f3228 |
TPSELoss | import torch
import torch.utils.data
from torch import nn
class TPSELoss(nn.Module):
def __init__(self):
super(TPSELoss, self).__init__()
self.loss = nn.L1Loss()
def forward(self, model_output):
gst_embed, tpse_embed = model_output[4], model_output[5]
gst_embed = gst_embed.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.triton_helpers import math as tl_math
import torch.utils.data
from torch import nn
assert_size_stride = torch._... | azahed98/mellotron | TPSELoss | false | 1,514 | [
"BSD-3-Clause"
] | 0 | 02998743de820e379e0c7ff44506088d6e65c693 | https://github.com/azahed98/mellotron/tree/02998743de820e379e0c7ff44506088d6e65c693 |
ActorNet | import torch
import torch.nn as nn
from torch.nn import functional as F
class ActorNet(nn.Module):
def __init__(self, obs_size, act_size, high_action=1):
super().__init__()
self.high_action = high_action
self.base = nn.Linear(obs_size, 400)
self.fc1 = nn.Linear(400, 300)
s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ayjabri/DeepRL | ActorNet | false | 1,515 | [
"MIT"
] | 0 | 0be095e3a3d04f60b4cdc97ed330dffc17b3024a | https://github.com/ayjabri/DeepRL/tree/0be095e3a3d04f60b4cdc97ed330dffc17b3024a |
DiceLoss | import torch
import torch.nn as nn
def IoU(logit, truth, smooth=1):
prob = torch.sigmoid(logit)
intersection = torch.sum(prob * truth)
union = torch.sum(prob + truth)
iou = (2 * intersection + smooth) / (union + smooth)
return iou
class DiceLoss(nn.Module):
def __init__(self, smooth=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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | bantiitnab/kaggle-TGS-salt-identification | DiceLoss | false | 1,516 | [
"MIT"
] | 0 | 8b3350278b2ee8f01ba2a0734af9514d369f3228 | https://github.com/bantiitnab/kaggle-TGS-salt-identification/tree/8b3350278b2ee8f01ba2a0734af9514d369f3228 |
BCE_Dice | import torch
import torch.nn as nn
def IoU(logit, truth, smooth=1):
prob = torch.sigmoid(logit)
intersection = torch.sum(prob * truth)
union = torch.sum(prob + truth)
iou = (2 * intersection + smooth) / (union + smooth)
return iou
class DiceLoss(nn.Module):
def __init__(self, smooth=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._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | bantiitnab/kaggle-TGS-salt-identification | BCE_Dice | false | 1,517 | [
"MIT"
] | 0 | 8b3350278b2ee8f01ba2a0734af9514d369f3228 | https://github.com/bantiitnab/kaggle-TGS-salt-identification/tree/8b3350278b2ee8f01ba2a0734af9514d369f3228 |
VanillaRNN | import torch
import torch.nn as nn
class VanillaRNN(nn.Module):
def __init__(self, seq_length, input_dim, num_hidden, num_classes,
batch_size, device='cpu'):
super(VanillaRNN, self).__init__()
self.seq_length = seq_length
self.h_init = nn.Parameter(torch.zeros(num_hidden, 1),
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | askliar/deep_learning | VanillaRNN | false | 1,518 | [
"MIT"
] | 0 | e61b2391a3258d18719bf12d9ed1404620ce6c02 | https://github.com/askliar/deep_learning/tree/e61b2391a3258d18719bf12d9ed1404620ce6c02 |
MLP | import torch
import torch.nn as nn
class MLP(nn.Module):
"""
Multi-Layer Perceptron network
"""
def __init__(self, obs_dim, dim_latent):
"""
Constructor
Args:
obs_dim: (int) dimension of observation
latent_dim: (int) dimension of output latent
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | baihuaxie/drl-lib | MLP | false | 1,519 | [
"MIT"
] | 0 | 3ad344901c3bb59e0bc16bb70202d2cfd538fd77 | https://github.com/baihuaxie/drl-lib/tree/3ad344901c3bb59e0bc16bb70202d2cfd538fd77 |
ChannelGate2d | import torch
import torch.nn as nn
class ChannelGate2d(nn.Module):
def __init__(self, channels, reduction=2):
super(ChannelGate2d, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc1 = nn.Conv2d(channels, channels // reduction, kernel_size=1,
padding=0)
s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | bantiitnab/kaggle-TGS-salt-identification | ChannelGate2d | false | 1,520 | [
"MIT"
] | 0 | 8b3350278b2ee8f01ba2a0734af9514d369f3228 | https://github.com/bantiitnab/kaggle-TGS-salt-identification/tree/8b3350278b2ee8f01ba2a0734af9514d369f3228 |
SpatialGate2d | import torch
import torch.nn as nn
class SpatialGate2d(nn.Module):
def __init__(self, in_channels):
super(SpatialGate2d, self).__init__()
self.conv1 = nn.Conv2d(in_channels, 1, kernel_size=1, stride=1)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
cal = self.conv1(x)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | bantiitnab/kaggle-TGS-salt-identification | SpatialGate2d | false | 1,521 | [
"MIT"
] | 0 | 8b3350278b2ee8f01ba2a0734af9514d369f3228 | https://github.com/bantiitnab/kaggle-TGS-salt-identification/tree/8b3350278b2ee8f01ba2a0734af9514d369f3228 |
HingeLoss | import torch
class HingeLoss(torch.nn.Module):
def __init__(self):
super(HingeLoss, self).__init__()
def forward(self, x, y, margin=2):
output = (margin - x + y).clamp(min=0)
return output.mean()
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | bashish101/ir | HingeLoss | false | 1,522 | [
"MIT"
] | 0 | cc90e86827c19035f38d0d85154f073a86aa9796 | https://github.com/bashish101/ir/tree/cc90e86827c19035f38d0d85154f073a86aa9796 |
Critic | import torch
import torch.nn as nn
import torch.nn.functional as F
class Critic(nn.Module):
def __init__(self, state_size, action_size, action_parameter_size,
hidden_layers=None, action_input_layer=0, init_type='normal',
activation='leaky_relu', init_std=0.01):
super(Critic, self).__init_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | bcahlit/MP-DQN | Critic | false | 1,523 | [
"MIT"
] | 0 | d80d34680e20192134f39e5b7c43abbc6bff3ba1 | https://github.com/bcahlit/MP-DQN/tree/d80d34680e20192134f39e5b7c43abbc6bff3ba1 |
Actor | import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
def hidden_init(layer):
"""
compute the bounds [-lim, lim] for subsequent uniform sampling
with lim = 1/sqrt(nb_output)
"""
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, 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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | bar0net/Udacity_DeepReinforcementLearning | Actor | false | 1,524 | [
"MIT"
] | 0 | 3b5f98b7c2c1911b351be541fda3aa190bf48456 | https://github.com/bar0net/Udacity_DeepReinforcementLearning/tree/3b5f98b7c2c1911b351be541fda3aa190bf48456 |
BertOutput | from _paritybench_helpers import _mock_config
import torch
import torch.nn
import torch.nn as nn
class BertOutput(nn.Module):
"""BERT output layer.
Based on: BERT (pytorch-transformer)
https://github.com/huggingface/transformers
"""
def __init__(self, config) ->None:
super().__init__()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn
imp... | bamf-health/MONAI | BertOutput | false | 1,525 | [
"Apache-2.0"
] | 0 | 6a2086d21baf4b60c2ab3d400ed5c97cf24a0da9 | https://github.com/bamf-health/MONAI/tree/6a2086d21baf4b60c2ab3d400ed5c97cf24a0da9 |
Actor | import torch
import torch.nn as nn
import torch.nn.functional as F
class Actor(nn.Module):
def __init__(self, state_size, action_size, action_parameter_size,
hidden_layers=None, init_std=0.01, init_type='normal', activation=
'leaky_relu', squashing_function=False):
super(Actor, self).__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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | bcahlit/MP-DQN | Actor | false | 1,526 | [
"MIT"
] | 0 | d80d34680e20192134f39e5b7c43abbc6bff3ba1 | https://github.com/bcahlit/MP-DQN/tree/d80d34680e20192134f39e5b7c43abbc6bff3ba1 |
BigRamDuel | import torch
import torch.nn as nn
from torch.nn import functional as F
class BigRamDuel(nn.Module):
"""
Definition: DuelDQNet(obs_size, act_size)
"""
def __init__(self, obs_size, act_size):
super().__init__()
self.base = nn.Linear(obs_size, 256)
self.fc1 = nn.Linear(256, 256)... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | ayjabri/DeepRL | BigRamDuel | false | 1,527 | [
"MIT"
] | 0 | 0be095e3a3d04f60b4cdc97ed330dffc17b3024a | https://github.com/ayjabri/DeepRL/tree/0be095e3a3d04f60b4cdc97ed330dffc17b3024a |
duelingDQNnetwork | import torch
import torch.nn.functional as F
import torch.nn as nn
class duelingDQNnetwork(nn.Module):
"""Actor (Policy) Model."""
def __init__(self, state_size, action_size, seed, fc1_units=64,
fc2_units=64):
"""Initialize parameters and build model.
Params
======
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | beibeiJ/deep-reinforcement-learning | duelingDQNnetwork | false | 1,528 | [
"MIT"
] | 0 | ab1b0f4ada8da69af2e38d3e2e82e3ae55837c60 | https://github.com/beibeiJ/deep-reinforcement-learning/tree/ab1b0f4ada8da69af2e38d3e2e82e3ae55837c60 |
LocAndConf | import torch
import torch.nn as nn
from math import sqrt as sqrt
from itertools import product as product
class LocAndConf(nn.Module):
def __init__(self, c_in, c_out, num_classes):
super(LocAndConf, self).__init__()
self.c_in = c_in
self.c_out = c_out
self.num_classes = num_classe... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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 math import sqrt as sqrt
from itertools import produc... | beichen2012/ssd.pytorch | LocAndConf | false | 1,529 | [
"MIT"
] | 0 | 90b68a6903d2bef4c358e295d88b25e6fc6daf54 | https://github.com/beichen2012/ssd.pytorch/tree/90b68a6903d2bef4c358e295d88b25e6fc6daf54 |
Actor | import torch
import torch.nn as nn
import torch.nn.functional as F
class Actor(nn.Module):
def __init__(self, state_dim, action_dim, max_action):
super(Actor, self).__init__()
self.l1 = nn.Linear(state_dim, 256)
self.l2 = nn.Linear(256, 256)
self.l3 = nn.Linear(256, action_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.... | baturaysaglam/DISCOVER | Actor | false | 1,530 | [
"MIT"
] | 0 | 423158c84a5935ca5755ccad06ea5fe20fb57d76 | https://github.com/baturaysaglam/DISCOVER/tree/423158c84a5935ca5755ccad06ea5fe20fb57d76 |
PositionEncoder | from _paritybench_helpers import _mock_config
import torch
import numpy as np
import torch.nn as nn
class PositionEncoder(nn.Module):
"""
Encodes the information into vectors
There are 2 pieces of information that goes into the encoded information:
1. Word Embedding
2. Position Embedding
Thi... | 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.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dyna... | benedictleedm/sgnlp | PositionEncoder | false | 1,531 | [
"MIT"
] | 0 | 03f0fda8c517d9ca4baf737ce4c46b2495bbd3ba | https://github.com/benedictleedm/sgnlp/tree/03f0fda8c517d9ca4baf737ce4c46b2495bbd3ba |
GeM | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Parameter
from torch.nn.parameter import Parameter
def gem(x, p=3, eps=1e-06):
return F.avg_pool2d(x.clamp(min=eps).pow(p), (x.size(-2), x.size(-1))).pow(
1.0 / p)
class GeM(nn.Module):
def __init__(self, p=3.0,... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import... | beesk135/ReID-Survey | GeM | false | 1,532 | [
"MIT"
] | 0 | d1467c0ce5d3ca78640196360a05df9ff9f9f42a | https://github.com/beesk135/ReID-Survey/tree/d1467c0ce5d3ca78640196360a05df9ff9f9f42a |
CoreNetwork | import torch
import torch.nn as nn
import torch.nn.functional as F
class CoreNetwork(nn.Module):
"""The core network.
An RNN that maintains an internal state by integrating
information extracted from the history of past observations.
It encodes the agent's knowledge of the environment through
a s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | bennzo/DT-RAM-PyTorch | CoreNetwork | false | 1,533 | [
"MIT"
] | 0 | b364662ab7650ffd26cf129673752521e004b13a | https://github.com/bennzo/DT-RAM-PyTorch/tree/b364662ab7650ffd26cf129673752521e004b13a |
MultiHeadAttention | import torch
import torch.utils.data
from torch import nn
import torch.nn.functional as F
class MultiHeadAttention(nn.Module):
"""
input:
query --- [N, T_q, query_dim]
key --- [N, T_k, key_dim]
output:
out --- [N, T_q, num_units]
"""
def __init__(self, query_dim, key_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.... | azahed98/mellotron | MultiHeadAttention | false | 1,534 | [
"BSD-3-Clause"
] | 0 | 02998743de820e379e0c7ff44506088d6e65c693 | https://github.com/azahed98/mellotron/tree/02998743de820e379e0c7ff44506088d6e65c693 |
StoppingNetwork | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions import Bernoulli
class StoppingNetwork(nn.Module):
"""The stopping network.
Uses the internal state `h_t` of the core network
to determine whether the network integrated enough
information to make a confident ... | import torch
from torch import device
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
impo... | bennzo/DT-RAM-PyTorch | StoppingNetwork | false | 1,535 | [
"MIT"
] | 0 | b364662ab7650ffd26cf129673752521e004b13a | https://github.com/bennzo/DT-RAM-PyTorch/tree/b364662ab7650ffd26cf129673752521e004b13a |
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_... | benvoe/mushroom-rl-benchmark | SACActorNetwork | false | 1,536 | [
"MIT"
] | 0 | 217d8c077bf6f3febaed92821a2cf183c83f703b | https://github.com/benvoe/mushroom-rl-benchmark/tree/217d8c077bf6f3febaed92821a2cf183c83f703b |
FeedForwardNetwork | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class FeedForwardNetwork(nn.Module):
"""
Based on the paper, each layer has 2 subayers:
A multi-headed attention mechanism &
a position-wise fully connected feed-forward network
Each layer employs a r... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | benedictleedm/sgnlp | FeedForwardNetwork | false | 1,537 | [
"MIT"
] | 0 | 03f0fda8c517d9ca4baf737ce4c46b2495bbd3ba | https://github.com/benedictleedm/sgnlp/tree/03f0fda8c517d9ca4baf737ce4c46b2495bbd3ba |
MLP | import torch
class MLP(torch.nn.Module):
def __init__(self, insize, outsize=128, nonlinear=torch.nn.ReLU,
activation=torch.nn.ReLU, hidden_layer_size=1, node_size=256):
super(MLP, self).__init__()
self.net = torch.nn.Sequential()
self.net.add_module('fc_1', torch.nn.Linear(insize,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | bengisug/multiagent-particle-envs | MLP | false | 1,538 | [
"MIT"
] | 0 | c87280f18fbaf885932fe6da4d600ab474fd83fe | https://github.com/bengisug/multiagent-particle-envs/tree/c87280f18fbaf885932fe6da4d600ab474fd83fe |
Classification | import torch
import torch.nn as nn
import torch.nn.functional as F
class Classification(torch.nn.Module):
def __init__(self, num_class, hidden_dim):
super(Classification, self).__init__()
self.num_class = num_class
self.label = nn.Linear(hidden_dim, num_class)
def forward(self, input... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | bentherien/measevalcompetition | Classification | false | 1,539 | [
"MIT"
] | 0 | 1d285991eb26403682a633a728629a9900923d80 | https://github.com/bentherien/measevalcompetition/tree/1d285991eb26403682a633a728629a9900923d80 |
LocationNetwork | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions import Normal
class LocationNetwork(nn.Module):
"""The location network.
Uses the internal state `h_t` of the core network to
produce the location coordinates `l_t` for the next
time step.
Concretely, fee... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | bennzo/DT-RAM-PyTorch | LocationNetwork | false | 1,540 | [
"MIT"
] | 0 | b364662ab7650ffd26cf129673752521e004b13a | https://github.com/bennzo/DT-RAM-PyTorch/tree/b364662ab7650ffd26cf129673752521e004b13a |
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 ... | benvoe/mushroom-rl-benchmark | A2CNetwork | false | 1,541 | [
"MIT"
] | 0 | 217d8c077bf6f3febaed92821a2cf183c83f703b | https://github.com/benvoe/mushroom-rl-benchmark/tree/217d8c077bf6f3febaed92821a2cf183c83f703b |
BertMixedLayer | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn
import torch.nn as nn
class BertAttention(nn.Module):
"""BERT attention layer.
Based on: BERT (pytorch-transformer)
https://github.com/huggingface/transformers
"""
def __init__(self, config) ->None:
sup... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | bamf-health/MONAI | BertMixedLayer | false | 1,542 | [
"Apache-2.0"
] | 0 | 6a2086d21baf4b60c2ab3d400ed5c97cf24a0da9 | https://github.com/bamf-health/MONAI/tree/6a2086d21baf4b60c2ab3d400ed5c97cf24a0da9 |
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_... | benvoe/mushroom-rl-benchmark | SACCriticNetwork | false | 1,543 | [
"MIT"
] | 0 | 217d8c077bf6f3febaed92821a2cf183c83f703b | https://github.com/benvoe/mushroom-rl-benchmark/tree/217d8c077bf6f3febaed92821a2cf183c83f703b |
Time2Vec | import torch
from torch import nn
class Time2Vec(nn.Module):
def __init__(self, input_dim=4, embed_dim=248, act_function=torch.sin):
assert embed_dim % input_dim == 0
super(Time2Vec, self).__init__()
self.enabled = embed_dim > 0
if self.enabled:
self.embed_dim = embed_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch im... | bernhein/spacetimeformer | Time2Vec | false | 1,544 | [
"MIT"
] | 0 | de252b68085943d979606fe69e177ac2a14586e7 | https://github.com/bernhein/spacetimeformer/tree/de252b68085943d979606fe69e177ac2a14586e7 |
HardSwish | import torch
import torch.nn as nn
class HardSwish(nn.Module):
def __init__(self):
super().__init__()
self.relu6 = nn.ReLU6()
def forward(self, x):
return self.relu6(x + 3.0) / 6.0 * x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | binyao2020/ElegantRL | HardSwish | false | 1,545 | [
"Apache-2.0"
] | 0 | bf79f0d071d00cd93be03f1ca005020c3ab8dfe0 | https://github.com/binyao2020/ElegantRL/tree/bf79f0d071d00cd93be03f1ca005020c3ab8dfe0 |
RMSELoss | import torch
import torch as th
import torch.nn as nn
class RMSELoss(nn.Module):
def __init__(self, reduction='mean', eps=1e-06):
super().__init__()
self.mse = nn.MSELoss(reduction=reduction)
self.eps = eps
def forward(self, yhat, y):
loss = th.sqrt(self.mse(yhat, y) + self.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 libdevice
import torch.nn as nn
assert... | bio-ontology-research-group/OntoML | RMSELoss | false | 1,546 | [
"BSD-3-Clause"
] | 0 | 4cdc17dc7ee26464db96c67838c3e77dba5318f9 | https://github.com/bio-ontology-research-group/OntoML/tree/4cdc17dc7ee26464db96c67838c3e77dba5318f9 |
GumbelQuantize | import torch
from torch import nn
from torch import einsum
import torch.nn.functional as F
class GumbelQuantize(nn.Module):
"""
Gumbel Softmax trick quantizer
Categorical Reparameterization with Gumbel-Softmax, Jang et al. 2016
https://arxiv.org/abs/1611.01144
"""
def __init__(self, num_hidde... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | baudm/deep-vector-quantization | GumbelQuantize | false | 1,547 | [
"MIT"
] | 0 | 211bda99a6c750c1e65aff082aa865fed8677b8a | https://github.com/baudm/deep-vector-quantization/tree/211bda99a6c750c1e65aff082aa865fed8677b8a |
TwoMLPHead | import torch
import torch.utils.data
from torch import nn
from torchvision.transforms import functional as F
import torch.nn.functional as F
class TwoMLPHead(nn.Module):
"""
Heads for FPN for classification
"""
def __init__(self, in_channels, representation_size):
super(TwoMLPHead, self).__in... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
from ... | billchenxi/vision | TwoMLPHead | false | 1,548 | [
"BSD-3-Clause"
] | 0 | 5608b06a6fbc9c84284dad9f07a16df61da10f85 | https://github.com/billchenxi/vision/tree/5608b06a6fbc9c84284dad9f07a16df61da10f85 |
QNetwork | import torch
import torch.nn.functional as F
import torch.nn as nn
class QNetwork(nn.Module):
"""Actor (Policy) Model."""
def __init__(self, state_size, action_size, seed):
"""Initialize parameters and build model.
Params
======
state_size (int): Dimension of each state
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | bhattsachin/deep-reinforcement-learning | QNetwork | false | 1,549 | [
"MIT"
] | 0 | 4d75b012495009bf156273e170d75caf400fa7aa | https://github.com/bhattsachin/deep-reinforcement-learning/tree/4d75b012495009bf156273e170d75caf400fa7aa |
GlobalpoolFC | import torch
from torch import nn
class GlobalpoolFC(nn.Module):
def __init__(self, num_in, num_class):
super(GlobalpoolFC, self).__init__()
self.pool = nn.AdaptiveAvgPool2d(output_size=1)
self.fc = nn.Linear(num_in, num_class)
def forward(self, x):
rep = self.pool(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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | blackcow/Fair-AT | GlobalpoolFC | false | 1,550 | [
"Apache-2.0"
] | 0 | 62fc269fedd4b63c4b48ae390d494b3832e65fa8 | https://github.com/blackcow/Fair-AT/tree/62fc269fedd4b63c4b48ae390d494b3832e65fa8 |
FairLoss | import torch
from torch import nn
class FairLoss(nn.Module):
def __init__(self, lamda):
super(FairLoss, self).__init__()
self.lamda = lamda
def forward(self, rep):
logits = torch.mm(rep, torch.transpose(rep, 0, 1))
logits = logits - torch.diag_embed(torch.diag(logits))
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch im... | blackcow/Fair-AT | FairLoss | false | 1,551 | [
"Apache-2.0"
] | 0 | 62fc269fedd4b63c4b48ae390d494b3832e65fa8 | https://github.com/blackcow/Fair-AT/tree/62fc269fedd4b63c4b48ae390d494b3832e65fa8 |
MockOpposite | import torch
import torch.nn as nn
class MockOpposite(nn.Module):
def __init__(self):
"""Initialize the model"""
super(MockOpposite, self).__init__()
def forward(self, input):
"""A single forward pass of the model. Returns input - 1.
Args:
input: The input to 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | blagojce95/ai-research-mamo-framework | MockOpposite | false | 1,552 | [
"Apache-2.0"
] | 0 | 7f3b5a5a9fb8b19c9eef453b81b03b6046a33bf2 | https://github.com/blagojce95/ai-research-mamo-framework/tree/7f3b5a5a9fb8b19c9eef453b81b03b6046a33bf2 |
PatchMerging | import torch
import torch.nn as nn
import torch.nn.functional as F
from math import sqrt
from torch import optim as optim
class PatchMerging(nn.Module):
"""Patch Merging Layer.
Args:
input_resolution (tuple[int]): Resolution of input feature.
dim (int): Number of input channels.
norm_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | billpsomas/ibot | PatchMerging | false | 1,553 | [
"Apache-2.0"
] | 0 | c6fbce7e2a59780f39ad7304ed9a8b1acf038d2d | https://github.com/billpsomas/ibot/tree/c6fbce7e2a59780f39ad7304ed9a8b1acf038d2d |
MyModel | import torch
import torch.nn as nn
class MyModel(nn.Module):
def __init__(self):
super(MyModel, self).__init__()
def forward(self, x):
return x + 1
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | blagojce95/ai-research-mamo-framework | MyModel | false | 1,554 | [
"Apache-2.0"
] | 0 | 7f3b5a5a9fb8b19c9eef453b81b03b6046a33bf2 | https://github.com/blagojce95/ai-research-mamo-framework/tree/7f3b5a5a9fb8b19c9eef453b81b03b6046a33bf2 |
GreedyTop1 | import torch
from typing import Optional
import torch as pt
import torch.distributed
import torch.distributed.elastic.multiprocessing.errors
class GreedyTop1(pt.nn.Module):
"""
Implements picking the highest scoring next word with support for vocabulary selection and target factors.
"""
def forward(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 as pt
import torch.distributed
import torch.distributed.elastic.multiprocessing.errors
assert_size_stride = torch._C._dynamo.gu... | blchu/sockeye | GreedyTop1 | false | 1,555 | [
"Apache-2.0"
] | 0 | 28044a44ee409c9b3df1711c0b16bdebdd463b2e | https://github.com/blchu/sockeye/tree/28044a44ee409c9b3df1711c0b16bdebdd463b2e |
PyTorchLHUC | import torch
import torch as pt
import torch.distributed
import torch.distributed.elastic.multiprocessing.errors
class PyTorchLHUC(pt.nn.Module):
"""
Learning Hidden Unit Contribution
David Vilar. "Learning Hidden Unit Contribution for Adapting Neural
Machine Translation Models" NAACL 2018
:para... | 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 as pt
import torch.distributed
import torch.distributed.elastic.multiprocessing.errors
assert_size_stride = torch._C._dynamo.gu... | blchu/sockeye | PyTorchLHUC | false | 1,556 | [
"Apache-2.0"
] | 0 | 28044a44ee409c9b3df1711c0b16bdebdd463b2e | https://github.com/blchu/sockeye/tree/28044a44ee409c9b3df1711c0b16bdebdd463b2e |
SE | import torch
from itertools import chain as chain
import torch.utils.data
import torch.nn as nn
class SwishEfficient(torch.autograd.Function):
"""Swish activation function: x * sigmoid(x)."""
@staticmethod
def forward(ctx, x):
result = x * torch.sigmoid(x)
ctx.save_for_backward(x)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from itertools import chain a... | billcai/SlowFast | SE | false | 1,557 | [
"Apache-2.0"
] | 0 | 778888e63351e55861801996b37c7ff9a3746587 | https://github.com/billcai/SlowFast/tree/778888e63351e55861801996b37c7ff9a3746587 |
SimpleNormLayer | import torch
import torch.nn as nn
class SimpleNormLayer(nn.Module):
"""Simple normalization layer that divides the output of a
preceding layer by a specified number
Parameters
----------
normalization_strength: float
The number with which input is normalized/dived by
"""
def __i... | 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... | bokutotu/cgnet | SimpleNormLayer | false | 1,558 | [
"BSD-3-Clause"
] | 0 | a35170001d969d51548dd01522b1ab93e43741b4 | https://github.com/bokutotu/cgnet/tree/a35170001d969d51548dd01522b1ab93e43741b4 |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self, feature_num):
super(Net, self).__init__()
self.layer_1 = nn.Linear(feature_num, 500)
self.layer_2 = nn.Linear(500, 20)
def forward(self, x):
x = F.relu(self.layer_1(x))... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | bm2-lab/MDML | Net | false | 1,559 | [
"MIT"
] | 0 | 222fb22b2ee53dd3c1a6f2e99a88f71e9635e3a0 | https://github.com/bm2-lab/MDML/tree/222fb22b2ee53dd3c1a6f2e99a88f71e9635e3a0 |
PyTorchSSRU | import torch
from typing import Tuple
from abc import abstractmethod
import torch as pt
import torch.distributed
import torch.distributed.elastic.multiprocessing.errors
class AutoregressiveLayer(pt.nn.Module):
@property
@abstractmethod
def num_state_tensors(self) ->int:
""" Number of state tensor... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 typing import Tuple
from... | blchu/sockeye | PyTorchSSRU | false | 1,560 | [
"Apache-2.0"
] | 0 | 28044a44ee409c9b3df1711c0b16bdebdd463b2e | https://github.com/blchu/sockeye/tree/28044a44ee409c9b3df1711c0b16bdebdd463b2e |
ShiftedSoftplus | import torch
import numpy as np
import torch.nn as nn
class ShiftedSoftplus(nn.Module):
""" Shifted softplus (SSP) activation function
SSP originates from the softplus function:
y = \\ln\\left(1 + e^{-x}\\right)
Schütt et al. (2018) introduced a shifting factor to the function in order
to 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.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.gu... | bokutotu/cgnet | ShiftedSoftplus | false | 1,561 | [
"BSD-3-Clause"
] | 0 | a35170001d969d51548dd01522b1ab93e43741b4 | https://github.com/bokutotu/cgnet/tree/a35170001d969d51548dd01522b1ab93e43741b4 |
PatchEmbed | import torch
from itertools import chain as chain
import torch.utils.data
import torch.nn as nn
class PatchEmbed(nn.Module):
"""
PatchEmbed.
"""
def __init__(self, dim_in=3, dim_out=768, kernel=(1, 16, 16), stride=(1,
4, 4), padding=(1, 7, 7), conv_2d=False):
super().__init__()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from itertools import chain as chain
import torch.utils.data
import torch.nn as ... | billcai/SlowFast | PatchEmbed | false | 1,562 | [
"Apache-2.0"
] | 0 | 778888e63351e55861801996b37c7ff9a3746587 | https://github.com/billcai/SlowFast/tree/778888e63351e55861801996b37c7ff9a3746587 |
NeighborNormLayer | import torch
import torch.nn as nn
class NeighborNormLayer(nn.Module):
"""Normalization layer that divides the output of a
preceding layer by the number of neighbor features.
Unlike the SimpleNormLayer, this layer allows for
dynamically changing number of neighbors during training.
"""
def __... | 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... | bokutotu/cgnet | NeighborNormLayer | false | 1,563 | [
"BSD-3-Clause"
] | 0 | a35170001d969d51548dd01522b1ab93e43741b4 | https://github.com/bokutotu/cgnet/tree/a35170001d969d51548dd01522b1ab93e43741b4 |
MultiheadSimilarity | import torch
import torch.utils.data
from torch import nn
class MultiheadSimilarity(nn.Module):
def __init__(self, d_model, num_head, seq_len, in_proj=True):
super().__init__()
self.num_head = num_head
self.seq_len = seq_len
self.d_head = d_model // num_head
self.in_proj =... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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
from torch import nn
assert_size_stride = torch._C._dyna... | bladewaltz1/clipvid-tmp | MultiheadSimilarity | false | 1,564 | [
"MIT"
] | 0 | 8a4a990c318fdfbf6dac443abd3bc16637abba3d | https://github.com/bladewaltz1/clipvid-tmp/tree/8a4a990c318fdfbf6dac443abd3bc16637abba3d |
ResidualBlock | import torch
from torch import nn
class ResidualBlock(nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
self.in_channels, self.out_channels = in_channels, out_channels
self.blocks = nn.Identity()
self.shortcut = nn.Identity()
self.should_apply_s... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
@triton.jit
def triton_poi_fused_add_0(in_ptr0, out_p... | boxiXia/pytorch_sac | ResidualBlock | false | 1,565 | [
"MIT"
] | 0 | ad570845c482498769217b398c22fafaff2ff2f1 | https://github.com/boxiXia/pytorch_sac/tree/ad570845c482498769217b398c22fafaff2ff2f1 |
ChannelAttention | import torch
import torch.nn as nn
class ChannelAttention(nn.Module):
def __init__(self, in_planes=96, ratio=16):
super(ChannelAttention, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.max_pool = nn.AdaptiveMaxPool2d(1)
self.fc1 = nn.Conv2d(in_planes, in_planes // r... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | biolee3/SAMDNet | ChannelAttention | false | 1,566 | [
"MIT"
] | 0 | 9a0d70f976e22d512046b4aa5727dd26422d0aff | https://github.com/biolee3/SAMDNet/tree/9a0d70f976e22d512046b4aa5727dd26422d0aff |
MiniBatchStddevLayer | import torch
import torch.nn as nn
import torch.distributed as dist
import torch.autograd as autograd
import torch.utils.cpp_extension
class AllGatherLayer(autograd.Function):
"""All gather layer with backward propagation path.
Indeed, this module is to make ``dist.all_gather()`` in the backward graph.
S... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.distributed as dist
import torch.autograd as... | bladesaber/mmgeneration | MiniBatchStddevLayer | false | 1,567 | [
"Apache-2.0"
] | 0 | 158b49f7efd8028f231f6e9ca758ae0e20dd72ae | https://github.com/bladesaber/mmgeneration/tree/158b49f7efd8028f231f6e9ca758ae0e20dd72ae |
SigmoidFocalLoss | import torch
import torch.nn as nn
from torch.nn import functional as F
def sigmoid_focal_loss(inputs: 'torch.Tensor', targets: 'torch.Tensor',
alpha: 'float'=-1, gamma: 'float'=2, reduction: 'str'='none'
) ->torch.Tensor:
"""
Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.0200... | 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... | botkop/lark | SigmoidFocalLoss | false | 1,568 | [
"Apache-2.0"
] | 0 | edb2defdb514213fc121418578b0d9006a55f3a0 | https://github.com/botkop/lark/tree/edb2defdb514213fc121418578b0d9006a55f3a0 |
LSTM | import torch
import torch.nn as nn
class LSTM(nn.Module):
def __init__(self, seq_length, input_dim, num_hidden, num_classes,
batch_size, device='cpu'):
super(LSTM, self).__init__()
self.seq_length = seq_length
self.h_init = nn.Parameter(torch.zeros(num_hidden, 1),
requ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | askliar/deep_learning | LSTM | false | 1,569 | [
"MIT"
] | 0 | e61b2391a3258d18719bf12d9ed1404620ce6c02 | https://github.com/askliar/deep_learning/tree/e61b2391a3258d18719bf12d9ed1404620ce6c02 |
PatchEmbed | import torch
import torch.nn as nn
from torch import optim as optim
class PatchEmbed(nn.Module):
""" Image to Patch Embedding
"""
def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
super().__init__()
num_patches = img_size // patch_size * (img_size // patch_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
from torch import optim as optim
assert_size_stride = torc... | billpsomas/ibot | PatchEmbed | false | 1,570 | [
"Apache-2.0"
] | 0 | c6fbce7e2a59780f39ad7304ed9a8b1acf038d2d | https://github.com/billpsomas/ibot/tree/c6fbce7e2a59780f39ad7304ed9a8b1acf038d2d |
FocalLoss | import torch
import torch.nn as nn
class FocalLoss(nn.Module):
def __init__(self, alpha: 'float'=0.25, gamma: 'float'=2.0, reduction:
'str'='mean'):
super().__init__()
self.bce = nn.BCEWithLogitsLoss(reduction='none')
self.alpha = alpha
self.gamma = gamma
if reduct... | 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... | botkop/lark | FocalLoss | false | 1,571 | [
"Apache-2.0"
] | 0 | edb2defdb514213fc121418578b0d9006a55f3a0 | https://github.com/botkop/lark/tree/edb2defdb514213fc121418578b0d9006a55f3a0 |
SReLU | import torch
import torch.nn as nn
class SReLU(nn.Module):
"""Shifted ReLU"""
def __init__(self, nc):
super(SReLU, self).__init__()
self.srelu_bias = nn.Parameter(torch.Tensor(1, nc, 1, 1))
self.srelu_relu = nn.ReLU(inplace=True)
nn.init.constant_(self.srelu_bias, -1.0)
d... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | bpicnbnk/ISONet | SReLU | false | 1,572 | [
"MIT"
] | 0 | 993150fa215dbc92460142ade59a3a5ec4a0efd9 | https://github.com/bpicnbnk/ISONet/tree/993150fa215dbc92460142ade59a3a5ec4a0efd9 |
AdaptiveInstanceNorm | import torch
import torch.nn as nn
from torch.nn.init import _calculate_correct_fan
import torch.utils.cpp_extension
def equalized_lr(module, name='weight', gain=2 ** 0.5, mode='fan_in',
lr_mul=1.0):
"""Equalized Learning Rate.
This trick is proposed in:
Progressive Growing of GANs for Improved Quali... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | bladesaber/mmgeneration | AdaptiveInstanceNorm | false | 1,573 | [
"Apache-2.0"
] | 0 | 158b49f7efd8028f231f6e9ca758ae0e20dd72ae | https://github.com/bladesaber/mmgeneration/tree/158b49f7efd8028f231f6e9ca758ae0e20dd72ae |
PANNsLoss | import torch
import torch.nn as nn
from torch.nn import functional as F
class PANNsLoss(nn.Module):
def __init__(self):
super().__init__()
@staticmethod
def stabilize_input(cwo):
cwo = torch.where(torch.isnan(cwo), torch.zeros_like(cwo), cwo)
cwo = torch.where(torch.isinf(cwo), t... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | botkop/lark | PANNsLoss | false | 1,574 | [
"Apache-2.0"
] | 0 | edb2defdb514213fc121418578b0d9006a55f3a0 | https://github.com/botkop/lark/tree/edb2defdb514213fc121418578b0d9006a55f3a0 |
SigmoidFocalLossStar | import torch
import torch.nn as nn
from torch.nn import functional as F
def sigmoid_focal_loss_star(inputs: 'torch.Tensor', targets: 'torch.Tensor',
alpha: 'float'=-1, gamma: 'float'=1, reduction: 'str'='none'
) ->torch.Tensor:
"""
FL* described in RetinaNet paper Appendix: https://arxiv.org/abs/1708.... | 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... | botkop/lark | SigmoidFocalLossStar | false | 1,575 | [
"Apache-2.0"
] | 0 | edb2defdb514213fc121418578b0d9006a55f3a0 | https://github.com/botkop/lark/tree/edb2defdb514213fc121418578b0d9006a55f3a0 |
ModMBStddevLayer | import torch
import torch.nn as nn
import torch.distributed as dist
import torch.autograd as autograd
import torch.utils.cpp_extension
class AllGatherLayer(autograd.Function):
"""All gather layer with backward propagation path.
Indeed, this module is to make ``dist.all_gather()`` in the backward graph.
S... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.distributed as dist
import torch.autograd as... | bladesaber/mmgeneration | ModMBStddevLayer | false | 1,576 | [
"Apache-2.0"
] | 0 | 158b49f7efd8028f231f6e9ca758ae0e20dd72ae | https://github.com/bladesaber/mmgeneration/tree/158b49f7efd8028f231f6e9ca758ae0e20dd72ae |
CriticArchitecture | import torch
import numpy as np
from abc import ABC
import torch.nn.functional as F
from torch import nn
from torch.nn import init
def fan_in_init(tensor):
fan_in = tensor.size(1)
v = 1.0 / np.sqrt(fan_in)
init.uniform_(tensor, -v, v)
class Architecture(nn.Module, ABC):
def __init__(self):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
from abc i... | bootml/agent | CriticArchitecture | false | 1,577 | [
"Apache-2.0"
] | 0 | 84235db931d6e4ef956962961c619994898ebdd5 | https://github.com/bootml/agent/tree/84235db931d6e4ef956962961c619994898ebdd5 |
SelfGating | import torch
import torch.utils.data
import torch as th
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data.distributed
class SelfGating(nn.Module):
def __init__(self, input_dim):
super(SelfGating, self).__init__()
self.fc = nn.Linear(input_dim, input_dim)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch.nn as nn
import torch.nn.parallel
import to... | bryant1410/MIL-NCE_HowTo100M | SelfGating | false | 1,578 | [
"Apache-2.0"
] | 0 | 9ba876bd67160e24a5ce379a07d18a8036be0d36 | https://github.com/bryant1410/MIL-NCE_HowTo100M/tree/9ba876bd67160e24a5ce379a07d18a8036be0d36 |
LearnedPositionalEmbedding | import torch
import torch.nn as nn
import torch.nn.functional as F
class LearnedPositionalEmbedding(nn.Embedding):
"""
This module learns positional embeddings up to a fixed maximum size.
Padding ids are ignored by either offsetting based on padding_idx
or by setting padding_idx to None and ensuring t... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | boxiangliu/esm | LearnedPositionalEmbedding | false | 1,579 | [
"MIT"
] | 0 | 3c143d99103e0ea38a9455f30a73cd9c87376606 | https://github.com/boxiangliu/esm/tree/3c143d99103e0ea38a9455f30a73cd9c87376606 |
simple_mlp | import torch
import torch.nn as nn
from torch.nn import functional as F
class simple_mlp(nn.Module):
def __init__(self, feature_dim, layer, hidden):
super(simple_mlp, self).__init__()
self.layer = layer
self.linear1 = nn.Linear(feature_dim, hidden)
if layer == 2:
self.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | broadinstitute/TCRP | simple_mlp | false | 1,580 | [
"MIT"
] | 0 | 9e580dbf0c9d0ec5e5b1a949087df5a3724fa35b | https://github.com/broadinstitute/TCRP/tree/9e580dbf0c9d0ec5e5b1a949087df5a3724fa35b |
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... | bugface/NeMo | MaskedInstanceNorm1d | false | 1,581 | [
"Apache-2.0"
] | 0 | 431c561380a120e9e164a4c9deed8f1ca9acace5 | https://github.com/bugface/NeMo/tree/431c561380a120e9e164a4c9deed8f1ca9acace5 |
LayerNorm | import torch
from torch import nn
class LayerNorm(nn.Module):
def __init__(self, d_model, eps=1e-12):
super(LayerNorm, self).__init__()
self.gamma = nn.Parameter(torch.ones(d_model))
self.beta = nn.Parameter(torch.zeros(d_model))
self.eps = eps
def forward(self, x):
m... | 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... | bsgiovanini/transformer | LayerNorm | false | 1,582 | [
"Apache-2.0"
] | 0 | e128fa862f1b3d17d7b92df169a2bbee3f08366f | https://github.com/bsgiovanini/transformer/tree/e128fa862f1b3d17d7b92df169a2bbee3f08366f |
Actor | import torch
import numpy as np
import torch.nn as nn
def fanin_init(size, fanin=None):
fanin = fanin or size[0]
v = 1.0 / np.sqrt(fanin)
return torch.Tensor(size).uniform_(-v, v)
class Actor(nn.Module):
def __init__(self, s_dim, a_dim):
super(Actor, self).__init__()
self.forward1 =... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | brooky56/DeepRL-UR-motion-planning | Actor | false | 1,583 | [
"MIT"
] | 0 | 0cc523da6d8a55896773f1f57feed1f0c77fea78 | https://github.com/brooky56/DeepRL-UR-motion-planning/tree/0cc523da6d8a55896773f1f57feed1f0c77fea78 |
ScaleDotProductAttention | import math
import torch
from torch import nn
class ScaleDotProductAttention(nn.Module):
"""
compute scale dot product attention
Query : given sentence that we focused on (decoder)
Key : every sentence to check relationship with Qeury(encoder)
Value : every sentence same with Key (encoder)
""... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | bsgiovanini/transformer | ScaleDotProductAttention | false | 1,584 | [
"Apache-2.0"
] | 0 | e128fa862f1b3d17d7b92df169a2bbee3f08366f | https://github.com/bsgiovanini/transformer/tree/e128fa862f1b3d17d7b92df169a2bbee3f08366f |
EqualLinearActModule | import torch
import torch.nn as nn
from copy import deepcopy
from functools import partial
from torch.nn.init import _calculate_correct_fan
import torch.utils.cpp_extension
def equalized_lr(module, name='weight', gain=2 ** 0.5, mode='fan_in',
lr_mul=1.0):
"""Equalized Learning Rate.
This trick is propose... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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 copy import deepcopy
from functools import partial
fr... | bladesaber/mmgeneration | EqualLinearActModule | false | 1,585 | [
"Apache-2.0"
] | 0 | 158b49f7efd8028f231f6e9ca758ae0e20dd72ae | https://github.com/bladesaber/mmgeneration/tree/158b49f7efd8028f231f6e9ca758ae0e20dd72ae |
NPairLoss | import torch
class NPairLoss(torch.nn.Module):
def __init__(self, l2=0.05):
"""
Basic N-Pair Loss as proposed in 'Improved Deep Metric Learning with Multi-class N-pair Loss Objective'
Args:
l2: float, weighting parameter for weight penality due to embeddings not being normaliz... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | bm2-lab/MDML | NPairLoss | false | 1,586 | [
"MIT"
] | 0 | 222fb22b2ee53dd3c1a6f2e99a88f71e9635e3a0 | https://github.com/bm2-lab/MDML/tree/222fb22b2ee53dd3c1a6f2e99a88f71e9635e3a0 |
FunctionalRelu6 | import torch
class FunctionalRelu6(torch.nn.Module):
def forward(self, x):
return torch.nn.functional.relu6(x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | bunderhi/torch2trt | FunctionalRelu6 | false | 1,587 | [
"MIT"
] | 0 | fa5e31e742a0f0c9a9ee38909a6fa56bb07ba96d | https://github.com/bunderhi/torch2trt/tree/fa5e31e742a0f0c9a9ee38909a6fa56bb07ba96d |
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