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 |
|---|---|---|---|---|---|---|---|---|---|---|
ScaledL2Norm | import torch
import torch.onnx
import torch
import torch.nn as nn
import torch.nn.functional as F
class ScaledL2Norm(nn.Module):
def __init__(self, in_channels, initial_scale):
super(ScaledL2Norm, self).__init__()
self.in_channels = in_channels
self.scale = nn.Parameter(torch.Tensor(in_ch... | 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.onnx
import tor... | SoonminHwang/pytorch-ssd | ScaledL2Norm | false | 9,498 | [
"MIT"
] | 0 | 1d6b9427a4b649bc2ce85a82511b9dd299f9d3e8 | https://github.com/SoonminHwang/pytorch-ssd/tree/1d6b9427a4b649bc2ce85a82511b9dd299f9d3e8 |
Discriminator2 | import torch
import torch.utils.data
import torch.nn as nn
class Discriminator2(nn.Module):
def __init__(self, n_h):
super(Discriminator2, self).__init__()
self.f_k = nn.Bilinear(n_h, n_h, 1)
for m in self.modules():
self.weights_init(m)
def weights_init(self, 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
import torch.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | XrosLiang/GraphCL | Discriminator2 | false | 5,996 | [
"MIT"
] | 1 | fdf9fabcdaddbc17e5c8b7ac9e9d2bdfe4acc56c | https://github.com/XrosLiang/GraphCL/tree/fdf9fabcdaddbc17e5c8b7ac9e9d2bdfe4acc56c |
TorchClampOptionMax | import torch
class TorchClampOptionMax(torch.nn.Module):
def forward(self, x):
return torch.clamp(x, max=0.1)
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
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | NVIDIA-AI-IOT-private/torch2trt | TorchClampOptionMax | false | 10,544 | [
"MIT"
] | 0 | 953d60039e0c81e90eea467c3df2e6e3f7040242 | https://github.com/NVIDIA-AI-IOT-private/torch2trt/tree/953d60039e0c81e90eea467c3df2e6e3f7040242 |
Ones | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.optim
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strid... | ai-in-motion/moai | Ones | false | 18,341 | [
"Apache-2.0"
] | 10 | e38cac046c059d2e2331ef4883bbabc5a500a5cf | https://github.com/ai-in-motion/moai/tree/e38cac046c059d2e2331ef4883bbabc5a500a5cf |
FocalLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | 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... | krisk84/retinanet-examples | FocalLoss | false | 12,685 | [
"BSD-3-Clause"
] | 0 | 174d95f3aabe1746d105c66f87aa445607f4eab8 | https://github.com/krisk84/retinanet-examples/tree/174d95f3aabe1746d105c66f87aa445607f4eab8 |
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... | dodo0822/PyTorch_Speaker_Verification | GE2ELoss | false | 3,447 | [
"BSD-3-Clause"
] | 0 | 5310f441894e77895de27380d31149629e309d0f | https://github.com/dodo0822/PyTorch_Speaker_Verification/tree/5310f441894e77895de27380d31149629e309d0f |
AttentionLayer | import torch
import torch.nn as nn
import torch.nn.functional as F
class AttentionLayer(nn.Module):
def __init__(self, hidden_size):
super(AttentionLayer, self).__init__()
self.hidden_size = hidden_size
def dot_product_attention(self, hidden, encoder_output):
return torch.sum(hidden ... | 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
... | u7javed/AI-Chatbot | AttentionLayer | false | 10,873 | [
"MIT"
] | 0 | d86916537e7b0b9a45f11d0fe0367fe9f66721e7 | https://github.com/u7javed/AI-Chatbot/tree/d86916537e7b0b9a45f11d0fe0367fe9f66721e7 |
L2Norm | import torch
import torch.nn as nn
class L2Norm(nn.Module):
"""
Scale shall be learnable according to original paper
scale: initial scale number
chan_num: L2Norm channel number (norm over all channels)
"""
def __init__(self, scale=20, chan_num=512):
super(L2Norm, self).__init... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | EldritchJS/inference_results_v0.5 | L2Norm | false | 409 | [
"Apache-2.0"
] | 0 | 5552490e184d9fc342d871fcc410ac423ea49053 | https://github.com/EldritchJS/inference_results_v0.5/tree/5552490e184d9fc342d871fcc410ac423ea49053 |
SpatialGate | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | HolmesShuan/OISR-PyTorch | SpatialGate | false | 13,793 | [
"BSD-2-Clause"
] | 141 | bbe0c88f71fe565a2842df7971b62a9bc5a56c48 | https://github.com/HolmesShuan/OISR-PyTorch/tree/bbe0c88f71fe565a2842df7971b62a9bc5a56c48 |
MSELoss | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.nn.functional as F
class MSELoss(nn.Module):
def __init__(self, ratio=1, size_average=None, reduce=None, reduction=
'mean'):
super(MSELoss, self).__init__()
self.ratio = rat... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data... | Dogacel/mmfashion | MSELoss | false | 11,413 | [
"Apache-2.0"
] | 0 | e49613245c8501042edd7aeeaa8fb93e5ea13238 | https://github.com/Dogacel/mmfashion/tree/e49613245c8501042edd7aeeaa8fb93e5ea13238 |
Concat | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | 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
import torch.utils.data
import torch.utils.tensorboard._pytorch_graph
import torch.onnx.symbolic_caffe2
assert_size_stride =... | arjunsuresh/aimet | Concat | false | 12,326 | [
"BSD-3-Clause"
] | 0 | f6e09cb07a91eed3a5e6b8e19e6b065303af5a39 | https://github.com/arjunsuresh/aimet/tree/f6e09cb07a91eed3a5e6b8e19e6b065303af5a39 |
SpatialGatherModule | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch._C
import torch.serialization
class SpatialGatherModule(nn.Module):
"""Aggregate the context features according to the initial predicted
probability distribution.
Employ the soft-weighted method to aggregate the context.
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | HusterRC/mmsegmentation | SpatialGatherModule | false | 5,310 | [
"Apache-2.0"
] | 1 | c3e4dbc2e06de3f47f75098f76772b4ee7e91e35 | https://github.com/HusterRC/mmsegmentation/tree/c3e4dbc2e06de3f47f75098f76772b4ee7e91e35 |
ConveRTOuterFeedForward | import torch
import torch.nn as nn
import torch.nn.functional as fnn
from torch.nn.modules.normalization import LayerNorm
class ConveRTOuterFeedForward(nn.Module):
"""Fully-Connected 3-layer Linear Model"""
def __init__(self, input_hidden: 'int', intermediate_hidden: 'int',
dropout_rate: 'float'=0.0)... | 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.triton_helpers import libdevice
import torch.nn as ... | luweishuang/ConveRT-pytorch | ConveRTOuterFeedForward | false | 10,569 | [
"Apache-2.0"
] | 0 | e14aaf2287eb3a78ee7d83ea02d9bd322863227f | https://github.com/luweishuang/ConveRT-pytorch/tree/e14aaf2287eb3a78ee7d83ea02d9bd322863227f |
Pool | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | 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... | borisfom/TRTorch | Pool | false | 9,792 | [
"BSD-3-Clause"
] | 0 | 1660633c6f6a480cd123d9d91cabf4eced12e8f3 | https://github.com/borisfom/TRTorch/tree/1660633c6f6a480cd123d9d91cabf4eced12e8f3 |
MultiHeadAttention | import math
import torch
import torch.nn as nn
def scaled_dot_product_attention(query, keys, values, mask=None):
d_k = keys.shape[-1]
dot_score = query @ keys.transpose(-2, -1) / math.sqrt(d_k)
if mask is not None:
dot_score = dot_score.masked_fill(mask == 0, -1000000000.0)
attn_score = torch.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | NathanYanJing/TransformerReplication | MultiHeadAttention | false | 11,748 | [
"MIT"
] | 0 | b20f987dcc507724971f843c2d214c9c76bd8e34 | https://github.com/NathanYanJing/TransformerReplication/tree/b20f987dcc507724971f843c2d214c9c76bd8e34 |
GradientReversal | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | 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 |
ConcatPool2d | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | 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... | davidleonfdez/face2anime | ConcatPool2d | false | 1,801 | [
"MIT"
] | 0 | 896bf85a7aa28322cc9e9e586685db8cbbf39d89 | https://github.com/davidleonfdez/face2anime/tree/896bf85a7aa28322cc9e9e586685db8cbbf39d89 |
LRN | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | 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_... | PengJingchao/DFNet | LRN | false | 937 | [
"MIT"
] | 0 | 49e83501f81515aebca211351e315896da7afc54 | https://github.com/PengJingchao/DFNet/tree/49e83501f81515aebca211351e315896da7afc54 |
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_1 = nn.Conv2d(1, 32, kernel_size=5, padding=2)
self.prelu1_1 = nn.PReLU()
self.conv1_2 = nn.Conv2d(32, 32, kernel_size=5, padding=2)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | jxgu1016/MNIST_with_centerloss.pytorch | Net | false | 15,812 | [
"MIT"
] | 346 | 4e94cc77fe94056a7f1f081fcaf0325781ba0224 | https://github.com/jxgu1016/MNIST_with_centerloss.pytorch/tree/4e94cc77fe94056a7f1f081fcaf0325781ba0224 |
LayerScaleBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | yifanc96/yifanc-DL | LayerScaleBlock | false | 11,101 | [
"MIT"
] | 0 | 25d56cec776fb151c8f6bcbd997bca94f07f3597 | https://github.com/yifanc96/yifanc-DL/tree/25d56cec776fb151c8f6bcbd997bca94f07f3597 |
LocalResponseNormLayer | import torch
import torch.nn as nn
import torch.nn.functional as F
class LocalResponseNormLayer(nn.Module):
def forward(self, tensor, size=5, alpha=9.999999747378752e-05, beta=
0.75, k=1.0):
return F.local_response_norm(tensor, size=size, alpha=alpha, beta=
beta, k=k)
def get_inputs... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | a-kore/lucent | LocalResponseNormLayer | false | 1,334 | [
"Apache-2.0"
] | 0 | 6b2b4dfea45c36c99e16f9923104a532df80e0a8 | https://github.com/a-kore/lucent/tree/6b2b4dfea45c36c99e16f9923104a532df80e0a8 |
C51ValueNetwork | import torch
import numpy as np
import torch.nn as nn
class C51ValueNetwork(nn.Module):
"""Critic - return Q value from given states and actions. """
def __init__(self, num_states, num_actions, hidden_size, v_min, v_max,
num_atoms, device='cuda'):
"""
Args:
num_states (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
from torch._inductor.runtime import triton_helpers
import numpy as np
import tor... | HzcIrving/DLRL_PlayGround | C51ValueNetwork | false | 8,286 | [
"MIT"
] | 27 | 0db9a4bdb87130d1d26aea1591ef74cbe6aaa43b | https://github.com/HzcIrving/DLRL_PlayGround/tree/0db9a4bdb87130d1d26aea1591ef74cbe6aaa43b |
BertSelfAttention | from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn
class BertSelfAttention(nn.Module):
def __init__(self, config):
super(BertSelfAttention, self).__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | minjoong507/Image-Captioning-Transformer | BertSelfAttention | false | 7,247 | [
"MIT"
] | 1 | 813060f0bb656e336154173f11e99a80362c8c2a | https://github.com/minjoong507/Image-Captioning-Transformer/tree/813060f0bb656e336154173f11e99a80362c8c2a |
GatedConvTranspose | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | ClaraBing/ffjord | GatedConvTranspose | false | 13,520 | [
"MIT"
] | 518 | a97c34ff546a063316828f53bd041555e663428d | https://github.com/ClaraBing/ffjord/tree/a97c34ff546a063316828f53bd041555e663428d |
CategoricalSampler | import torch
import torch.nn as nn
class Sampler(nn.Module):
""" args; logits: (batch, n_nodes)
return; next_node: (batch, 1)
TopKSampler <=> greedy; sample one with biggest probability
CategoricalSampler <=> sampling; randomly sample one from possible distribution based on probability
"""
def __init_... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | TSLNIHAOGIT/VRP_DRL_MHA | CategoricalSampler | false | 14,457 | [
"MIT"
] | 55 | 6a59918ffb815fbdab4d75cb78130fc638c64d69 | https://github.com/TSLNIHAOGIT/VRP_DRL_MHA/tree/6a59918ffb815fbdab4d75cb78130fc638c64d69 |
LastBlock | import torch
import numpy as np
import torch.nn as nn
class BatchNormLayer(nn.Module):
"""Implements batch normalization layer."""
def __init__(self, channels, gamma=False, beta=True, decay=0.9, epsilon
=1e-05):
"""Initializes with basic settings.
Args:
channels: Number of channels... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import numpy as np
import torch.nn as nn
assert_size_stride = torch._C._dynamo.g... | thunguyenphuoc/idinvert_pytorch | LastBlock | false | 13,133 | [
"MIT"
] | 0 | bf8a81e75d193c22a05d9c4457907dc468389766 | https://github.com/thunguyenphuoc/idinvert_pytorch/tree/bf8a81e75d193c22a05d9c4457907dc468389766 |
AgreementRouting | import torch
import torch.nn as nn
import torch.nn.functional as F
def squash(x):
lengths2 = x.pow(2).sum(dim=2)
lengths = lengths2.sqrt()
x = x * (lengths2 / (1 + lengths2) / lengths).view(x.size(0), x.size(1), 1)
return x
class AgreementRouting(nn.Module):
def __init__(self, input_caps, outpu... | 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... | bentrevett/capsules | AgreementRouting | false | 3,237 | [
"MIT"
] | 0 | 239273de25c607d7a7504e8c6900772fddd15cd3 | https://github.com/bentrevett/capsules/tree/239273de25c607d7a7504e8c6900772fddd15cd3 |
SigmoidRange | from torch.nn import Module
import functools
import torch
import torch.nn as nn
from typing import *
def sigmoid_range(x, low, high):
"""Sigmoid function with range `(low, high)`"""
return torch.sigmoid(x) * (high - low) + low
class PrePostInitMeta(type):
"""A metaclass that calls optional `__pre_init__... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
import functools
import torch.nn as nn
from typing import *
assert_size_stride = torch._C._dynamo.guards.assert_... | davidpfahler/fastai_dev | SigmoidRange | false | 10,053 | [
"Apache-2.0"
] | 0 | a86b15f86138a9902e8649e3f745e76a19139ab3 | https://github.com/davidpfahler/fastai_dev/tree/a86b15f86138a9902e8649e3f745e76a19139ab3 |
NormLayer | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | 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... | LMdeLiangMi/captum | NormLayer | false | 5,472 | [
"BSD-3-Clause"
] | 1 | 8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | https://github.com/LMdeLiangMi/captum/tree/8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 |
LearnedUpsampling1d | import torch
from torch import nn
class LearnedUpsampling1d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, bias=True):
super().__init__()
self.conv_t = nn.ConvTranspose1d(in_channels=in_channels,
out_channels=out_channels, kernel_size=kernel_size, stride=
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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... | fdb/samplernn-pytorch | LearnedUpsampling1d | false | 15,345 | [
"MIT"
] | 259 | 87ce71cc2cf26601a271648597f198df33059f96 | https://github.com/fdb/samplernn-pytorch/tree/87ce71cc2cf26601a271648597f198df33059f96 |
NaiveGroupNorm | from torch.nn import Module
import torch
from torch.nn import Parameter
from torch.nn import init
import torch.nn.parallel
class NaiveGroupNorm(Module):
"""NaiveGroupNorm implements Group Normalization with the high-level matrix operations in PyTorch.
It is a temporary solution to export GN by ONNX before the... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch.nn import Module
from torch.nn import Parameter
from torch.nn import... | EllisHui/outOfRailWay | NaiveGroupNorm | false | 427 | [
"BSD-2-Clause"
] | 0 | e3bf9aaa18879bee5536740d55006c872f06278f | https://github.com/EllisHui/outOfRailWay/tree/e3bf9aaa18879bee5536740d55006c872f06278f |
QRLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | ethanwhite/torchgeo | QRLoss | false | 15,313 | [
"MIT"
] | 678 | cb20e1abfd9213f9ee7700df972385db13568642 | https://github.com/ethanwhite/torchgeo/tree/cb20e1abfd9213f9ee7700df972385db13568642 |
DeepSVDDLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | 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 functools import reduce
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | NjuHaoZhang/AutoregressModel-AE_VAD_CVPR2019 | DeepSVDDLoss | false | 8,595 | [
"MIT"
] | 12 | b9843f34ecb59f908d78ddf977ee4670e0ed6cb4 | https://github.com/NjuHaoZhang/AutoregressModel-AE_VAD_CVPR2019/tree/b9843f34ecb59f908d78ddf977ee4670e0ed6cb4 |
NLKProjection | import torch
from torch import nn
import torch.nn.functional as F
class TwoLayerNet(nn.Module):
def __init__(self, dim, hidden_dim, output_dim):
super(TwoLayerNet, self).__init__()
self.layer1 = nn.Linear(dim, hidden_dim)
self.layer2 = nn.Linear(hidden_dim, output_dim)
nn.init.xav... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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
import t... | HKUST-KnowComp/EFO-1-QA-benchmark | NLKProjection | false | 17,357 | [
"MIT"
] | 9 | 600fb02c76ab631f93ee362ceb789216ec085790 | https://github.com/HKUST-KnowComp/EFO-1-QA-benchmark/tree/600fb02c76ab631f93ee362ceb789216ec085790 |
UnpoolAvgEquiangular | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | 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
reinterpret... | ownzonefeng/weather_prediction | UnpoolAvgEquiangular | false | 7,426 | [
"MIT"
] | 1 | 723c02b6b3c0a40751d87572b66c7a4e040dec92 | https://github.com/ownzonefeng/weather_prediction/tree/723c02b6b3c0a40751d87572b66c7a4e040dec92 |
MinimaxDiscriminatorLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
def minimax_discriminator_loss(dx, dgz, label_smoothing=0.0, reduction='mean'):
target_ones = torch.ones_like(dgz) * (1.0 - label_smoothing)
target_zeros = torch.zeros_like(dx)
loss = F.binary_cross_entropy_with_logits(dx, target_ones, red... | 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... | torchgan/torchgan | MinimaxDiscriminatorLoss | false | 16,609 | [
"MIT"
] | 1,300 | f4139537ac2d3d8609d5aecc859a6fb797b107a1 | https://github.com/torchgan/torchgan/tree/f4139537ac2d3d8609d5aecc859a6fb797b107a1 |
PolicyNetwork | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | DensoITLab/spinningup_in_pytorch | PolicyNetwork | false | 7,955 | [
"MIT"
] | 11 | 612d8c4c6593c8c5ecb5a939bf43085daac9e552 | https://github.com/DensoITLab/spinningup_in_pytorch/tree/612d8c4c6593c8c5ecb5a939bf43085daac9e552 |
WeightedBinaryCrossEntropyLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | 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... | LaudateCorpus1/LIGA-Stereo | WeightedBinaryCrossEntropyLoss | false | 13,987 | [
"Apache-2.0"
] | 56 | aee3731a24a0ab1667e633e520cc89be2f135272 | https://github.com/LaudateCorpus1/LIGA-Stereo/tree/aee3731a24a0ab1667e633e520cc89be2f135272 |
TVLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | 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.... | GuoShi28/GCP-Net | TVLoss | false | 8,160 | [
"Apache-2.0"
] | 24 | cef7513fa242343055af64e612429e4384d3c1d7 | https://github.com/GuoShi28/GCP-Net/tree/cef7513fa242343055af64e612429e4384d3c1d7 |
ConvReg | import torch
import torch.nn as nn
class ConvReg(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(3, 64, 3, stride=2, padding=1)
self.conv2 = nn.Conv2d(64, 128, 3, stride=2, padding=1)
self.relu = nn.ReLU()
self.sigmoid = nn.Sigmoid()
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_... | Guru-Uni-siegen/Domain-Shifting-Network | ConvReg | false | 11,475 | [
"MIT"
] | 0 | dd9eb7bda07634874497a335151b5e967aaad874 | https://github.com/Guru-Uni-siegen/Domain-Shifting-Network/tree/dd9eb7bda07634874497a335151b5e967aaad874 |
PCK | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | 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.nn.functional
assert_size_stride = torch._C.... | miracleyoo/lifting_events_to_3d_hpe | PCK | false | 10,600 | [
"Apache-2.0"
] | 0 | dfe734ee055900d6ab90c064bf82db7672830ac7 | https://github.com/miracleyoo/lifting_events_to_3d_hpe/tree/dfe734ee055900d6ab90c064bf82db7672830ac7 |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
import torch.autograd
class Net(nn.Module):
def __init__(self, STATE_NUM, ACTION_NUM):
super(Net, self).__init__()
self.fc1 = nn.Linear(in_features=STATE_NUM, out_features=128)
self.fc2 = nn.Linear(in_fe... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | ChangQingAAS/Deep-Reinforcement-Learning | Net | false | 233 | [
"MIT"
] | 0 | 3bc1381c632b1730a48e63e972aea62086c4287c | https://github.com/ChangQingAAS/Deep-Reinforcement-Learning/tree/3bc1381c632b1730a48e63e972aea62086c4287c |
ScaleNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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_... | Piki1989/spacetimeformer | ScaleNorm | false | 14,190 | [
"MIT"
] | 209 | 7e0caf17dd03e5d25e2766c4f7132805779bcc40 | https://github.com/Piki1989/spacetimeformer/tree/7e0caf17dd03e5d25e2766c4f7132805779bcc40 |
SeparableConv2d_same | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
from torch.nn import functional as F
assert_size_stride = t... | Gummary/Pytorch-Project-Template | SeparableConv2d_same | false | 505 | [
"MIT"
] | 0 | 56bc5e253627d40fb8771eccdb2bb663c833beb3 | https://github.com/Gummary/Pytorch-Project-Template/tree/56bc5e253627d40fb8771eccdb2bb663c833beb3 |
AdditiveAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | AxlAlm/SegNLP | AdditiveAttention | false | 4,881 | [
"Apache-2.0"
] | 1 | 89b8d077952397dfcea089376b373b117bcf6a65 | https://github.com/AxlAlm/SegNLP/tree/89b8d077952397dfcea089376b373b117bcf6a65 |
TransposeGatedConv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | delldu/DeepFillv2 | TransposeGatedConv2d | false | 6,565 | [
"MIT"
] | 1 | a564b9589c1b42bcdddd3d7601f4059c4594a439 | https://github.com/delldu/DeepFillv2/tree/a564b9589c1b42bcdddd3d7601f4059c4594a439 |
ImageLinearAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | CUMLSec/stateformer | ImageLinearAttention | false | 7,916 | [
"MIT"
] | 41 | 87cb3c906c43fcff42b2ca820eb6e7fd918d0a1c | https://github.com/CUMLSec/stateformer/tree/87cb3c906c43fcff42b2ca820eb6e7fd918d0a1c |
CAModule | import torch
import torch.nn as nn
class CAModule(nn.Module):
"""
Re-implementation of Squeeze-and-Excitation (SE) block described in:
*Hu et al., Squeeze-and-Excitation Networks, arXiv:1709.01507*
code reference:
https://github.com/kobiso/CBAM-keras/blob/master/models/attention_module.py
"""
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | C3-ASV-Team/torchxrayvision | CAModule | false | 4,929 | [
"Apache-2.0"
] | 1 | 7e53f0606986562f17a1ffd9f31d006756eff78d | https://github.com/C3-ASV-Team/torchxrayvision/tree/7e53f0606986562f17a1ffd9f31d006756eff78d |
ScaledDotProductAttention | import torch
import torch.nn as nn
import torch.nn.functional as F
class ScaledDotProductAttention(nn.Module):
""" Scaled Dot-Product Attention """
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature = temperature
self.dropout = nn.Dropout(attn_dropo... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | RuaBQ/FEAT | ScaledDotProductAttention | false | 2,823 | [
"MIT"
] | 0 | e46f56b03f8ef820d549cb385600a12bdf224de9 | https://github.com/RuaBQ/FEAT/tree/e46f56b03f8ef820d549cb385600a12bdf224de9 |
FocalLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch ... | CAMP-eXplain-AI/imba-explain | FocalLoss | false | 2,040 | [
"MIT"
] | 0 | e41b4ca5de63955cb0e925aad9599f38c5a3e973 | https://github.com/CAMP-eXplain-AI/imba-explain/tree/e41b4ca5de63955cb0e925aad9599f38c5a3e973 |
CNN_MNIST | import torch
import torch.nn as nn
import torch.nn.functional as F
class CNN_MNIST(nn.Module):
def __init__(self, num_channels, num_classes):
super(CNN_MNIST, self).__init__()
self.conv1 = nn.Conv2d(num_channels, 32, 3, stride=1, padding=0)
self.conv2 = nn.Conv2d(32, 64, 3, stride=1, padd... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | Billy1900/Noise-Adaption-Layer | CNN_MNIST | false | 16,999 | [
"MIT"
] | 5 | 57b52dc4873f8eba7b8332db0ca3e593c2e3ffa8 | https://github.com/Billy1900/Noise-Adaption-Layer/tree/57b52dc4873f8eba7b8332db0ca3e593c2e3ffa8 |
FCN32s | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
from torch... | Yusoi/mmdetection | FCN32s | false | 9,960 | [
"Apache-2.0"
] | 0 | cbb5fb00f6e124fbb2c15e7e3438d7fa76b8850a | https://github.com/Yusoi/mmdetection/tree/cbb5fb00f6e124fbb2c15e7e3438d7fa76b8850a |
SAM_Module | import torch
import torch.nn as nn
from torchvision.transforms import *
class SAM_Module(nn.Module):
""" Position attention module"""
def __init__(self, channels):
super(SAM_Module, self).__init__()
self.relu = nn.ReLU(inplace=True)
self.conv_after_concat = nn.Conv2d(1, 1, kernel_size... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torchvision.transforms import *
assert_size_stride = ... | Vill-Lab/IGOAS | SAM_Module | false | 18,043 | [
"MIT"
] | 8 | 42ca1d45e441f993c95b5e8f33c9f97ea3b916f3 | https://github.com/Vill-Lab/IGOAS/tree/42ca1d45e441f993c95b5e8f33c9f97ea3b916f3 |
LinearAdditiveUpsample | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
import torch
import torch.nn as nn
assert_size_stride = torch._C.... | giuliabaldini/Pix2PixNIfTI | LinearAdditiveUpsample | false | 3,544 | [
"BSD-3-Clause"
] | 0 | 59ff825760f682d2734bd5e95503a03f80d32414 | https://github.com/giuliabaldini/Pix2PixNIfTI/tree/59ff825760f682d2734bd5e95503a03f80d32414 |
TransformerEncoderLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Lingzhi-WANG/Quotation-Recommendation | TransformerEncoderLayer | false | 17,595 | [
"MIT"
] | 4 | 40a875a41f10a597604206e067a16cbbfc88cdd7 | https://github.com/Lingzhi-WANG/Quotation-Recommendation/tree/40a875a41f10a597604206e067a16cbbfc88cdd7 |
MaxPoolStride1 | import torch
import torch.nn as nn
import torch.nn.functional as F
class MaxPoolStride1(nn.Module):
def __init__(self, kernel_size):
super(MaxPoolStride1, self).__init__()
self.kernel_size = kernel_size
self.pad = kernel_size - 1
def forward(self, x):
padded_x = F.pad(x, (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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | TCC-MonitoramentoInteligente/dev-tool | MaxPoolStride1 | false | 9,509 | [
"MIT"
] | 0 | d3a1d697c4ba7a5fff54be08541da4fc4811ab5e | https://github.com/TCC-MonitoramentoInteligente/dev-tool/tree/d3a1d697c4ba7a5fff54be08541da4fc4811ab5e |
Generator | import torch
from torch import nn
def gumbel_softmax(logits, tau=1.0, hard=False, log_mode=True, dim=-1):
while True:
gumbels = -torch.empty_like(logits).exponential_().log()
gumbels = (logits + gumbels) / tau
if log_mode:
y_soft = gumbels.log_softmax(dim)
else:
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | mosespv96/SCAPT-ABSA | Generator | false | 16,108 | [
"MIT"
] | 49 | 6f7f89a131127f262a8d1fd2774e5a96b58e7193 | https://github.com/mosespv96/SCAPT-ABSA/tree/6f7f89a131127f262a8d1fd2774e5a96b58e7193 |
PatchEmbed | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.nn.parallel
assert_size_stride = torch._C._dy... | QLSong/cv-classify | PatchEmbed | false | 2,749 | [
"Apache-2.0"
] | 0 | 02f53d03868f299a08b5c97a266b50a7fdcd3f2b | https://github.com/QLSong/cv-classify/tree/02f53d03868f299a08b5c97a266b50a7fdcd3f2b |
ToContinuous | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | 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.nn.parallel
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty... | kampta/multiview-shapes | ToContinuous | false | 3,796 | [
"MIT"
] | 0 | a79eb4b492be8c2c279e2c69b13d5a19dff1621b | https://github.com/kampta/multiview-shapes/tree/a79eb4b492be8c2c279e2c69b13d5a19dff1621b |
UpConv | import torch
import torch.nn as nn
class UpConv(nn.Module):
def __init__(self, input_nc, output_nc, kernel_size):
super(UpConv, self).__init__()
self.deconv = nn.ConvTranspose2d(in_channels=input_nc, out_channels
=output_nc, kernel_size=2, bias=True, stride=2, padding=0)
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.triton_helpers import libdevice
import torch.nn as ... | dong1015323606/LKVOLearner | UpConv | false | 15,205 | [
"BSD-3-Clause"
] | 237 | 6ac9fb5d3c22d6a81529063f8c52d6aa34166b2a | https://github.com/dong1015323606/LKVOLearner/tree/6ac9fb5d3c22d6a81529063f8c52d6aa34166b2a |
BertGELU | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | 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... | Codle/texar-pytorch | BertGELU | false | 11,298 | [
"Apache-2.0"
] | 0 | d63556e7a8f48076c396467314a771d56552d595 | https://github.com/Codle/texar-pytorch/tree/d63556e7a8f48076c396467314a771d56552d595 |
_MLP_C | import torch
import torch.nn as nn
class _MLP_C(nn.Module):
"""MLP that use DPMs from fcn and age, gender and MMSE"""
def __init__(self, in_size, drop_rate, fil_num):
super(_MLP_C, self).__init__()
self.fc1 = nn.Linear(in_size, fil_num)
self.fc2 = nn.Linear(fil_num, 2)
self.do... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | GaelKBertrand/Meliora_DeepLearning | _MLP_C | false | 5,185 | [
"MIT"
] | 1 | 5618e01066d4d0afcd7dfe074dda91af22b5857c | https://github.com/GaelKBertrand/Meliora_DeepLearning/tree/5618e01066d4d0afcd7dfe074dda91af22b5857c |
L2Norm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
from math import sqrt as sqrt
from itertools import produ... | ashwath007/amenity-detection | L2Norm | false | 6,263 | [
"Apache-2.0"
] | 1 | acb885eb4d791acc6e65237445a4fc6830e4d30c | https://github.com/ashwath007/amenity-detection/tree/acb885eb4d791acc6e65237445a4fc6830e4d30c |
Sigmoid | import torch
import torch.nn as nn
class ActivationFunction(nn.Module):
def __init__(self):
super().__init__()
self.name = self.__class__.__name__
self.config = {'name': self.name}
class Sigmoid(ActivationFunction):
def forward(self, x):
return 1 / (1 + torch.exp(-x))
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
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | ashutoshml/lightning-tutorials | Sigmoid | false | 6,243 | [
"Apache-2.0"
] | 1 | 898b8b6f9852c0b80f034a3187bc1cd34dd521ce | https://github.com/ashutoshml/lightning-tutorials/tree/898b8b6f9852c0b80f034a3187bc1cd34dd521ce |
ModulatedConv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | AsianZeus/Diverse-Facial-Edit | ModulatedConv2d | false | 9,415 | [
"Apache-2.0"
] | 0 | 3d4b1b41546a08a1fa3cb164ade33e319806b12b | https://github.com/AsianZeus/Diverse-Facial-Edit/tree/3d4b1b41546a08a1fa3cb164ade33e319806b12b |
PropMaxPool | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | 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.parallel
import torch.nn as nn
import torch.utils.data
import torch.backe... | EGO4D/episodic-memory | PropMaxPool | false | 8,812 | [
"MIT"
] | 27 | 2a3464882cd4f665c358c1b05a6397339e33c2e1 | https://github.com/EGO4D/episodic-memory/tree/2a3464882cd4f665c358c1b05a6397339e33c2e1 |
VanillaGenerativeAdversarialLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | 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... | neka-nat/Transfer-Learning-Library | VanillaGenerativeAdversarialLoss | false | 16,147 | [
"MIT"
] | 1,474 | a3b27b0d7562fa90a02e914140b37ab438469e6c | https://github.com/neka-nat/Transfer-Learning-Library/tree/a3b27b0d7562fa90a02e914140b37ab438469e6c |
h_sigmoid | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | 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... | rahulmangalampalli/esvit | h_sigmoid | false | 12,917 | [
"MIT"
] | 0 | 5caf6e36b088ae2e7aaa4100b307eec991078e3e | https://github.com/rahulmangalampalli/esvit/tree/5caf6e36b088ae2e7aaa4100b307eec991078e3e |
OneLayerFCBodyWithAction | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | Sohojoe/UdacityDeepRL-Project2 | OneLayerFCBodyWithAction | false | 5,844 | [
"MIT"
] | 1 | 7137eea0b606ea32d00424d23130ff213f03ecf1 | https://github.com/Sohojoe/UdacityDeepRL-Project2/tree/7137eea0b606ea32d00424d23130ff213f03ecf1 |
LabelSmoothingCrossEntropy | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | 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._C
import... | Molly6/segmentation_shengteng2021 | LabelSmoothingCrossEntropy | false | 8,569 | [
"Apache-2.0"
] | 21 | 33dfefa80193586f504069793d9e141944549e99 | https://github.com/Molly6/segmentation_shengteng2021/tree/33dfefa80193586f504069793d9e141944549e99 |
MaskedL1Loss | import torch
import torch.utils.data
import torch.nn as nn
class MaskedL1Loss(nn.Module):
def __init__(self):
super(MaskedL1Loss, self).__init__()
self.criterion = nn.L1Loss()
def forward(self, input, target, mask):
mask = mask.expand(-1, input.size()[1], -1, -1)
loss = self.... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.utils.dat... | lichnost/head2head | MaskedL1Loss | false | 3,914 | [
"MIT"
] | 0 | b0ec8b6965c9a32f3727dee9c164a7aaff027c5f | https://github.com/lichnost/head2head/tree/b0ec8b6965c9a32f3727dee9c164a7aaff027c5f |
Attention | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import *
class Attention(nn.Module):
def __init__(self, opt):
super(Attention, self).__init__()
self.rnn_size = opt.rnn_size
self.att_hid_size = opt.att_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
from torch._inductor.runtime.... | Zhendong-Wang/arsm_image_captioning | Attention | false | 11,137 | [
"MIT"
] | 0 | 2282b76ab03b53952269d94d6c4b19ab98636ca5 | https://github.com/Zhendong-Wang/arsm_image_captioning/tree/2282b76ab03b53952269d94d6c4b19ab98636ca5 |
SimpleMLPGen_with_meta_feature | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.optim
... | zhaofeng-shu33/deep_euler_tests | SimpleMLPGen_with_meta_feature | false | 13,166 | [
"MIT"
] | 0 | a3d0961af679d490b0c58873ee0726234122bc7a | https://github.com/zhaofeng-shu33/deep_euler_tests/tree/a3d0961af679d490b0c58873ee0726234122bc7a |
MLP | import torch
import numpy as np
import torch.nn as nn
import torch.optim as optim
from collections import OrderedDict
class MLP(nn.Module):
def __init__(self, input_size, output_size):
super(MLP, self).__init__()
self.input_size = input_size
self.output_size = output_size
self.mlp... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
import tor... | andreasbinder/Stochastic-Graph-assisted-Genre-Classification | MLP | false | 1,435 | [
"MIT"
] | 0 | 78752716030466f02424dcf1cbe5a66d756a13c4 | https://github.com/andreasbinder/Stochastic-Graph-assisted-Genre-Classification/tree/78752716030466f02424dcf1cbe5a66d756a13c4 |
MyBatchNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | 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_... | shohamda/deep-learning | MyBatchNorm | false | 4,319 | [
"MIT"
] | 0 | 160296c403cefd5351ffe5161e07789c22637284 | https://github.com/shohamda/deep-learning/tree/160296c403cefd5351ffe5161e07789c22637284 |
MySimpleNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | samxu0823/anfis-pytorch | MySimpleNet | false | 4,259 | [
"MIT"
] | 0 | b4ec3f0e8259963800e9e0a2904a580d1e56cc1c | https://github.com/samxu0823/anfis-pytorch/tree/b4ec3f0e8259963800e9e0a2904a580d1e56cc1c |
GlobalAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Flamexmt/LMA | GlobalAttention | false | 13,715 | [
"MIT"
] | 321 | f6fdec2d17a2d7a7733dd5a5745312bad392cdf3 | https://github.com/Flamexmt/LMA/tree/f6fdec2d17a2d7a7733dd5a5745312bad392cdf3 |
PytorchBinary | import torch
import torch.nn as nn
import torch.nn.functional as F
class PytorchBinary(nn.Module):
def __init__(self, num_features):
super(PytorchBinary, self).__init__()
self.layer_1 = nn.Linear(num_features, 256)
self.layer_out = nn.Linear(256, 1)
self.sigmoid = nn.Sigmoid()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | freescania/advdsi_at2 | PytorchBinary | false | 10,150 | [
"MIT"
] | 0 | 13fa0b8beaeccc28975aea40ee5a1db3dd3e33be | https://github.com/freescania/advdsi_at2/tree/13fa0b8beaeccc28975aea40ee5a1db3dd3e33be |
LearnedUpsampling1d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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... | fdb/samplernn-pytorch | LearnedUpsampling1d | false | 15,345 | [
"MIT"
] | 259 | 87ce71cc2cf26601a271648597f198df33059f96 | https://github.com/fdb/samplernn-pytorch/tree/87ce71cc2cf26601a271648597f198df33059f96 |
PixelNorm | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | 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... | Liamkuo/SAIR | PixelNorm | false | 17,574 | [
"MIT"
] | 6 | 0fb289cd975b5a196b58e7d16bac00e31fd41d39 | https://github.com/Liamkuo/SAIR/tree/0fb289cd975b5a196b58e7d16bac00e31fd41d39 |
BCEFocalLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch ... | Atharva-Phatak/torchflare | BCEFocalLoss | false | 13,330 | [
"Apache-2.0"
] | 86 | 945f4bee73a855edd8cb19cd646731155499a27f | https://github.com/Atharva-Phatak/torchflare/tree/945f4bee73a855edd8cb19cd646731155499a27f |
Sparsemax | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | 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.autograd import Function
from torch import nn
assert_size_stride = torch._C._d... | gitlost-murali/awesome-align | Sparsemax | false | 3,554 | [
"BSD-3-Clause"
] | 0 | 39fb45ca85a98e005447bddb52c48e65ce7d399b | https://github.com/gitlost-murali/awesome-align/tree/39fb45ca85a98e005447bddb52c48e65ce7d399b |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data.distributed
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 32, (3, 3))
self.pool1 = nn.MaxPool2d((2, 2))
self.conv2 = nn.Conv2d(32, 32, (3, 3... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | CSCfi/machine-learning-scripts | Net | false | 13,484 | [
"MIT"
] | 59 | 005f9343fb703ca2b6b11b5c2369e19efcaa5f62 | https://github.com/CSCfi/machine-learning-scripts/tree/005f9343fb703ca2b6b11b5c2369e19efcaa5f62 |
AffineTransform | import torch
from torch import nn
class FC(nn.Module):
def __init__(self, n_dim_in, n_dim_out, equal_lr=True):
super().__init__()
norm_const = n_dim_in ** -0.5
scale_init = 1 if equal_lr else norm_const
self.scale_forward = norm_const if equal_lr else 1
self.weight = nn.Pa... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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... | moritztng/stylegan2-pytorch | AffineTransform | false | 4,028 | [
"MIT"
] | 0 | 8827eae2e76c54b7406b34b2d49563ae53b04001 | https://github.com/moritztng/stylegan2-pytorch/tree/8827eae2e76c54b7406b34b2d49563ae53b04001 |
SimBasedLoss | import torch
from torch import nn
import torch.nn.functional as F
class SimBasedLoss(nn.Module):
def __init__(self):
super(SimBasedLoss, self).__init__()
def forward(self, y_s, y_t):
y_s = F.normalize(y_s, p=2, dim=1)
y_t = F.normalize(y_t, p=2, dim=1)
student_sims = torch.ma... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | kctsiolis/RepDistiller | SimBasedLoss | false | 3,929 | [
"BSD-2-Clause"
] | 0 | ce88f6e53fcf8ef81c5bac2d20ad31628dd279ac | https://github.com/kctsiolis/RepDistiller/tree/ce88f6e53fcf8ef81c5bac2d20ad31628dd279ac |
TransposeConv2dLayer | import torch
import torch.nn as nn
from torch.nn import functional as F
from torch.nn import Parameter
def l2normalize(v, eps=1e-12):
return v / (v.norm() + eps)
class LayerNorm(nn.Module):
def __init__(self, num_features, eps=1e-08, affine=True):
super(LayerNorm, self).__init__()
self.num_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.nn import Parameter
assert_size_stride = torch.... | autocomic/deepfillv2 | TransposeConv2dLayer | false | 12,140 | [
"MIT"
] | 0 | 4b0f565accbf20ee90093a4504b1cff0099d9cb9 | https://github.com/autocomic/deepfillv2/tree/4b0f565accbf20ee90093a4504b1cff0099d9cb9 |
Attention | import torch
from torch import nn
import torch.nn.utils
class Attention(nn.Module):
def __init__(self, hidden_dim):
super(Attention, self).__init__()
self.hidden_dim = hidden_dim
self.ff = nn.Linear(in_features=hidden_dim, out_features=1)
self.softmax = nn.Softmax(dim=-1)
def... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | bstee615/ReVeal | Attention | false | 14,982 | [
"MIT"
] | 63 | fc22d0d54a3a23d4e0bc45a249b7eea22749685e | https://github.com/bstee615/ReVeal/tree/fc22d0d54a3a23d4e0bc45a249b7eea22749685e |
FFModule | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | Masao-Someki/Conformer | FFModule | false | 8,529 | [
"MIT"
] | 18 | 866da9ae05a6d07304775c592caac8d516f67c92 | https://github.com/Masao-Someki/Conformer/tree/866da9ae05a6d07304775c592caac8d516f67c92 |
CircleLoss | import torch
from torch import Tensor
from torch import nn
class CircleLoss(nn.Module):
def __init__(self, m: 'float', gamma: 'float') ->None:
super(CircleLoss, self).__init__()
self.m = m
self.gamma = gamma
self.soft_plus = nn.Softplus()
def forward(self, sp: 'Tensor', sn: '... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch ... | fabiozappo/Person_reID_tensorrt | CircleLoss | false | 6,675 | [
"Apache-2.0"
] | 1 | 164441f35777698274e7664a9aefcc8d54467dc3 | https://github.com/fabiozappo/Person_reID_tensorrt/tree/164441f35777698274e7664a9aefcc8d54467dc3 |
MSE | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | 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... | Clement25/Multimodal-Attack | MSE | false | 285 | [
"MIT"
] | 0 | bd04ee099d457e87b6e6ee918c03f65a589bcb9a | https://github.com/Clement25/Multimodal-Attack/tree/bd04ee099d457e87b6e6ee918c03f65a589bcb9a |
Transition | import torch
import torch.nn as nn
import torch.nn.functional as F
class Transition(nn.Module):
def __init__(self, in_planes, out_planes):
super(Transition, self).__init__()
self.conv = nn.Conv2d(in_planes, out_planes, kernel_size=1, bias=True)
def forward(self, x):
out = self.conv(F... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Mahoumaru/auto_LiRPA | Transition | false | 11,673 | [
"BSD-3-Clause"
] | 0 | b03a6c36eb1b921726778359d6d2b94e0cd7e480 | https://github.com/Mahoumaru/auto_LiRPA/tree/b03a6c36eb1b921726778359d6d2b94e0cd7e480 |
BiaffineScorer | import torch
import torch.nn as nn
class BiaffineScorer(nn.Module):
def __init__(self, input1_size, input2_size, output_size):
super().__init__()
self.W_bilin = nn.Bilinear(input1_size + 1, input2_size + 1,
output_size)
self.W_bilin.weight.data.zero_()
self.W_bilin.bia... | 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... | NLPInBLCU/BiaffineDependencyParsing | BiaffineScorer | false | 14,074 | [
"MIT"
] | 67 | 40b133648c747957dacd59916add0403371fe680 | https://github.com/NLPInBLCU/BiaffineDependencyParsing/tree/40b133648c747957dacd59916add0403371fe680 |
SelfAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | eitin-infant/FinRL-Meta | SelfAttention | false | 15,295 | [
"MIT"
] | 214 | 4c94011e58425796e7e2e5c1bf848afd65c828d6 | https://github.com/eitin-infant/FinRL-Meta/tree/4c94011e58425796e7e2e5c1bf848afd65c828d6 |
FixupBasicBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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
import t... | bethgelab/robustness | FixupBasicBlock | false | 14,954 | [
"Apache-2.0"
] | 67 | aa0a6798fe3973bae5f47561721b59b39f126ab7 | https://github.com/bethgelab/robustness/tree/aa0a6798fe3973bae5f47561721b59b39f126ab7 |
MNACLayer | import collections
import math
import torch
import torch.utils.data
def sparsity_error(W):
W_error = torch.min(torch.abs(W), torch.abs(1 - torch.abs(W)))
return torch.max(W_error)
def mnac(x, W, mode='prod'):
out_size, in_size = W.size()
x = x.view(x.size()[0], in_size, 1)
W = W.t().view(1, in_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 collections
import math
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = ... | wlm2019/Neural-Arithmetic-Units | MNACLayer | false | 16,716 | [
"MIT"
] | 147 | f9de9d004bb2dc2ee28577cd1760d0a00c185836 | https://github.com/wlm2019/Neural-Arithmetic-Units/tree/f9de9d004bb2dc2ee28577cd1760d0a00c185836 |
HardAttn | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | Danish-VSL/deep-person-reid | HardAttn | false | 13,551 | [
"MIT"
] | 244 | 2e3a4b6706b84c77203f9905683b917ab0871b93 | https://github.com/Danish-VSL/deep-person-reid/tree/2e3a4b6706b84c77203f9905683b917ab0871b93 |
CMMD | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | 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... | JDAI-CV/CM-NAS | CMMD | false | 8,302 | [
"Apache-2.0"
] | 31 | bbc77f427b2c8afb9f3865f5a04e86079d33dd28 | https://github.com/JDAI-CV/CM-NAS/tree/bbc77f427b2c8afb9f3865f5a04e86079d33dd28 |
GAT | import torch
import torch.nn as nn
import torch.nn.functional as F
class GAT(nn.Module):
def __init__(self, num_feats):
super(GAT, self).__init__()
self.num_feats = num_feats
self.weight_key = nn.Parameter(torch.zeros(size=(self.num_feats, 1)))
self.weight_query = nn.Parameter(tor... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | juaduan/babybrainguardian | GAT | false | 6,992 | [
"MIT"
] | 1 | b871e3a83fef98c2e05dd8857721a3c964a46418 | https://github.com/juaduan/babybrainguardian/tree/b871e3a83fef98c2e05dd8857721a3c964a46418 |
SamePadConv2d | import torch
from torch.nn import functional as F
import torch.nn as nn
class SamePadConv2d(nn.Conv2d):
"""
Conv with TF padding='same'
https://github.com/pytorch/pytorch/issues/3867#issuecomment-349279036
"""
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
dilation=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... | jjeamin/obJDetection | SamePadConv2d | false | 6,951 | [
"MIT"
] | 1 | eb7fbc410beb00fad1a6477e827e9ce2d8efbac5 | https://github.com/jjeamin/obJDetection/tree/eb7fbc410beb00fad1a6477e827e9ce2d8efbac5 |
F_conv | import torch
import warnings
import torch.nn as nn
import torch.nn.functional as F
class F_conv(nn.Module):
"""ResNet transformation, not itself reversible, just used below"""
def __init__(self, in_channels, channels, channels_hidden=None, stride=
None, kernel_size=3, leaky_slope=0.1, batch_norm=Fals... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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 warnings
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guar... | ramonpeter/LaSeR | F_conv | false | 7,531 | [
"MIT"
] | 1 | 28daa6876256501ed0d3e84a4ddfedc7892bd528 | https://github.com/ramonpeter/LaSeR/tree/28daa6876256501ed0d3e84a4ddfedc7892bd528 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.