entry_point stringlengths 1 65 | original_triton_code stringlengths 4.5k 619k | python_code stringlengths 208 60.9k | 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 | pytorch_code stringlengths 200 4.05k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
MaxPool | # 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 torch.nn as nn
class MaxPool(nn.Module):
def __init__(self, kernel_size, stride):
super(MaxPool, self).__init__()
self.pool = nn.MaxPool2d(kernel_size=kernel_size, stride=stride)
def forward(self, x):
x = self.pool(x)
return x
def get_inputs():
retur... | 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... | Hiroaki-Ozaki/modelib-classification | MaxPool | false | 17,382 | [
"WTFPL"
] | 10 | 11077704cc0bc9a42fc4b94da60b57d31ff0f65c | https://github.com/Hiroaki-Ozaki/modelib-classification/tree/11077704cc0bc9a42fc4b94da60b57d31ff0f65c | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, kernel_size, stride):
super().__init__()
self.pool = nn.MaxPool2d(kernel_size=kernel_size, stride=stride)
def forward(self, x):
x = self.pool(x)
return x
def get_inputs():
return [torch.rand([... |
MeanVoxelFeatureExtractor | # 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 torch.nn as nn
class VoxelFeatureExtractor(nn.Module):
def __init__(self, **kwargs):
super().__init__()
def get_output_feature_dim(self):
raise NotImplementedError
def forward(self, **kwargs):
raise NotImplementedError
class MeanVoxelFeatureExtractor(VoxelF... | 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... | Hub-Tian/CADNet | MeanVoxelFeatureExtractor | false | 17,383 | [
"Apache-2.0"
] | 7 | 37d2be6121bb184d8ded92fa468cb6490a15caea | https://github.com/Hub-Tian/CADNet/tree/37d2be6121bb184d8ded92fa468cb6490a15caea | import torch
import torch.nn as nn
class VoxelFeatureExtractor(nn.Module):
def __init__(self, **kwargs):
super().__init__()
def get_output_feature_dim(self):
raise NotImplementedError
def forward(self, **kwargs):
raise NotImplementedError
class Model(VoxelFeatureExtractor):
... |
SAM | # 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 torch.nn as nn
def conv(in_channels, out_channels, kernel_size, bias=False, stride=1):
return nn.Conv2d(in_channels, out_channels, kernel_size, padding=
kernel_size // 2, bias=bias, stride=stride)
class SAM(nn.Module):
def __init__(self, n_feat, kernel_size=3, bias=True):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | HolyWu/vs-hinet | SAM | false | 17,384 | [
"MIT"
] | 4 | b1083ab169d082696d4bf40281922ee52c762714 | https://github.com/HolyWu/vs-hinet/tree/b1083ab169d082696d4bf40281922ee52c762714 | import torch
import torch.nn as nn
def conv(in_channels, out_channels, kernel_size, bias=False, stride=1):
return nn.Conv2d(in_channels, out_channels, kernel_size, padding=
kernel_size // 2, bias=bias, stride=stride)
class Model(nn.Module):
def __init__(self, n_feat, kernel_size=3, bias=True):
... |
LayerNorm | # 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 torch.nn as nn
class LayerNorm(nn.Module):
"""Layer normalization class. Normalization is done on the last dimension
Args:
input_size: size of input sample
Inputs:
a Tensor with shape (batch, length, input_size) or (batch, input_size)
Outputs:
a Tensor wi... | 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_... | Hritikbansal/RNNs_SVA_OOD | LayerNorm | false | 17,385 | [
"MIT"
] | 4 | a1c73955342d9d35c49da5fcb7b315e37b0f75d1 | https://github.com/Hritikbansal/RNNs_SVA_OOD/tree/a1c73955342d9d35c49da5fcb7b315e37b0f75d1 | import torch
import torch.nn as nn
class Model(nn.Module):
"""Layer normalization class. Normalization is done on the last dimension
Args:
input_size: size of input sample
Inputs:
a Tensor with shape (batch, length, input_size) or (batch, input_size)
Outputs:
a Tensor with s... |
ArcMarginProduct | # 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 math
import torch
import torch.nn as nn
from torch.nn import functional as F
import torch.nn.parallel
from torch.nn import Parameter
class ArcMarginProduct(nn.Module):
"""Implement of large margin arc distance: :
Args:
in_features: size of each input sample
out_features: 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
from torch._inductor.runtime.... | HotaekHan/classification_uncertainty | ArcMarginProduct | false | 17,386 | [
"MIT"
] | 5 | f0f119b93a84f7b041baf4eddf835dd99013e6a3 | https://github.com/HotaekHan/classification_uncertainty/tree/f0f119b93a84f7b041baf4eddf835dd99013e6a3 | import math
import torch
import torch.nn as nn
from torch.nn import functional as F
import torch.nn.parallel
from torch.nn import Parameter
class Model(nn.Module):
"""Implement of large margin arc distance: :
Args:
in_features: size of each input sample
out_features: size of each o... |
UNet | # 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 import nn
from torch.nn import functional as F
import torch.nn.parallel
class down(nn.Module):
"""
A class for creating neural network blocks containing layers:
Average Pooling --> Convlution + Leaky ReLU --> Convolution + Leaky ReLU
This is used in the UNet Class to create a... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
from tor... | DA4EVENT/home | UNet | false | 17,387 | [
"MIT"
] | 5 | 18cc93a795ce132e05b886aa34565a102915b1c6 | https://github.com/DA4EVENT/home/tree/18cc93a795ce132e05b886aa34565a102915b1c6 | import torch
from torch import nn
from torch.nn import functional as F
import torch.nn.parallel
class down(nn.Module):
"""
A class for creating neural network blocks containing layers:
Average Pooling --> Convlution + Leaky ReLU --> Convolution + Leaky ReLU
This is used in the UNet Class to create a... |
CumMax | # 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 torch.nn as nn
class CumMax(nn.Module):
def __init__(self):
super(CumMax, self).__init__()
def forward(self, input):
return torch.cumsum(nn.Softmax(dim=-1)(input), -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
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | Hritikbansal/RNNs_SVA_OOD | CumMax | false | 17,388 | [
"MIT"
] | 4 | a1c73955342d9d35c49da5fcb7b315e37b0f75d1 | https://github.com/Hritikbansal/RNNs_SVA_OOD/tree/a1c73955342d9d35c49da5fcb7b315e37b0f75d1 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, input):
return torch.cumsum(nn.Softmax(dim=-1)(input), -1)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return []
|
PositionWiseFeedForward | # 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 torchvision.transforms import functional as F
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.modules.module
class PositionWiseFeedForward(nn.Module):
""" A two-feed-forward-layer module """
def __init__(self, d_in, d_hid, dropout=0.1):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ChCh1999/RTPB | PositionWiseFeedForward | false | 17,389 | [
"MIT"
] | 8 | 1066a3bfe4fe1b41eff74fd152936880302a60a2 | https://github.com/ChCh1999/RTPB/tree/1066a3bfe4fe1b41eff74fd152936880302a60a2 | import torch
from torchvision.transforms import functional as F
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.modules.module
class Model(nn.Module):
""" A two-feed-forward-layer module """
def __init__(self, d_in, d_hid, dropout=0.1):
super().__init__()... |
FastRCNNPredictor | # 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 import nn
import torch.nn.functional as F
import torch.utils.data
from torchvision.transforms import functional as F
class FastRCNNPredictor(nn.Module):
"""
Standard classification + bounding box regression layers
for Fast R-CNN.
Arguments:
in_channels (int): number of... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.utils.data
assert_size_stride = torch._C._dyna... | CancerDataScience/NuCLS | FastRCNNPredictor | false | 17,390 | [
"MIT"
] | 7 | c172b55b18d4ea78c3f51a8fd28ee6c2595c8360 | https://github.com/CancerDataScience/NuCLS/tree/c172b55b18d4ea78c3f51a8fd28ee6c2595c8360 | import torch
from torch import nn
import torch.nn.functional as F
import torch.utils.data
from torchvision.transforms import functional as F
class Model(nn.Module):
"""
Standard classification + bounding box regression layers
for Fast R-CNN.
Arguments:
in_channels (int): number of input chann... |
ImageDiscriminator | # 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 torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class ImageDiscriminator(nn.Module):
def __init__(self):
super(ImageDiscriminator, self).__init__()
self.conv1 = nn.Conv2d(in_channels=6, out_channels=64, kernel_size=
3, stride=2, padding=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
import torch.utils.data
assert_size_stride = torch._C._dyn... | HotaekHan/Synthetically_Supervised_Text_Recognition | ImageDiscriminator | false | 17,391 | [
"MIT"
] | 8 | a6bb7d3039b1280c6efe177b69d8b985d2e13285 | https://github.com/HotaekHan/Synthetically_Supervised_Text_Recognition/tree/a6bb7d3039b1280c6efe177b69d8b985d2e13285 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class Model(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(in_channels=6, out_channels=64, kernel_size=
3, stride=2, padding=1)
self.conv2 = nn.Conv2d(in_chann... |
MAXATTN | # 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 torch.nn as nn
class MAXATTN(nn.Module):
def __init__(self, embed_dim, num_heads, dropout=0.0, bias=True,
add_bias_kv=False, add_zero_attn=False, kdim=None, vdim=None):
super(MAXATTN, self).__init__()
self.attention_layer = nn.MultiheadAttention(embed_dim, num_heads)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Hritikbansal/RNNs_SVA_OOD | MAXATTN | false | 17,392 | [
"MIT"
] | 4 | a1c73955342d9d35c49da5fcb7b315e37b0f75d1 | https://github.com/Hritikbansal/RNNs_SVA_OOD/tree/a1c73955342d9d35c49da5fcb7b315e37b0f75d1 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, embed_dim, num_heads, dropout=0.0, bias=True,
add_bias_kv=False, add_zero_attn=False, kdim=None, vdim=None):
super().__init__()
self.attention_layer = nn.MultiheadAttention(embed_dim, num_heads)
def forward... |
UIAttention | # 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 torch.nn as nn
import torch.nn.functional as F
class UIAttention(nn.Module):
def __init__(self, latent_dim, att_size):
super(UIAttention, self).__init__()
self.dense = nn.Linear(in_features=latent_dim * 2, out_features=
att_size)
nn.init.xavier_normal_(self... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Hui-Li/MCRec_PyTorch | UIAttention | false | 17,393 | [
"MIT"
] | 9 | da4da77d2cade40c0a1961481c8e47ac396d12ee | https://github.com/Hui-Li/MCRec_PyTorch/tree/da4da77d2cade40c0a1961481c8e47ac396d12ee | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, latent_dim, att_size):
super().__init__()
self.dense = nn.Linear(in_features=latent_dim * 2, out_features=
att_size)
nn.init.xavier_normal_(self.dense.weight.data)
... |
GAT | # 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 torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
class GraphAttentionLayer(nn.Module):
"""
Simple GAT layer, similar to https://arxiv.org/abs/1710.10903
"""
def __init__(self, in_features, out_features, dropout, alpha, concat=True):
super(GraphAtten... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | HecatePhy/directed_graphsage | GAT | false | 17,394 | [
"MIT"
] | 6 | 0e35f8971d44b8b3477fd7339225e1a69da4456a | https://github.com/HecatePhy/directed_graphsage/tree/0e35f8971d44b8b3477fd7339225e1a69da4456a | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
class GraphAttentionLayer(nn.Module):
"""
Simple GAT layer, similar to https://arxiv.org/abs/1710.10903
"""
def __init__(self, in_features, out_features, dropout, alpha, concat=True):
super().__init__... |
GridReduction1 | # 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.nn import functional as F
import torch.nn as nn
class Conv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, batch_norm=
False, **kwargs):
super(Conv2d, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, **kwargs)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | Hiroaki-Ozaki/modelib-classification | GridReduction1 | false | 17,395 | [
"WTFPL"
] | 10 | 11077704cc0bc9a42fc4b94da60b57d31ff0f65c | https://github.com/Hiroaki-Ozaki/modelib-classification/tree/11077704cc0bc9a42fc4b94da60b57d31ff0f65c | import torch
from torch.nn import functional as F
import torch.nn as nn
class Conv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, batch_norm=
False, **kwargs):
super().__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, **kwargs)
self... |
InceptionB | # 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.nn import functional as F
import torch.nn as nn
class Conv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, batch_norm=
False, **kwargs):
super(Conv2d, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, **kwargs)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | Hiroaki-Ozaki/modelib-classification | InceptionB | false | 17,396 | [
"WTFPL"
] | 10 | 11077704cc0bc9a42fc4b94da60b57d31ff0f65c | https://github.com/Hiroaki-Ozaki/modelib-classification/tree/11077704cc0bc9a42fc4b94da60b57d31ff0f65c | import torch
from torch.nn import functional as F
import torch.nn as nn
class Conv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, batch_norm=
False, **kwargs):
super().__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, **kwargs)
self... |
GroupGRUCell | # 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 math
import torch
import torch.nn as nn
class GroupLinearLayer(nn.Module):
def __init__(self, din, dout, num_blocks):
super(GroupLinearLayer, self).__init__()
self.w = nn.Parameter(0.01 * torch.randn(num_blocks, din, dout))
def forward(self, x):
x = x.permute(1, 0, 2)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import ... | Hritikbansal/RNNs_SVA_OOD | GroupGRUCell | false | 17,397 | [
"MIT"
] | 4 | a1c73955342d9d35c49da5fcb7b315e37b0f75d1 | https://github.com/Hritikbansal/RNNs_SVA_OOD/tree/a1c73955342d9d35c49da5fcb7b315e37b0f75d1 | import math
import torch
import torch.nn as nn
class GroupLinearLayer(nn.Module):
def __init__(self, din, dout, num_blocks):
super().__init__()
self.w = nn.Parameter(0.01 * torch.randn(num_blocks, din, dout))
def forward(self, x):
x = x.permute(1, 0, 2)
x = torch.bmm(x, self.... |
CNN | # 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 torch.nn as nn
import torch.nn.functional as F
class CNN(nn.Module):
def __init__(self, action_dim=7):
super(CNN, self).__init__()
self.action_dim = action_dim
self.conv1 = nn.Conv2d(3, 16, 5, padding=2)
self.conv2 = nn.Conv2d(16, 32, 5, padding=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_... | HeegerGao/CRIL | CNN | false | 17,398 | [
"MIT"
] | 9 | c4095bca7cf5c8e376b0014447b1422c1b5b6cec | https://github.com/HeegerGao/CRIL/tree/c4095bca7cf5c8e376b0014447b1422c1b5b6cec | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, action_dim=7):
super().__init__()
self.action_dim = action_dim
self.conv1 = nn.Conv2d(3, 16, 5, padding=2)
self.conv2 = nn.Conv2d(16, 32, 5, padding=2)
self.conv3 ... |
DecayModule | # 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 math
import torch
import torch.nn as nn
class DecayModule(nn.Module):
def __init__(self, input_size, hidden_size, bias=True, num_chunks=1,
activation='relu', nodiag=False):
super(DecayModule, self).__init__()
self.sigmoid = nn.Sigmoid()
self.tanh = nn.Tanh()
self.re... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 math
import torch.nn a... | Hritikbansal/RNNs_SVA_OOD | DecayModule | false | 17,399 | [
"MIT"
] | 4 | a1c73955342d9d35c49da5fcb7b315e37b0f75d1 | https://github.com/Hritikbansal/RNNs_SVA_OOD/tree/a1c73955342d9d35c49da5fcb7b315e37b0f75d1 | import math
import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, input_size, hidden_size, bias=True, num_chunks=1,
activation='relu', nodiag=False):
super().__init__()
self.sigmoid = nn.Sigmoid()
self.tanh = nn.Tanh()
self.relu = nn.ReLU()
... |
GridReduction2 | # 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.nn import functional as F
import torch.nn as nn
class Conv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, batch_norm=
False, **kwargs):
super(Conv2d, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, **kwargs)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | Hiroaki-Ozaki/modelib-classification | GridReduction2 | false | 17,400 | [
"WTFPL"
] | 10 | 11077704cc0bc9a42fc4b94da60b57d31ff0f65c | https://github.com/Hiroaki-Ozaki/modelib-classification/tree/11077704cc0bc9a42fc4b94da60b57d31ff0f65c | import torch
from torch.nn import functional as F
import torch.nn as nn
class Conv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, batch_norm=
False, **kwargs):
super().__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, **kwargs)
self... |
MetaPathAttention | # 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 torch.nn as nn
import torch.nn.functional as F
class MetaPathAttention(nn.Module):
def __init__(self, att_size, latent_dim, metapath_type_num):
super(MetaPathAttention, self).__init__()
self.att_size = att_size
self.latent_dim = latent_dim
self.metapath_type_nu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Hui-Li/MCRec_PyTorch | MetaPathAttention | false | 17,401 | [
"MIT"
] | 9 | da4da77d2cade40c0a1961481c8e47ac396d12ee | https://github.com/Hui-Li/MCRec_PyTorch/tree/da4da77d2cade40c0a1961481c8e47ac396d12ee | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, att_size, latent_dim, metapath_type_num):
super().__init__()
self.att_size = att_size
self.latent_dim = latent_dim
self.metapath_type_num = metapath_type_num
self.... |
DummyEmbedder | # 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 torch.nn as nn
class DummyEmbedder(nn.Module):
def __init__(self, embedding_dim):
super().__init__()
self.embedding_dim = embedding_dim
self.day_embedding = nn.Linear(1, embedding_dim)
self.week_embedding = nn.Linear(1, embedding_dim)
self.month_embeddi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | HumaticsLAB/GTM-Transformer | DummyEmbedder | false | 17,402 | [
"MIT"
] | 7 | 94124d3246c7c22d8b952beeda53639a9ad170e3 | https://github.com/HumaticsLAB/GTM-Transformer/tree/94124d3246c7c22d8b952beeda53639a9ad170e3 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, embedding_dim):
super().__init__()
self.embedding_dim = embedding_dim
self.day_embedding = nn.Linear(1, embedding_dim)
self.week_embedding = nn.Linear(1, embedding_dim)
self.month_embedding = nn.... |
InceptionAux | # 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.nn import functional as F
import torch.nn as nn
class Conv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, batch_norm=
False, **kwargs):
super(Conv2d, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, **kwargs)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | Hiroaki-Ozaki/modelib-classification | InceptionAux | false | 17,403 | [
"WTFPL"
] | 10 | 11077704cc0bc9a42fc4b94da60b57d31ff0f65c | https://github.com/Hiroaki-Ozaki/modelib-classification/tree/11077704cc0bc9a42fc4b94da60b57d31ff0f65c | import torch
from torch.nn import functional as F
import torch.nn as nn
class Conv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, batch_norm=
False, **kwargs):
super().__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, **kwargs)
self... |
Gate | # 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 torch.nn as nn
class Gate(nn.Module):
def __init__(self, dhid, dfeature, init_range=0.1, init_dist='uniform',
dropout=0.5):
super(Gate, self).__init__()
self.dhid = dhid
self.dfeature = dfeature
self.linear_z = nn.Linear(self.dhid + self.dfeature, 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 ... | Hunter-DDM/DeFT-naacl2021 | Gate | false | 17,404 | [
"MIT"
] | 6 | c61aeb4f63a650a0a1b71fb1b0b245cb3925009b | https://github.com/Hunter-DDM/DeFT-naacl2021/tree/c61aeb4f63a650a0a1b71fb1b0b245cb3925009b | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, dhid, dfeature, init_range=0.1, init_dist='uniform',
dropout=0.5):
super().__init__()
self.dhid = dhid
self.dfeature = dfeature
self.linear_z = nn.Linear(self.dhid + self.dfeature, self.dhid)
... |
LayerNorm | # 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
class LayerNorm(torch.nn.Module):
def __init__(self, input_dim):
super(LayerNorm, self).__init__()
self.gamma = torch.nn.Parameter(torch.ones(input_dim))
self.beta = torch.nn.Parameter(torch.zeros(input_dim))
self.eps = 1e-06
def forward(self, x, mask):
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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | IBM/context-relevant-pruning-textrl | LayerNorm | false | 17,405 | [
"Apache-2.0"
] | 8 | c8630203af5df64c8e1e3c4624e4a158b40a5f27 | https://github.com/IBM/context-relevant-pruning-textrl/tree/c8630203af5df64c8e1e3c4624e4a158b40a5f27 | import torch
class Model(torch.nn.Module):
def __init__(self, input_dim):
super().__init__()
self.gamma = torch.nn.Parameter(torch.ones(input_dim))
self.beta = torch.nn.Parameter(torch.zeros(input_dim))
self.eps = 1e-06
def forward(self, x, mask):
mean = x.mean(-1, ke... |
Attention | # 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 torch.nn as nn
class Attention(nn.Module):
def __init__(self, encoder_dim, decoder_dim, attention_dim):
super(Attention, self).__init__()
self.encoder_dim = encoder_dim
self.encoder_att = nn.Linear(encoder_dim, attention_dim)
self.decoder_att = nn.Linear(decode... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | HumaticsLAB/AttentionBasedMultiModalRNN | Attention | false | 17,406 | [
"MIT"
] | 5 | 0c060a97cdddf1348938a5f2d456e83e5f8bf887 | https://github.com/HumaticsLAB/AttentionBasedMultiModalRNN/tree/0c060a97cdddf1348938a5f2d456e83e5f8bf887 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, encoder_dim, decoder_dim, attention_dim):
super().__init__()
self.encoder_dim = encoder_dim
self.encoder_att = nn.Linear(encoder_dim, attention_dim)
self.decoder_att = nn.Linear(decoder_dim, attention_di... |
InceptionA | # 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.nn import functional as F
import torch.nn as nn
class Conv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, batch_norm=
False, **kwargs):
super(Conv2d, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, **kwargs)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | Hiroaki-Ozaki/modelib-classification | InceptionA | false | 17,407 | [
"WTFPL"
] | 10 | 11077704cc0bc9a42fc4b94da60b57d31ff0f65c | https://github.com/Hiroaki-Ozaki/modelib-classification/tree/11077704cc0bc9a42fc4b94da60b57d31ff0f65c | import torch
from torch.nn import functional as F
import torch.nn as nn
class Conv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, batch_norm=
False, **kwargs):
super().__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, **kwargs)
self... |
FeatLoss | # 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 torch.utils.data
import torch.nn as nn
from sklearn import *
class FeatLoss(nn.Module):
"""
This criterion is a implemenation of Focal Loss, which is proposed in
Focal Loss for Dense Object Detection.
Loss(x, class) = - \\alpha (1-softmax(x)[class])^gamma \\log(sof... | 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... | CityU-AIM-Group/SIGMA | FeatLoss | false | 17,408 | [
"MIT"
] | 5 | 19f89777db8d42f750a9b87756d3326c7efd18f5 | https://github.com/CityU-AIM-Group/SIGMA/tree/19f89777db8d42f750a9b87756d3326c7efd18f5 | import torch
import torch.utils.data
import torch.nn as nn
from sklearn import *
class Model(nn.Module):
"""
This criterion is a implemenation of Focal Loss, which is proposed in
Focal Loss for Dense Object Detection.
Loss(x, class) = - \\alpha (1-softmax(x)[class])^gamma \\log(softma... |
Network | # 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 torch.nn as nn
import torch.nn.functional as F
class Network(nn.Module):
def __init__(self, input_dim):
super(Network, self).__init__()
self.first_layer = nn.Linear(input_dim, 6)
self.out_layer = nn.Linear(6, 1)
def forward(self, x):
out = self.first_layer... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | HyperScypion/KMS_Neural_Networks | Network | false | 17,409 | [
"MIT"
] | 6 | 71d0e9c6ee02ea7978ac8ab1b899290743afac7d | https://github.com/HyperScypion/KMS_Neural_Networks/tree/71d0e9c6ee02ea7978ac8ab1b899290743afac7d | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, input_dim):
super().__init__()
self.first_layer = nn.Linear(input_dim, 6)
self.out_layer = nn.Linear(6, 1)
def forward(self, x):
out = self.first_layer(x)
out... |
MetaPathEmbedding | # 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 torch.nn as nn
import torch.nn.functional as F
class MetaPathEmbedding(nn.Module):
def __init__(self, path_num, hop_num, feature_size, latent_dim):
super(MetaPathEmbedding, self).__init__()
self.path_num = path_num
self.hop_num = hop_num
self.feature_size = fea... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | Hui-Li/MCRec_PyTorch | MetaPathEmbedding | false | 17,410 | [
"MIT"
] | 9 | da4da77d2cade40c0a1961481c8e47ac396d12ee | https://github.com/Hui-Li/MCRec_PyTorch/tree/da4da77d2cade40c0a1961481c8e47ac396d12ee | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, path_num, hop_num, feature_size, latent_dim):
super().__init__()
self.path_num = path_num
self.hop_num = hop_num
self.feature_size = feature_size
self.latent_dim =... |
layer_1_to_1 | # 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 numpy as np
import torch.nn as nn
def contractions_1_to_1(inputs, dim, normalization='inf', normalization_val=1.0
):
sum_all = torch.sum(inputs, dim=2).unsqueeze(dim=2)
op1 = inputs
op2 = torch.cat([sum_all for d in range(dim)], dim=2)
if normalization is not None:
if n... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import numpy as np
import torch.nn as nn
assert_size_stride = torch._C._dynamo.g... | HyTruongSon/InvariantGraphNetworks-PyTorch | layer_1_to_1 | false | 17,411 | [
"Apache-2.0"
] | 7 | da9fdaa4f858d6fcae14b08a59d4b172a2aabaf8 | https://github.com/HyTruongSon/InvariantGraphNetworks-PyTorch/tree/da9fdaa4f858d6fcae14b08a59d4b172a2aabaf8 | import torch
import numpy as np
import torch.nn as nn
def contractions_1_to_1(inputs, dim, normalization='inf', normalization_val=1.0
):
sum_all = torch.sum(inputs, dim=2).unsqueeze(dim=2)
op1 = inputs
op2 = torch.cat([sum_all for d in range(dim)], dim=2)
if normalization is not None:
if n... |
Asym_ReLU_Block | # 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 import nn
class Asym_ReLU_Block(nn.Module):
def __init__(self):
super(Asym_ReLU_Block, self).__init__()
self.conv1 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size
=(3, 1), stride=1, padding=(1, 0), bias=False)
self.conv2 = nn.Conv2d(in_channels... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | HwangToeMat/Asym_VDSR | Asym_ReLU_Block | false | 17,412 | [
"MIT"
] | 4 | 598200f745434fc6e1bb46b6da7d6cf7b0fdaa50 | https://github.com/HwangToeMat/Asym_VDSR/tree/598200f745434fc6e1bb46b6da7d6cf7b0fdaa50 | import torch
from torch import nn
class Model(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size
=(3, 1), stride=1, padding=(1, 0), bias=False)
self.conv2 = nn.Conv2d(in_channels=64, out_channels=64, kernel_si... |
layer_2_to_1 | # 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 numpy as np
import torch.nn as nn
def contractions_2_to_1(inputs, dim, normalization='inf', normalization_val=1.0
):
diag_part = torch.diagonal(inputs, dim1=2, dim2=3)
sum_diag_part = torch.sum(diag_part, dim=2).unsqueeze(dim=2)
sum_of_rows = torch.sum(inputs, dim=3)
sum_of_col... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | HyTruongSon/InvariantGraphNetworks-PyTorch | layer_2_to_1 | false | 17,413 | [
"Apache-2.0"
] | 7 | da9fdaa4f858d6fcae14b08a59d4b172a2aabaf8 | https://github.com/HyTruongSon/InvariantGraphNetworks-PyTorch/tree/da9fdaa4f858d6fcae14b08a59d4b172a2aabaf8 | import torch
import numpy as np
import torch.nn as nn
def contractions_2_to_1(inputs, dim, normalization='inf', normalization_val=1.0
):
diag_part = torch.diagonal(inputs, dim1=2, dim2=3)
sum_diag_part = torch.sum(diag_part, dim=2).unsqueeze(dim=2)
sum_of_rows = torch.sum(inputs, dim=3)
sum_of_col... |
TransformerDecoderLayer | # 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 torch.nn as nn
import torch.nn.functional as F
class TransformerDecoderLayer(nn.Module):
def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1,
activation='relu'):
super(TransformerDecoderLayer, self).__init__()
self.multihead_attn = nn.MultiheadAttentio... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | HumaticsLAB/GTM-Transformer | TransformerDecoderLayer | false | 17,414 | [
"MIT"
] | 7 | 94124d3246c7c22d8b952beeda53639a9ad170e3 | https://github.com/HumaticsLAB/GTM-Transformer/tree/94124d3246c7c22d8b952beeda53639a9ad170e3 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1,
activation='relu'):
super().__init__()
self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout
=dropout)... |
InceptionC | # 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.nn import functional as F
import torch.nn as nn
class Conv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, batch_norm=
False, **kwargs):
super(Conv2d, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, **kwargs)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | Hiroaki-Ozaki/modelib-classification | InceptionC | false | 17,415 | [
"WTFPL"
] | 10 | 11077704cc0bc9a42fc4b94da60b57d31ff0f65c | https://github.com/Hiroaki-Ozaki/modelib-classification/tree/11077704cc0bc9a42fc4b94da60b57d31ff0f65c | import torch
from torch.nn import functional as F
import torch.nn as nn
class Conv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, batch_norm=
False, **kwargs):
super().__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, **kwargs)
self... |
PositionalEncoding | # 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 math
import torch
from torch import nn
class PositionalEncoding(nn.Module):
def __init__(self, dimension: 'int', dropout: 'float'=0.1):
super().__init__()
self.dropout = nn.Dropout(p=dropout)
self.dimension = dimension
def forward(self, x: 'torch.Tensor') ->torch.Tensor:
... | 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
from torch import nn
a... | IMDxD/NonAttentiveTacotron | PositionalEncoding | false | 17,416 | [
"MIT"
] | 4 | a227fba1bdfa4c5ec63a0f0364313f3ac0fef1ba | https://github.com/IMDxD/NonAttentiveTacotron/tree/a227fba1bdfa4c5ec63a0f0364313f3ac0fef1ba | import math
import torch
from torch import nn
class Model(nn.Module):
def __init__(self, dimension: 'int', dropout: 'float'=0.1):
super().__init__()
self.dropout = nn.Dropout(p=dropout)
self.dimension = dimension
def forward(self, x: 'torch.Tensor') ->torch.Tensor:
position =... |
Conv_ReLU_Block | # 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 import nn
class Conv_ReLU_Block(nn.Module):
def __init__(self):
super(Conv_ReLU_Block, self).__init__()
self.conv = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=
3, stride=1, padding=1, bias=False)
self.relu = nn.ReLU(inplace=True)
def fo... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | HwangToeMat/Asym_VDSR | Conv_ReLU_Block | false | 17,417 | [
"MIT"
] | 4 | 598200f745434fc6e1bb46b6da7d6cf7b0fdaa50 | https://github.com/HwangToeMat/Asym_VDSR/tree/598200f745434fc6e1bb46b6da7d6cf7b0fdaa50 | import torch
from torch import nn
class Model(nn.Module):
def __init__(self):
super().__init__()
self.conv = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=
3, stride=1, padding=1, bias=False)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
return ... |
AlexNet | # 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 torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.functional as F
class AlexNet(nn.Module):
def __init__(self, num_classes=10, out_ch_conv1=64, out_ch_conv2=256,
out_ch_conv3=384, out_ch_conv4=256, ou... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | FujitsuLaboratories/CAC | AlexNet | false | 17,418 | [
"Apache-2.0"
] | 8 | d12df8e47f61eaf7d7b0ed355e2d1aa296453f86 | https://github.com/FujitsuLaboratories/CAC/tree/d12df8e47f61eaf7d7b0ed355e2d1aa296453f86 | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, num_classes=10, out_ch_conv1=64, out_ch_conv2=256,
out_ch_conv3=384, out_ch_conv4=256, out_... |
nnConv2dSymQuant | # 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 torch.nn as nn
import torch.nn.functional as F
from torch.nn.modules.utils import _pair
class SymmetricQuantizeDequantize(torch.autograd.Function):
@staticmethod
def forward(ctx, input, precision, clamp_val):
ctx.save_for_backward(input)
"""
Compute quantization st... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.functional as F
from torch.nn.modules.util... | IBM/energy-efficient-resilience | nnConv2dSymQuant | false | 17,419 | [
"Apache-2.0"
] | 4 | 13dfcac143df218abe20ed8d8752a0bd7e5a424b | https://github.com/IBM/energy-efficient-resilience/tree/13dfcac143df218abe20ed8d8752a0bd7e5a424b | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.modules.utils import _pair
class SymmetricQuantizeDequantize(torch.autograd.Function):
@staticmethod
def forward(ctx, input, precision, clamp_val):
ctx.save_for_backward(input)
"""
Compute quantization st... |
NonAttentiveTacotronLoss | # 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 import nn
import torch.nn.functional as F
class NonAttentiveTacotronLoss(nn.Module):
def __init__(self, sample_rate: 'int', hop_size: 'int'):
super(NonAttentiveTacotronLoss, self).__init__()
self.sample_rate = sample_rate
self.hop_size = hop_size
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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
a... | IMDxD/NonAttentiveTacotron | NonAttentiveTacotronLoss | false | 17,420 | [
"MIT"
] | 4 | a227fba1bdfa4c5ec63a0f0364313f3ac0fef1ba | https://github.com/IMDxD/NonAttentiveTacotron/tree/a227fba1bdfa4c5ec63a0f0364313f3ac0fef1ba | import torch
from torch import nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, sample_rate: 'int', hop_size: 'int'):
super().__init__()
self.sample_rate = sample_rate
self.hop_size = hop_size
def forward(self, prenet_mels, postnet_mels, model_durations,
... |
layer_1_to_2 | # 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 numpy as np
import torch.nn as nn
def contractions_1_to_2(inputs, dim, normalization='inf', normalization_val=1.0
):
sum_all = torch.sum(inputs, dim=2).unsqueeze(dim=2)
op1 = torch.diag_embed(inputs, dim1=2, dim2=3)
op2 = torch.diag_embed(torch.cat([sum_all for d in range(dim)], di... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import numpy as np
import torch.nn as nn
assert_size_stride = torch._C._dynamo.g... | HyTruongSon/InvariantGraphNetworks-PyTorch | layer_1_to_2 | false | 17,421 | [
"Apache-2.0"
] | 7 | da9fdaa4f858d6fcae14b08a59d4b172a2aabaf8 | https://github.com/HyTruongSon/InvariantGraphNetworks-PyTorch/tree/da9fdaa4f858d6fcae14b08a59d4b172a2aabaf8 | import torch
import numpy as np
import torch.nn as nn
def contractions_1_to_2(inputs, dim, normalization='inf', normalization_val=1.0
):
sum_all = torch.sum(inputs, dim=2).unsqueeze(dim=2)
op1 = torch.diag_embed(inputs, dim1=2, dim2=3)
op2 = torch.diag_embed(torch.cat([sum_all for d in range(dim)], di... |
MRAE | # 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 torch.nn as nn
class MRAE(nn.Module):
def __init__(self):
super(MRAE, self).__init__()
def forward(self, output, target, mask=None):
relative_diff = torch.abs(output - target) / (target + 1.0 / 65535.0)
if mask is not None:
relative_diff = mask * relat... | 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
... | IVRL/Multi-Modal-Spectral-Image-Super-Resolution | MRAE | false | 17,422 | [
"MIT"
] | 9 | 6afe35c16d4cc2466e5eb51f3ddc39b43f6f765e | https://github.com/IVRL/Multi-Modal-Spectral-Image-Super-Resolution/tree/6afe35c16d4cc2466e5eb51f3ddc39b43f6f765e | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, output, target, mask=None):
relative_diff = torch.abs(output - target) / (target + 1.0 / 65535.0)
if mask is not None:
relative_diff = mask * relative_diff
... |
GLU | # 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 torch.nn as nn
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
class GLU(nn.Module):
def __init__(self, dim):
super().__init__()
self.dim = dim
def forward(self, x):
out, gate = x.chunk(2, dim=self.dim)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
assert_size_str... | IIP-Sogang/Audio-Visual-Speech-Recognition | GLU | false | 17,423 | [
"MIT"
] | 9 | bd03be91135acbc6162b83092d462b7fe71dd007 | https://github.com/IIP-Sogang/Audio-Visual-Speech-Recognition/tree/bd03be91135acbc6162b83092d462b7fe71dd007 | import torch
import torch.nn as nn
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
class Model(nn.Module):
def __init__(self, dim):
super().__init__()
self.dim = dim
def forward(self, x):
out, gate = x.chunk(2, dim=self.dim)
... |
tofp16 | # 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 torch.utils.data
import torch
import torch.nn as nn
class tofp16(nn.Module):
def __init__(self):
super(tofp16, self).__init__()
def forward(self, input):
return input.half()
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.utils.data
import torch
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cud... | Icep2020/CrowdGAN | tofp16 | false | 17,424 | [
"MIT"
] | 7 | 4adebaa09460f2f8296d368ffeba03f32c963d4d | https://github.com/Icep2020/CrowdGAN/tree/4adebaa09460f2f8296d368ffeba03f32c963d4d | import torch
import torch.utils.data
import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, input):
return input.half()
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return []
|
GlobalAveragePooling2d | # 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 torch as pt
import torch.nn as nn
class GlobalAveragePooling2d(nn.Module):
"""class for performing global average pooling on 2d feature maps"""
def forward(self, x):
"""
calculates the average of each feature map in the tensor
:param x: input tensor of shape [batc... | 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... | IljaManakov/Autoencoders | GlobalAveragePooling2d | false | 17,425 | [
"MIT"
] | 4 | bd2ccc6decda37a004cc57a41dcd406752c21d61 | https://github.com/IljaManakov/Autoencoders/tree/bd2ccc6decda37a004cc57a41dcd406752c21d61 | import torch
import torch as pt
import torch.nn as nn
class Model(nn.Module):
"""class for performing global average pooling on 2d feature maps"""
def forward(self, x):
"""
calculates the average of each feature map in the tensor
:param x: input tensor of shape [batch, channels, heig... |
ComplexConvTranspose2d | # 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 torch.nn as nn
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
class ComplexConvTranspose2d(nn.Module):
def __init__(self, in_channel, out_channel, kernel_size, stride=1,
padding=0, output_padding=0, dilation=1, groups=1, bias=Tru... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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
import torch.onnx.operators
import... | IIP-Sogang/Audio-Visual-Speech-Recognition | ComplexConvTranspose2d | false | 17,426 | [
"MIT"
] | 9 | bd03be91135acbc6162b83092d462b7fe71dd007 | https://github.com/IIP-Sogang/Audio-Visual-Speech-Recognition/tree/bd03be91135acbc6162b83092d462b7fe71dd007 | import torch
import torch.nn as nn
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
class Model(nn.Module):
def __init__(self, in_channel, out_channel, kernel_size, stride=1,
padding=0, output_padding=0, dilation=1, groups=1, bias=True, **kwargs
... |
FeatureDiscriminator | # 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 torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class FeatureDiscriminator(nn.Module):
def __init__(self):
super(FeatureDiscriminator, self).__init__()
self.conv1 = nn.Conv2d(in_channels=512, out_channels=256,
kernel_size=1, stride=1)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | HotaekHan/Synthetically_Supervised_Text_Recognition | FeatureDiscriminator | false | 17,427 | [
"MIT"
] | 8 | a6bb7d3039b1280c6efe177b69d8b985d2e13285 | https://github.com/HotaekHan/Synthetically_Supervised_Text_Recognition/tree/a6bb7d3039b1280c6efe177b69d8b985d2e13285 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class Model(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(in_channels=512, out_channels=256,
kernel_size=1, stride=1)
self.conv2 = nn.Conv2d(in_channels=256, ... |
ActorNetwork | # 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 torch.nn as nn
import torch.nn.functional as F
class ActorNetwork(nn.Module):
def __init__(self, input_size, hidden_size, action_size):
super(ActorNetwork, self).__init__()
self.fc1 = nn.Linear(input_size, hidden_size)
self.fc2 = nn.Linear(hidden_size, hidden_size)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | IandRover/meta-gradient_RL | ActorNetwork | false | 17,428 | [
"MIT"
] | 6 | 5d2539aceb9fa68b1849feac7d37741f9e5f83a3 | https://github.com/IandRover/meta-gradient_RL/tree/5d2539aceb9fa68b1849feac7d37741f9e5f83a3 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, input_size, hidden_size, action_size):
super().__init__()
self.fc1 = nn.Linear(input_size, hidden_size)
self.fc2 = nn.Linear(hidden_size, hidden_size)
self.fc3 = nn.Linear... |
CircleLoss | # 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 typing import *
import torch.nn as nn
import torch.nn.functional as F
class CircleLoss(nn.Module):
def __init__(self, gamma, m):
super().__init__()
self.gamma = gamma
self.m = m
def forward(self, s_p, s_n):
alpha_p = torch.clamp_min(1 + self.m - s_p, 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, math as tl_math
from typing... | IntelLabs/MICSAS | CircleLoss | false | 17,429 | [
"MIT",
"BSD-3-Clause"
] | 7 | 4124991a683cc10004e403f3f3eb442f58616519 | https://github.com/IntelLabs/MICSAS/tree/4124991a683cc10004e403f3f3eb442f58616519 | import torch
from typing import *
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, gamma, m):
super().__init__()
self.gamma = gamma
self.m = m
def forward(self, s_p, s_n):
alpha_p = torch.clamp_min(1 + self.m - s_p, 0)
a... |
GeneralRelu | # 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 import nn
import torch.nn.functional as F
from typing import *
class GeneralRelu(nn.Module):
def __init__(self, leak=None, sub=None, maxv=None):
super().__init__()
self.leak, self.sub, self.maxv = leak, sub, maxv
def forward(self, x):
x = F.leaky_relu(x, 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 import nn
from typing import *
assert_size_stride = torch._C._dynamo.guards.as... | ImadDabbura/fastai-courses | GeneralRelu | false | 17,430 | [
"Apache-2.0"
] | 3 | 053637a2dd3b4ad6c35f97a13f3fba87af1d3940 | https://github.com/ImadDabbura/fastai-courses/tree/053637a2dd3b4ad6c35f97a13f3fba87af1d3940 | import torch
from torch import nn
import torch.nn.functional as F
from typing import *
class Model(nn.Module):
def __init__(self, leak=None, sub=None, maxv=None):
super().__init__()
self.leak, self.sub, self.maxv = leak, sub, maxv
def forward(self, x):
x = F.leaky_relu(x, self.leak) ... |
NetVLAD | # 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 numpy as np
from torch import nn
import torch.nn.functional as F
from sklearn.neighbors import NearestNeighbors
class NetVLAD(nn.Module):
"""NetVLAD layer implementation"""
def __init__(self, num_clusters=64, dim=128, normalize_input=True,
vladv2=False):
"""
Args:
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | ByungHeeCha/visual_localization | NetVLAD | false | 17,431 | [
"BSD-3-Clause"
] | 3 | 787fb8f6ee5c6e69ece9e83a016d15596e5524bc | https://github.com/ByungHeeCha/visual_localization/tree/787fb8f6ee5c6e69ece9e83a016d15596e5524bc | import torch
import numpy as np
from torch import nn
import torch.nn.functional as F
from sklearn.neighbors import NearestNeighbors
class Model(nn.Module):
"""NetVLAD layer implementation"""
def __init__(self, num_clusters=64, dim=128, normalize_input=True,
vladv2=False):
"""
Args:
... |
SELayer | # 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 math
import torch
from torch import nn
from torch.nn import functional as F
import torch.onnx
from torch.optim.lr_scheduler import *
def composite_swish(inputs_1, inputs_2):
return inputs_1 * torch.sigmoid(inputs_2)
def swish(x):
return torch.sigmoid(x) * x
class _Conv2dSamePadding(nn.Conv2d):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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 math
from torch import nn
from torch.nn import functional as F
import tor... | IST-DASLab/ACDC | SELayer | false | 17,432 | [
"Apache-2.0"
] | 6 | ac53210b6adc1f2506ff909de08172ed9cad25d5 | https://github.com/IST-DASLab/ACDC/tree/ac53210b6adc1f2506ff909de08172ed9cad25d5 | import math
import torch
from torch import nn
from torch.nn import functional as F
import torch.onnx
from torch.optim.lr_scheduler import *
def composite_swish(inputs_1, inputs_2):
return inputs_1 * torch.sigmoid(inputs_2)
def swish(x):
return torch.sigmoid(x) * x
class _Conv2dSamePadding(nn.Conv2d):
... |
LayerNorm | # 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 import nn
from typing import *
class LayerNorm(nn.Module):
"""Normalize by channels, height and width for images."""
__constants__ = ['eps']
def __init__(self, eps):
super().__init__()
self.eps = eps
self.gamma = nn.Parameter(torch.ones(1))
self.bet... | 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
from typing import *
assert_size_stride = torch._C._dynamo... | ImadDabbura/fastai-courses | LayerNorm | false | 17,433 | [
"Apache-2.0"
] | 3 | 053637a2dd3b4ad6c35f97a13f3fba87af1d3940 | https://github.com/ImadDabbura/fastai-courses/tree/053637a2dd3b4ad6c35f97a13f3fba87af1d3940 | import torch
from torch import nn
from typing import *
class Model(nn.Module):
"""Normalize by channels, height and width for images."""
__constants__ = ['eps']
def __init__(self, eps):
super().__init__()
self.eps = eps
self.gamma = nn.Parameter(torch.ones(1))
self.beta = ... |
InstanceNorm | # 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 import nn
from typing import *
class InstanceNorm(nn.Module):
"""Normalize by height and width for images."""
__constants__ = ['eps']
def __init__(self, nf, mom, eps):
super().__init__()
self.eps = eps
self.gamma = nn.Parameter(torch.ones(nf, 1, 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.triton_helpers import libdevice
from torch import nn
from typing import *
assert_size_stride = torch._C._dynamo... | ImadDabbura/fastai-courses | InstanceNorm | false | 17,434 | [
"Apache-2.0"
] | 3 | 053637a2dd3b4ad6c35f97a13f3fba87af1d3940 | https://github.com/ImadDabbura/fastai-courses/tree/053637a2dd3b4ad6c35f97a13f3fba87af1d3940 | import torch
from torch import nn
from typing import *
class Model(nn.Module):
"""Normalize by height and width for images."""
__constants__ = ['eps']
def __init__(self, nf, mom, eps):
super().__init__()
self.eps = eps
self.gamma = nn.Parameter(torch.ones(nf, 1, 1))
self.b... |
layer_basic | # 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 numpy as np
import torch.nn as nn
class layer_basic(nn.Module):
"""
:param name: name of layer
:param input_depth: D
:param output_depth: S
:param inputs: N x D x m x m tensor
:return: output: N x S x m x m tensor
"""
def __init__(self, input_depth, output_depth, n... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import numpy as np
import torch.nn as nn
assert_size_stride = torch._C._dynamo.g... | HyTruongSon/InvariantGraphNetworks-PyTorch | layer_basic | false | 17,435 | [
"Apache-2.0"
] | 7 | da9fdaa4f858d6fcae14b08a59d4b172a2aabaf8 | https://github.com/HyTruongSon/InvariantGraphNetworks-PyTorch/tree/da9fdaa4f858d6fcae14b08a59d4b172a2aabaf8 | import torch
import numpy as np
import torch.nn as nn
class Model(nn.Module):
"""
:param name: name of layer
:param input_depth: D
:param output_depth: S
:param inputs: N x D x m x m tensor
:return: output: N x S x m x m tensor
"""
def __init__(self, input_depth, output_depth, normali... |
SamePad2dStrong | # 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 math
import torch
import torch.nn.functional as F
import torch.nn as nn
class SamePad2dStrong(nn.Module):
"""Mimics tensorflow's 'SAME' padding.
"""
def __init__(self, kernel_size, stride):
super(SamePad2dStrong, self).__init__()
self.kernel_size = torch.nn.modules.utils._pair(kern... | 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... | IssamLaradji/wisenet | SamePad2dStrong | false | 17,436 | [
"Apache-2.0"
] | 7 | 881457f5168815f5e9d03f110244842d539747a0 | https://github.com/IssamLaradji/wisenet/tree/881457f5168815f5e9d03f110244842d539747a0 | import math
import torch
import torch.nn.functional as F
import torch.nn as nn
class Model(nn.Module):
"""Mimics tensorflow's 'SAME' padding.
"""
def __init__(self, kernel_size, stride):
super().__init__()
self.kernel_size = torch.nn.modules.utils._pair(kernel_size)
self.stride = ... |
InstrShifting | # 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 torch.nn as nn
class InstrShifting(nn.Module):
""" Sub-Instruction Shifting Module.
Decide whether the current subinstruction will
be completed by the next action or not. """
def __init__(self, rnn_hidden_size, shift_hidden_size, action_emb_size,
max_subinstr_size... | import torch
from torch._inductor.select_algorithm import extern_kernels
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | IMNearth/Curriculum-Learning-For-VLN | InstrShifting | false | 17,437 | [
"MIT"
] | 8 | d2fe1286eb295dc8c63a0c886b35883f32481d85 | https://github.com/IMNearth/Curriculum-Learning-For-VLN/tree/d2fe1286eb295dc8c63a0c886b35883f32481d85 | import torch
import torch.nn as nn
class Model(nn.Module):
""" Sub-Instruction Shifting Module.
Decide whether the current subinstruction will
be completed by the next action or not. """
def __init__(self, rnn_hidden_size, shift_hidden_size, action_emb_size,
max_subinstr_size, drop_r... |
DiceLoss | # 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 collections
import torch
import warnings
from typing import Optional
from typing import Union
from typing import Callable
from typing import Any
from typing import Tuple
import torch.nn
from torch.nn.modules.loss import _Loss
from enum import Enum
import collections.abc
def issequenceiterable(obj: 'Any') ->boo... | 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
from typing import Optional
from typing import Union
from typing import Callable
from typing import Any
from typing impor... | Irme/MONAI | DiceLoss | false | 17,438 | [
"Apache-2.0"
] | 3 | dc4bf661831b14f4231cb325cc1b15d38c1e406c | https://github.com/Irme/MONAI/tree/dc4bf661831b14f4231cb325cc1b15d38c1e406c | import collections
import torch
import warnings
from typing import Optional
from typing import Union
from typing import Callable
from typing import Any
from typing import Tuple
import torch.nn
from torch.nn.modules.loss import _Loss
from enum import Enum
import collections.abc
def issequenceiterable(obj: 'Any') ->boo... |
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 torch.utils.data
from sklearn import *
class BCEFocalLoss(torch.nn.Module):
"""
二分类的Focalloss alpha 固定
"""
def __init__(self, gamma=2, alpha=0.25, reduction='elementwise_mean'):
super().__init__()
self.gamma = gamma
self.alpha = alpha
self.reduction... | 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... | CityU-AIM-Group/SIGMA | BCEFocalLoss | false | 17,439 | [
"MIT"
] | 5 | 19f89777db8d42f750a9b87756d3326c7efd18f5 | https://github.com/CityU-AIM-Group/SIGMA/tree/19f89777db8d42f750a9b87756d3326c7efd18f5 | import torch
import torch.utils.data
from sklearn import *
class Model(torch.nn.Module):
"""
二分类的Focalloss alpha 固定
"""
def __init__(self, gamma=2, alpha=0.25, reduction='elementwise_mean'):
super().__init__()
self.gamma = gamma
self.alpha = alpha
self.reduction = redu... |
BiAttention | # 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 torchvision.transforms import functional as F
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils import weight_norm
import torch.nn.modules.module
class FCNet(nn.Module):
def __init__(self, in_size, out_size, activate=None, drop=0.0):
super... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | ChCh1999/RTPB | BiAttention | false | 17,440 | [
"MIT"
] | 8 | 1066a3bfe4fe1b41eff74fd152936880302a60a2 | https://github.com/ChCh1999/RTPB/tree/1066a3bfe4fe1b41eff74fd152936880302a60a2 | import torch
from torchvision.transforms import functional as F
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils import weight_norm
import torch.nn.modules.module
class FCNet(nn.Module):
def __init__(self, in_size, out_size, activate=None, drop=0.0):
super... |
ScaledDotProductAttention | # 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 import nn
from typing import Optional
class ScaledDotProductAttention(nn.Module):
def __init__(self, dropout: 'Optional[float]'=None, scale: 'bool'=True):
super(ScaledDotProductAttention, self).__init__()
if dropout is not None:
self.dropout = nn.Dropout(p=drop... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | IusztinPaul/yacht | ScaledDotProductAttention | false | 17,441 | [
"Apache-2.0"
] | 5 | c68ab7c66bde860bb91534c29e97772ba328adb5 | https://github.com/IusztinPaul/yacht/tree/c68ab7c66bde860bb91534c29e97772ba328adb5 | import torch
from torch import nn
from typing import Optional
class Model(nn.Module):
def __init__(self, dropout: 'Optional[float]'=None, scale: 'bool'=True):
super().__init__()
if dropout is not None:
self.dropout = nn.Dropout(p=dropout)
else:
self.dropout = dropo... |
ResBlock | # 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 import nn
import torch.distributed
class ResBlock(nn.Module):
def __init__(self, feature_size, action_size):
super(ResBlock, self).__init__()
self.lin_1 = nn.Linear(feature_size + action_size, feature_size)
self.lin_2 = nn.Linear(feature_size + action_size, feature... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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.distributed
assert_size_stride = torch._C._dyn... | Improbable-AI/curiosity_baselines | ResBlock | false | 17,442 | [
"MIT"
] | 5 | 42dca92b2fb66c0790a72206bf48595d3b5b487f | https://github.com/Improbable-AI/curiosity_baselines/tree/42dca92b2fb66c0790a72206bf48595d3b5b487f | import torch
from torch import nn
import torch.distributed
class Model(nn.Module):
def __init__(self, feature_size, action_size):
super().__init__()
self.lin_1 = nn.Linear(feature_size + action_size, feature_size)
self.lin_2 = nn.Linear(feature_size + action_size, feature_size)
def f... |
ZReLU | # 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 numpy
import torch
import numpy as np
import torch.nn as nn
import numpy.matlib
def cylindricalToPolarConversion(input1, input2=None):
if input2 is None:
"""input1 is tensor of [B,C,H,W,D,2] contains both real and imaginary channels
in the last dims"""
ndims = input1.ndimension()
... | 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_... | HMS-CardiacMR/MyoMapNet-Myocardial-Parametric-Mapping | ZReLU | false | 17,443 | [
"MIT"
] | 4 | 1e2dee8d6d1f97722eba91618462537faf9efba7 | https://github.com/HMS-CardiacMR/MyoMapNet-Myocardial-Parametric-Mapping/tree/1e2dee8d6d1f97722eba91618462537faf9efba7 | import numpy
import torch
import numpy as np
import torch.nn as nn
import numpy.matlib
def cylindricalToPolarConversion(input1, input2=None):
if input2 is None:
"""input1 is tensor of [B,C,H,W,D,2] contains both real and imaginary channels
in the last dims"""
ndims = input1.ndimension()
... |
Dense | # 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 torch.nn as nn
import torch.nn.functional as functions
class Dense(nn.Module):
def __init__(self):
super(Dense, self).__init__()
self.fc1 = nn.Linear(6 * 7, 32)
self.fc2 = nn.Linear(32, 16)
self.probhead = nn.Linear(16, 7)
self.valuehead = nn.Linear(16,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | IvLabs/model-based-RL | Dense | false | 17,444 | [
"MIT"
] | 7 | 8d22eabf7bf2601629015ef6c869e3850c306d6f | https://github.com/IvLabs/model-based-RL/tree/8d22eabf7bf2601629015ef6c869e3850c306d6f | import torch
import torch.nn as nn
import torch.nn.functional as functions
class Model(nn.Module):
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(6 * 7, 32)
self.fc2 = nn.Linear(32, 16)
self.probhead = nn.Linear(16, 7)
self.valuehead = nn.Linear(16, 1)
... |
ActorCritic | # 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 torch.nn as nn
import torch.nn.functional as F
class ActorCritic(nn.Module):
""" Actor Critic neural network with shared body.
The Actor maps states (actions) to action, log_probs, entropy.
The Critic maps states to values.
"""
def __init__(self, state_size, action_size, seed=0):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ImmanuelXIV/ppo-self-play | ActorCritic | false | 17,445 | [
"MIT"
] | 7 | 21c000492b2450628b5a506d4101b7b12e5755e0 | https://github.com/ImmanuelXIV/ppo-self-play/tree/21c000492b2450628b5a506d4101b7b12e5755e0 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
""" Actor Critic neural network with shared body.
The Actor maps states (actions) to action, log_probs, entropy.
The Critic maps states to values.
"""
def __init__(self, state_size, action_size, seed=0):
"""... |
ResidualSequential | # 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 torch.optim
import torch.nn as nn
import torch
import torch.nn.init
class ResidualSequential(nn.Sequential):
def __init__(self, *args):
super(ResidualSequential, self).__init__(*args)
def forward(self, x):
out = super(ResidualSequential, self).forward(x)
x_ = None... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.optim
import torch.nn as nn
import torch
import torch.nn.init
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
e... | Jay-Lewis/phase_retrieval | ResidualSequential | false | 17,446 | [
"MIT"
] | 4 | 799cef92852c53e62e2a548f605652923e979456 | https://github.com/Jay-Lewis/phase_retrieval/tree/799cef92852c53e62e2a548f605652923e979456 | import torch
import torch.optim
import torch.nn as nn
import torch
import torch.nn.init
class Model(nn.Sequential):
def __init__(self, *args):
super().__init__(*args)
def forward(self, x):
out = super(ResidualSequential, self).forward(x)
x_ = None
if out.size(2) != x.size(2) ... |
ConvLayer | # 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
class ConvLayer(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride):
super(ConvLayer, self).__init__()
reflection_padding = kernel_size // 2
self.reflection_pad = torch.nn.ReflectionPad2d(reflection_padding)
self.conv2d = torch.nn.Conv... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
assert_size_s... | JEF1056/Reconstruction-Style | ConvLayer | false | 17,447 | [
"MIT"
] | 6 | 3430d9e9f05c6980ae251cf15b619148a2c899d6 | https://github.com/JEF1056/Reconstruction-Style/tree/3430d9e9f05c6980ae251cf15b619148a2c899d6 | import torch
class Model(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride):
super().__init__()
reflection_padding = kernel_size // 2
self.reflection_pad = torch.nn.ReflectionPad2d(reflection_padding)
self.conv2d = torch.nn.Conv2d(in_channels, out... |
Decoder | # 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 torch.nn as nn
class Decoder(nn.Module):
def __init__(self, dim_encoding, vocab_size):
super().__init__()
self.E = nn.Embedding(dim_encoding, vocab_size)
self.b = nn.Parameter(torch.zeros(1, vocab_size))
def forward(self, Z, targets):
scores = Z @ self.E.w... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | J-zin/SNUH | Decoder | false | 17,448 | [
"MIT"
] | 4 | e4bde66609e1480f890b8386046431d488b825bd | https://github.com/J-zin/SNUH/tree/e4bde66609e1480f890b8386046431d488b825bd | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, dim_encoding, vocab_size):
super().__init__()
self.E = nn.Embedding(dim_encoding, vocab_size)
self.b = nn.Parameter(torch.zeros(1, vocab_size))
def forward(self, Z, targets):
scores = Z @ self.E.wei... |
Resample | # 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 import nn
from typing import Optional
class LinearStack(nn.Module):
def __init__(self, in_features: 'int', out_features: 'int',
activation_fn: 'Optional[nn.Module]'=None, n: 'int'=1,
hidden_features: 'Optional[int]'=None, dropout: 'Optional[float]'=None
):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
from typing import Optional
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch.... | IusztinPaul/yacht | Resample | false | 17,449 | [
"Apache-2.0"
] | 5 | c68ab7c66bde860bb91534c29e97772ba328adb5 | https://github.com/IusztinPaul/yacht/tree/c68ab7c66bde860bb91534c29e97772ba328adb5 | import torch
from torch import nn
from typing import Optional
class LinearStack(nn.Module):
def __init__(self, in_features: 'int', out_features: 'int',
activation_fn: 'Optional[nn.Module]'=None, n: 'int'=1,
hidden_features: 'Optional[int]'=None, dropout: 'Optional[float]'=None
):
... |
ResampleNorm | # 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 import nn
import torch.nn.functional as F
class LearnableInterpolation(nn.Module):
def __init__(self, input_size: 'int', output_size: 'int', trainable:
'bool'=False):
super().__init__()
self.input_size = input_size
self.output_size = output_size
sel... | 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
import torch.nn.functional as F
assert_size_stride = torch... | IusztinPaul/yacht | ResampleNorm | false | 17,450 | [
"Apache-2.0"
] | 5 | c68ab7c66bde860bb91534c29e97772ba328adb5 | https://github.com/IusztinPaul/yacht/tree/c68ab7c66bde860bb91534c29e97772ba328adb5 | import torch
from torch import nn
import torch.nn.functional as F
class LearnableInterpolation(nn.Module):
def __init__(self, input_size: 'int', output_size: 'int', trainable:
'bool'=False):
super().__init__()
self.input_size = input_size
self.output_size = output_size
sel... |
IntervalObservationEncoder | # 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 import nn
class IntervalObservationEncoder(nn.Module):
def __init__(self, num_input_channel: 'int', num_output_channel: 'int',
kernel_size: 'int', initial_output_weight_value: 'float'):
super().__init__()
assert initial_output_weight_value <= 1
self.conv_1d... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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... | IusztinPaul/yacht | IntervalObservationEncoder | false | 17,451 | [
"Apache-2.0"
] | 5 | c68ab7c66bde860bb91534c29e97772ba328adb5 | https://github.com/IusztinPaul/yacht/tree/c68ab7c66bde860bb91534c29e97772ba328adb5 | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, num_input_channel: 'int', num_output_channel: 'int',
kernel_size: 'int', initial_output_weight_value: 'float'):
super().__init__()
assert initial_output_weight_value <= 1
self.conv_1d = nn.Conv1d(in_chann... |
ComplexConv2d | # 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 torch.nn as nn
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
class ComplexConv2d(nn.Module):
def __init__(self, in_channel, out_channel, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=True, **kwargs):
super... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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
import torch.onnx.operators
import... | IIP-Sogang/Audio-Visual-Speech-Recognition | ComplexConv2d | false | 17,452 | [
"MIT"
] | 9 | bd03be91135acbc6162b83092d462b7fe71dd007 | https://github.com/IIP-Sogang/Audio-Visual-Speech-Recognition/tree/bd03be91135acbc6162b83092d462b7fe71dd007 | import torch
import torch.nn as nn
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
class Model(nn.Module):
def __init__(self, in_channel, out_channel, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=True, **kwargs):
super().__ini... |
SquashingCosine_Classifier | # 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 math
import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
class SquashingCosine_Classifier(nn.Module):
def __init__(self, in_dims, out_dims, scale=16, margin=0.5, init_std=0.001
):
super(SquashingCosine_Classifier, self).__init__()
self.in_dims = in_dims
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import ... | JKozerawski/BLT | SquashingCosine_Classifier | false | 17,453 | [
"MIT"
] | 5 | 6f3a6f4dc3c832b62c4ac3f3baf34b6a0bd6e181 | https://github.com/JKozerawski/BLT/tree/6f3a6f4dc3c832b62c4ac3f3baf34b6a0bd6e181 | import math
import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
class Model(nn.Module):
def __init__(self, in_dims, out_dims, scale=16, margin=0.5, init_std=0.001
):
super().__init__()
self.in_dims = in_dims
self.out_dims = out_dims
self.scale = sca... |
FscoreMetric | # 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 torch.nn as nn
def f_score(pr, gt, beta=1, eps=1e-07, threshold=0.5):
"""dice score(also referred to as F1-score)"""
if threshold is not None:
pr = (pr > threshold).float()
tp = torch.sum(gt * pr)
fp = torch.sum(pr) - tp
fn = torch.sum(gt) - tp
score = ((1 + beta **... | 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... | JACKYLUO1991/HybridNet | FscoreMetric | false | 17,454 | [
"Apache-2.0"
] | 6 | eb97d8a048ca4bb4087bc542360172e169a08dbf | https://github.com/JACKYLUO1991/HybridNet/tree/eb97d8a048ca4bb4087bc542360172e169a08dbf | import torch
import torch.nn as nn
def f_score(pr, gt, beta=1, eps=1e-07, threshold=0.5):
"""dice score(also referred to as F1-score)"""
if threshold is not None:
pr = (pr > threshold).float()
tp = torch.sum(gt * pr)
fp = torch.sum(pr) - tp
fn = torch.sum(gt) - tp
score = ((1 + beta **... |
MnistMLP | # 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 import nn
from torch.nn import functional as F
import torch.onnx
from torch.optim.lr_scheduler import *
class MnistMLP(nn.Module):
def __init__(self, hidden_size=500):
super(MnistMLP, self).__init__()
self.hidden_size = hidden_size
self.fc1 = nn.Linear(784, hidden_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | IST-DASLab/ACDC | MnistMLP | false | 17,455 | [
"Apache-2.0"
] | 6 | ac53210b6adc1f2506ff909de08172ed9cad25d5 | https://github.com/IST-DASLab/ACDC/tree/ac53210b6adc1f2506ff909de08172ed9cad25d5 | import torch
from torch import nn
from torch.nn import functional as F
import torch.onnx
from torch.optim.lr_scheduler import *
class Model(nn.Module):
def __init__(self, hidden_size=500):
super().__init__()
self.hidden_size = hidden_size
self.fc1 = nn.Linear(784, hidden_size)
sel... |
SqueezeAndExcite | # 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 torch.nn as nn
class SqueezeAndExcite(nn.Module):
def __init__(self, channels, squeeze_channels, se_ratio):
super(SqueezeAndExcite, self).__init__()
squeeze_channels = squeeze_channels * se_ratio
if not squeeze_channels.is_integer():
raise ValueError('chann... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | JACKYLUO1991/HybridNet | SqueezeAndExcite | false | 17,456 | [
"Apache-2.0"
] | 6 | eb97d8a048ca4bb4087bc542360172e169a08dbf | https://github.com/JACKYLUO1991/HybridNet/tree/eb97d8a048ca4bb4087bc542360172e169a08dbf | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, channels, squeeze_channels, se_ratio):
super().__init__()
squeeze_channels = squeeze_channels * se_ratio
if not squeeze_channels.is_integer():
raise ValueError('channels must be divisible by 1 / rati... |
ResForward | # 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 import nn
import torch.distributed
class ResBlock(nn.Module):
def __init__(self, feature_size, action_size):
super(ResBlock, self).__init__()
self.lin_1 = nn.Linear(feature_size + action_size, feature_size)
self.lin_2 = nn.Linear(feature_size + action_size, feature... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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.distributed
assert_size_stride = torch._C._dyn... | Improbable-AI/curiosity_baselines | ResForward | false | 17,457 | [
"MIT"
] | 5 | 42dca92b2fb66c0790a72206bf48595d3b5b487f | https://github.com/Improbable-AI/curiosity_baselines/tree/42dca92b2fb66c0790a72206bf48595d3b5b487f | import torch
from torch import nn
import torch.distributed
class ResBlock(nn.Module):
def __init__(self, feature_size, action_size):
super().__init__()
self.lin_1 = nn.Linear(feature_size + action_size, feature_size)
self.lin_2 = nn.Linear(feature_size + action_size, feature_size)
de... |
Autoencoder | # 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 import nn
class Autoencoder(nn.Module):
def __init__(self, input_dim, output_dim, n_hid, n_bottleneck):
super(Autoencoder, self).__init__()
self.fc1 = nn.Linear(input_dim, n_hid)
self.fc2 = nn.Linear(n_hid, n_bottleneck)
self.fc3 = nn.Linear(n_bottleneck, n... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | JavierAntoran/tiger-costume | Autoencoder | false | 17,458 | [
"MIT"
] | 10 | 975661dfab2c435281f74c6be86529b16881ebcb | https://github.com/JavierAntoran/tiger-costume/tree/975661dfab2c435281f74c6be86529b16881ebcb | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, input_dim, output_dim, n_hid, n_bottleneck):
super().__init__()
self.fc1 = nn.Linear(input_dim, n_hid)
self.fc2 = nn.Linear(n_hid, n_bottleneck)
self.fc3 = nn.Linear(n_bottleneck, n_hid)
self.fc4 ... |
DQfDNetwork | # 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 torch.nn as nn
import torch.nn.functional as F
class DQfDNetwork(nn.Module):
def __init__(self, in_size, out_size):
super(DQfDNetwork, self).__init__()
HIDDEN_SIZE = 30
self.f1 = nn.Linear(in_size, HIDDEN_SIZE)
self.f2 = nn.Linear(HIDDEN_SIZE, HIDDEN_SIZE)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | DPS0340/DQNDemo | DQfDNetwork | false | 17,459 | [
"MIT"
] | 8 | 5b57159ea8ff8a6b127cb18ff28da6696b40665b | https://github.com/DPS0340/DQNDemo/tree/5b57159ea8ff8a6b127cb18ff28da6696b40665b | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, in_size, out_size):
super().__init__()
HIDDEN_SIZE = 30
self.f1 = nn.Linear(in_size, HIDDEN_SIZE)
self.f2 = nn.Linear(HIDDEN_SIZE, HIDDEN_SIZE)
self.f3 = nn.Linear... |
NeuralClassifier | # 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 torch.nn as nn
import torch.utils.data
class NeuralClassifier(nn.Module):
def __init__(self, input_size, n_classes):
super(NeuralClassifier, self).__init__()
self.input_size = input_size
self.mapping1 = nn.Linear(input_size, input_size)
self.mapping2 = nn.Linea... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | JayWalker512/PacketGAN | NeuralClassifier | false | 17,460 | [
"MIT"
] | 5 | 93d4266ab9299c25ffd1f0aedf68fa4639f66572 | https://github.com/JayWalker512/PacketGAN/tree/93d4266ab9299c25ffd1f0aedf68fa4639f66572 | import torch
import torch.nn as nn
import torch.utils.data
class Model(nn.Module):
def __init__(self, input_size, n_classes):
super().__init__()
self.input_size = input_size
self.mapping1 = nn.Linear(input_size, input_size)
self.mapping2 = nn.Linear(input_size, n_classes)
... |
Sinkhorn | # 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 torch.utils.data
import torch.nn as nn
from sklearn import *
class Sinkhorn(nn.Module):
"""
BiStochastic Layer turns the input matrix into a bi-stochastic matrix.
Parameter: maximum iterations max_iter
a small number for numerical stability epsilon
Input: input matri... | 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
from sklearn import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_st... | CityU-AIM-Group/SIGMA | Sinkhorn | false | 17,461 | [
"MIT"
] | 5 | 19f89777db8d42f750a9b87756d3326c7efd18f5 | https://github.com/CityU-AIM-Group/SIGMA/tree/19f89777db8d42f750a9b87756d3326c7efd18f5 | import torch
import torch.utils.data
import torch.nn as nn
from sklearn import *
class Model(nn.Module):
"""
BiStochastic Layer turns the input matrix into a bi-stochastic matrix.
Parameter: maximum iterations max_iter
a small number for numerical stability epsilon
Input: input matrix s... |
TotalVariations | # 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.nn.modules.loss import _Loss
class TotalVariations(_Loss):
def forward(self, img1):
return torch.sum(torch.abs(img1[:, :, :-1] - img1[:, :, 1:])
) + torch.sum(torch.abs(img1[:, :-1, :] - img1[:, 1:, :]))
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def g... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch.nn.modules.loss import _Loss
assert_size_stride = torch._C._dy... | HMS-CardiacMR/MyoMapNet-Myocardial-Parametric-Mapping | TotalVariations | false | 17,462 | [
"MIT"
] | 4 | 1e2dee8d6d1f97722eba91618462537faf9efba7 | https://github.com/HMS-CardiacMR/MyoMapNet-Myocardial-Parametric-Mapping/tree/1e2dee8d6d1f97722eba91618462537faf9efba7 | import torch
from torch.nn.modules.loss import _Loss
class Model(_Loss):
def forward(self, img1):
return torch.sum(torch.abs(img1[:, :, :-1] - img1[:, :, 1:])
) + torch.sum(torch.abs(img1[:, :-1, :] - img1[:, 1:, :]))
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_in... |
Nloss_GD | # 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 numpy as np
from torch import nn
class Nloss_GD(nn.Module):
def __init__(self, dim):
super(Nloss_GD, self).__init__()
self.dim = dim
torch.manual_seed(0)
def get_likelihoods(self, X, Y, Beta, eps=1e-06):
inv_det = Beta.prod(dim=1)
if (inv_det < eps... | 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 numpy as np
from torch import nn
assert_size_stride = torch._C._dy... | JavierAntoran/tiger-costume | Nloss_GD | false | 17,463 | [
"MIT"
] | 10 | 975661dfab2c435281f74c6be86529b16881ebcb | https://github.com/JavierAntoran/tiger-costume/tree/975661dfab2c435281f74c6be86529b16881ebcb | import torch
import numpy as np
from torch import nn
class Model(nn.Module):
def __init__(self, dim):
super().__init__()
self.dim = dim
torch.manual_seed(0)
def get_likelihoods(self, X, Y, Beta, eps=1e-06):
inv_det = Beta.prod(dim=1)
if (inv_det < eps).any():
... |
LogisticRegressionBinaryClassifier | # 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 torch.nn as nn
import torch.utils.data
class LogisticRegressionBinaryClassifier(nn.Module):
def __init__(self, input_size):
super(LogisticRegressionBinaryClassifier, self).__init__()
self.input_size = input_size
self.mapping = nn.Linear(input_size, 1)
def forward(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | JayWalker512/PacketGAN | LogisticRegressionBinaryClassifier | false | 17,464 | [
"MIT"
] | 5 | 93d4266ab9299c25ffd1f0aedf68fa4639f66572 | https://github.com/JayWalker512/PacketGAN/tree/93d4266ab9299c25ffd1f0aedf68fa4639f66572 | import torch
import torch.nn as nn
import torch.utils.data
class Model(nn.Module):
def __init__(self, input_size):
super().__init__()
self.input_size = input_size
self.mapping = nn.Linear(input_size, 1)
def forward(self, x):
return torch.sigmoid(self.mapping(x))
def get_inp... |
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 torch as th
import torch.utils.data
import torch
import torch.autograd
class TVLoss(th.nn.Module):
def __init__(self, strength=1.0):
super(TVLoss, self).__init__()
self.strength = strength
def forward(self, input):
self.x_diff = input[:, :, 1:, :] - input[:, :, :-... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch as th
import torch.utils.data
import torch
import torch.auto... | JCBrouwer/maua | TVLoss | false | 17,465 | [
"BSD-2-Clause"
] | 9 | 4208023020bc56dd81f6933347f9c4e7c1853318 | https://github.com/JCBrouwer/maua/tree/4208023020bc56dd81f6933347f9c4e7c1853318 | import torch
import torch as th
import torch.utils.data
import torch
import torch.autograd
class Model(th.nn.Module):
def __init__(self, strength=1.0):
super().__init__()
self.strength = strength
def forward(self, input):
self.x_diff = input[:, :, 1:, :] - input[:, :, :-1, :]
... |
StridedNet | # 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 torch.nn.functional as F
import torch.nn as nn
class StridedNet(nn.Module):
def __init__(self):
super(StridedNet, self).__init__()
self.conv1 = nn.Conv2d(in_channels=1, out_channels=10, kernel_size=
6, stride=1, dilation=1)
self.pool1 = nn.MaxPool2d(kernel_... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | JHorcasitas/cnn_document_binarization | StridedNet | false | 17,466 | [
"MIT"
] | 9 | 075f76aed375ca14a53011f4dfeb12379debb5b3 | https://github.com/JHorcasitas/cnn_document_binarization/tree/075f76aed375ca14a53011f4dfeb12379debb5b3 | import torch
import torch.nn.functional as F
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(in_channels=1, out_channels=10, kernel_size=
6, stride=1, dilation=1)
self.pool1 = nn.MaxPool2d(kernel_size=2, stride=1, dil... |
ResidualBlock | # 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.nn import functional as F
class ConvLayer(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride):
super(ConvLayer, self).__init__()
reflection_padding = kernel_size // 2
self.reflection_pad = torch.nn.ReflectionPad2d(reflection_padding... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | JEF1056/Reconstruction-Style | ResidualBlock | false | 17,467 | [
"MIT"
] | 6 | 3430d9e9f05c6980ae251cf15b619148a2c899d6 | https://github.com/JEF1056/Reconstruction-Style/tree/3430d9e9f05c6980ae251cf15b619148a2c899d6 | import torch
from torch.nn import functional as F
class ConvLayer(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride):
super().__init__()
reflection_padding = kernel_size // 2
self.reflection_pad = torch.nn.ReflectionPad2d(reflection_padding)
self.... |
BoundaryDiscriminator | # 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 torch.nn as nn
class BoundaryDiscriminator(nn.Module):
def __init__(self):
super(BoundaryDiscriminator, self).__init__()
filter_num_list = [64, 128, 256, 512, 1]
self.conv1 = nn.Conv2d(1, filter_num_list[0], kernel_size=4, stride
=2, padding=2, bias=False)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | JACKYLUO1991/DCBNet | BoundaryDiscriminator | false | 17,468 | [
"MIT"
] | 6 | b797584b66ad99fe984f58268befb12ec60ccfae | https://github.com/JACKYLUO1991/DCBNet/tree/b797584b66ad99fe984f58268befb12ec60ccfae | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
filter_num_list = [64, 128, 256, 512, 1]
self.conv1 = nn.Conv2d(1, filter_num_list[0], kernel_size=4, stride
=2, padding=2, bias=False)
self.conv2 = nn.Conv2d(filter_num_l... |
MaskDiscriminator | # 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 torch.nn as nn
class MaskDiscriminator(nn.Module):
def __init__(self):
super(MaskDiscriminator, self).__init__()
filter_num_list = [64, 128, 256, 512, 2]
self.conv1 = nn.Conv2d(2, filter_num_list[0], kernel_size=4, stride
=2, padding=2, bias=False)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | JACKYLUO1991/DCBNet | MaskDiscriminator | false | 17,469 | [
"MIT"
] | 6 | b797584b66ad99fe984f58268befb12ec60ccfae | https://github.com/JACKYLUO1991/DCBNet/tree/b797584b66ad99fe984f58268befb12ec60ccfae | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
filter_num_list = [64, 128, 256, 512, 2]
self.conv1 = nn.Conv2d(2, filter_num_list[0], kernel_size=4, stride
=2, padding=2, bias=False)
self.conv2 = nn.Conv2d(filter_num_l... |
StrongMask | # 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 math
import torch
import torch.nn.functional as F
import torch.nn as nn
class SamePad2dStrong(nn.Module):
"""Mimics tensorflow's 'SAME' padding.
"""
def __init__(self, kernel_size, stride):
super(SamePad2dStrong, self).__init__()
self.kernel_size = torch.nn.modules.utils._pair(kern... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 math
import torch.nn.f... | IssamLaradji/wisenet | StrongMask | false | 17,470 | [
"Apache-2.0"
] | 7 | 881457f5168815f5e9d03f110244842d539747a0 | https://github.com/IssamLaradji/wisenet/tree/881457f5168815f5e9d03f110244842d539747a0 | import math
import torch
import torch.nn.functional as F
import torch.nn as nn
class SamePad2dStrong(nn.Module):
"""Mimics tensorflow's 'SAME' padding.
"""
def __init__(self, kernel_size, stride):
super().__init__()
self.kernel_size = torch.nn.modules.utils._pair(kernel_size)
self... |
GramMatrix | # 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 torch.nn as nn
class GramMatrix(nn.Module):
def forward(self, input):
a, b, c, d = input.size()
features = input.view(a, b, c * d)
G = torch.bmm(features, features.transpose(1, 2))
return G.div(b * c * d)
def get_inputs():
return [torch.rand([4, 4, 4, 4])... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Jay2020-01/TextureGAN--Flask | GramMatrix | false | 17,471 | [
"MIT"
] | 5 | cddea505b0d66b58d58fb24435f8bae42fd5a852 | https://github.com/Jay2020-01/TextureGAN--Flask/tree/cddea505b0d66b58d58fb24435f8bae42fd5a852 | import torch
import torch.nn as nn
class Model(nn.Module):
def forward(self, input):
a, b, c, d = input.size()
features = input.view(a, b, c * d)
G = torch.bmm(features, features.transpose(1, 2))
return G.div(b * c * d)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
d... |
GeneralizedDiceLoss | # 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 collections
import torch
import warnings
from typing import Optional
from typing import Union
from typing import Callable
from typing import Any
from typing import Tuple
import torch.nn
from torch.nn.modules.loss import _Loss
from enum import Enum
import collections.abc
def issequenceiterable(obj: 'Any') ->boo... | 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 collections
from typi... | Irme/MONAI | GeneralizedDiceLoss | false | 17,472 | [
"Apache-2.0"
] | 3 | dc4bf661831b14f4231cb325cc1b15d38c1e406c | https://github.com/Irme/MONAI/tree/dc4bf661831b14f4231cb325cc1b15d38c1e406c | import collections
import torch
import warnings
from typing import Optional
from typing import Union
from typing import Callable
from typing import Any
from typing import Tuple
import torch.nn
from torch.nn.modules.loss import _Loss
from enum import Enum
import collections.abc
def issequenceiterable(obj: 'Any') ->boo... |
MedianPool2d | # 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 torch.nn as nn
import torch.nn.functional as F
from torch.nn.modules.utils import _pair
from torch.nn.modules.utils import _quadruple
class MedianPool2d(nn.Module):
"""Median pool (usable as median filter when stride=1) module.
Args:
kernel_size: size of pooling kernel, int or 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
import torch.nn as nn
from torch.nn.modules.utils import _pair
from torch... | Jiaqi0602/adversarial-attack-from-leakage | MedianPool2d | false | 17,473 | [
"BSD-3-Clause"
] | 9 | 90db721bed10094ac7d458b232ad5b1573884338 | https://github.com/Jiaqi0602/adversarial-attack-from-leakage/tree/90db721bed10094ac7d458b232ad5b1573884338 | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.modules.utils import _pair
from torch.nn.modules.utils import _quadruple
class Model(nn.Module):
"""Median pool (usable as median filter when stride=1) module.
Args:
kernel_size: size of pooling kernel, int or 2-tuple
... |
SlideNet | # 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 torch.nn.functional as F
import torch.nn as nn
class SlideNet(nn.Module):
"""
Slided window network
"""
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(in_channels=1, out_channels=10, kernel_size=6)
self.conv2 = nn.Conv2d(in_channels=10, out_c... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | JHorcasitas/cnn_document_binarization | SlideNet | false | 17,474 | [
"MIT"
] | 9 | 075f76aed375ca14a53011f4dfeb12379debb5b3 | https://github.com/JHorcasitas/cnn_document_binarization/tree/075f76aed375ca14a53011f4dfeb12379debb5b3 | import torch
import torch.nn.functional as F
import torch.nn as nn
class Model(nn.Module):
"""
Slided window network
"""
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(in_channels=1, out_channels=10, kernel_size=6)
self.conv2 = nn.Conv2d(in_channels=10, out_chan... |
ActionScoring | # 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 torch.nn as nn
class ActionScoring(nn.Module):
""" Linearly mapping h and v to the same dimension,
and do a elementwise multiplication and a linear scoring. """
def __init__(self, action_size, hidden_size, dot_size: 'int'=256):
super(ActionScoring, 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... | IMNearth/Curriculum-Learning-For-VLN | ActionScoring | false | 17,475 | [
"MIT"
] | 8 | d2fe1286eb295dc8c63a0c886b35883f32481d85 | https://github.com/IMNearth/Curriculum-Learning-For-VLN/tree/d2fe1286eb295dc8c63a0c886b35883f32481d85 | import torch
import torch.nn as nn
class Model(nn.Module):
""" Linearly mapping h and v to the same dimension,
and do a elementwise multiplication and a linear scoring. """
def __init__(self, action_size, hidden_size, dot_size: 'int'=256):
super().__init__()
self.linear_act = nn.Line... |
GraphConv | # 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 torch.nn as nn
import torch.utils.data
from torch.nn import init
import torch.nn.functional as F
class MLP(nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim, act=nn.ReLU(),
normalize_input=True):
super(MLP, self).__init__()
self.linear_1 = nn.Linear(inpu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | JiaxuanYou/graph-pooling | GraphConv | false | 17,476 | [
"MIT"
] | 5 | e6237f03a72ac55d8a10192ca36fa596973461f5 | https://github.com/JiaxuanYou/graph-pooling/tree/e6237f03a72ac55d8a10192ca36fa596973461f5 | import torch
import torch.nn as nn
import torch.utils.data
from torch.nn import init
import torch.nn.functional as F
class MLP(nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim, act=nn.ReLU(),
normalize_input=True):
super().__init__()
self.linear_1 = nn.Linear(input_dim, hi... |
SALayer | # 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 torch.nn as nn
import torch.utils.model_zoo
class SALayer(nn.Module):
def __init__(self, kernel_size=7):
super(SALayer, self).__init__()
padding = 3 if kernel_size == 7 else 1
self.conv1 = nn.Conv2d(2, 1, kernel_size, padding=padding, bias=False)
self.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
import ... | JiahangGu/RFN | SALayer | false | 17,477 | [
"MIT"
] | 4 | 8f7b33e22bb0a9f4057476720e05cc694a46ec00 | https://github.com/JiahangGu/RFN/tree/8f7b33e22bb0a9f4057476720e05cc694a46ec00 | import torch
import torch.nn as nn
import torch.utils.model_zoo
class Model(nn.Module):
def __init__(self, kernel_size=7):
super().__init__()
padding = 3 if kernel_size == 7 else 1
self.conv1 = nn.Conv2d(2, 1, kernel_size, padding=padding, bias=False)
self.sigmoid = nn.Sigmoid()
... |
NN | # 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 import nn
import torch.nn.functional as F
class NN(nn.Module):
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(4, 16)
self.fc2 = nn.Linear(16, 3)
def forward(self, x):
x = F.relu(self.fc1(x))
x = self.fc2(x)
return x
def g... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | Jie-Yuan/Deeps | NN | false | 17,478 | [
"MIT"
] | 4 | b4acbb8e16b8ff5d181e70c3b549df0d818d0d76 | https://github.com/Jie-Yuan/Deeps/tree/b4acbb8e16b8ff5d181e70c3b549df0d818d0d76 | import torch
from torch import nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(4, 16)
self.fc2 = nn.Linear(16, 3)
def forward(self, x):
x = F.relu(self.fc1(x))
x = self.fc2(x)
return x
de... |
WeightedCrossEntropyLoss | # 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 torch.nn as nn
import torch.nn.functional as F
class WeightedCrossEntropyLoss(nn.Module):
"""
Transform input to fit the fomation of PyTorch offical cross entropy loss
with anchor-wise weighting.
"""
def __init__(self):
super(WeightedCrossEntropyLoss, 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 math as tl_math
import torch.nn as nn
... | Jiaolong/trajectory-prediction | WeightedCrossEntropyLoss | false | 17,479 | [
"Apache-2.0"
] | 6 | 3fd4e6253b44dfdc86e7c08e93c002baf66f2e46 | https://github.com/Jiaolong/trajectory-prediction/tree/3fd4e6253b44dfdc86e7c08e93c002baf66f2e46 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
"""
Transform input to fit the fomation of PyTorch offical cross entropy loss
with anchor-wise weighting.
"""
def __init__(self):
super().__init__()
def forward(self, input: 'torch.Tensor', tar... |
SRB | # 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 torch.nn as nn
import torch.utils.model_zoo
class SRB(nn.Module):
def __init__(self):
super(SRB, self).__init__()
self.conv1 = nn.Conv2d(3, 64, 9, padding=4)
self.conv2 = nn.Conv2d(64, 32, 5, padding=2)
self.conv3 = nn.Conv2d(32, 3, 5, padding=2)
self.a... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | JiahangGu/RFN | SRB | false | 17,480 | [
"MIT"
] | 4 | 8f7b33e22bb0a9f4057476720e05cc694a46ec00 | https://github.com/JiahangGu/RFN/tree/8f7b33e22bb0a9f4057476720e05cc694a46ec00 | import torch
import torch.nn as nn
import torch.utils.model_zoo
class Model(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(3, 64, 9, padding=4)
self.conv2 = nn.Conv2d(64, 32, 5, padding=2)
self.conv3 = nn.Conv2d(32, 3, 5, padding=2)
self.act = nn... |
SigmoidFocalClassificationLoss | # 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 torch.nn as nn
class SigmoidFocalClassificationLoss(nn.Module):
"""
Sigmoid focal cross entropy loss.
"""
def __init__(self, gamma: 'float'=2.0, alpha: 'float'=0.25):
"""
Args:
gamma: Weighting parameter to balance loss for hard and easy examples.
... | 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... | Jiaolong/trajectory-prediction | SigmoidFocalClassificationLoss | false | 17,481 | [
"Apache-2.0"
] | 6 | 3fd4e6253b44dfdc86e7c08e93c002baf66f2e46 | https://github.com/Jiaolong/trajectory-prediction/tree/3fd4e6253b44dfdc86e7c08e93c002baf66f2e46 | import torch
import torch.nn as nn
class Model(nn.Module):
"""
Sigmoid focal cross entropy loss.
"""
def __init__(self, gamma: 'float'=2.0, alpha: 'float'=0.25):
"""
Args:
gamma: Weighting parameter to balance loss for hard and easy examples.
alpha: Weighting p... |
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