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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
Encoder_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 Encoder_attention(nn.Module):
def __init__(self, n_h):
super(Encoder_attention, self).__init__()
self.linear = nn.Linear(n_h, 1)
self.softmax = nn.Softmax(dim=1)
def forward(self, x):
"""Output: X """
x1 = self.linear(x).squeez... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | CrowdDynamicsLab/InfoMotif | Encoder_attention | false | 17,177 | [
"BSD-3-Clause"
] | 7 | cca1ffa14cc94408a5c4c50b7b1707c608e3bc9b | https://github.com/CrowdDynamicsLab/InfoMotif/tree/cca1ffa14cc94408a5c4c50b7b1707c608e3bc9b | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, n_h):
super().__init__()
self.linear = nn.Linear(n_h, 1)
self.softmax = nn.Softmax(dim=1)
def forward(self, x):
"""Output: X """
x1 = self.linear(x).squeeze()
weights = self.softmax(... |
DQN_xy3 | # 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
import torch.utils.data
class DQN_xy3(nn.Module):
"""
A MLP for DQN learning.
Note: Uses a one hot board representation
"""
def __init__(self):
super(DQN_xy3, self).__init__()
self.fc1 = nn.Linear(4, 10)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | CoAxLab/azad | DQN_xy3 | false | 17,178 | [
"MIT"
] | 6 | d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 | https://github.com/CoAxLab/azad/tree/d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
class Model(nn.Module):
"""
A MLP for DQN learning.
Note: Uses a one hot board representation
"""
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(4, 10)
self.fc2 = ... |
BPR | # 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
class BPR(torch.nn.Module):
def __init__(self):
super(BPR, self).__init__()
self._sigmoid = torch.nn.Sigmoid()
def forward(self, pos, neg):
loss = torch.log(self._sigmoid(pos.double() - neg.double()))
return -loss.mean()
def get_inputs():
return [torch.rand... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._... | DanielMorales9/FactorizationPyTorch | BPR | false | 17,179 | [
"MIT"
] | 4 | 50f0644fdb4a903550fb3f1ba78fb9fb8649ceb1 | https://github.com/DanielMorales9/FactorizationPyTorch/tree/50f0644fdb4a903550fb3f1ba78fb9fb8649ceb1 | import torch
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
self._sigmoid = torch.nn.Sigmoid()
def forward(self, pos, neg):
loss = torch.log(self._sigmoid(pos.double() - neg.double()))
return -loss.mean()
def get_inputs():
return [torch.rand([4, 4,... |
UpsampleConvLayer | # 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.nn.parallel
class UpsampleConvLayer(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, activation='relu', norm=None):
super(UpsampleConvLayer, self).__init__()
bias = Fa... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | DA4EVENT/home | UpsampleConvLayer | false | 17,180 | [
"MIT"
] | 5 | 18cc93a795ce132e05b886aa34565a102915b1c6 | https://github.com/DA4EVENT/home/tree/18cc93a795ce132e05b886aa34565a102915b1c6 | import torch
from torch import nn
import torch.nn.functional as f
import torch.nn.parallel
class Model(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, activation='relu', norm=None):
super().__init__()
bias = False if norm == 'BN' else True
... |
DeepTable3 | # 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
import torch.utils.data
class DeepTable3(nn.Module):
"""A deep differentialable 'Table' for learning one-hot input and output.
"""
def __init__(self, in_channels, out_channels, num_hidden1=200,
num_hidden2=100):
super(Deep... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | CoAxLab/azad | DeepTable3 | false | 17,181 | [
"MIT"
] | 6 | d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 | https://github.com/CoAxLab/azad/tree/d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
class Model(nn.Module):
"""A deep differentialable 'Table' for learning one-hot input and output.
"""
def __init__(self, in_channels, out_channels, num_hidden1=200,
num_hidden2=100):
super().__init_... |
BinaryPrimitivesSomethingElse | # 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
def apply_last_dim(model, x):
size = list(x.size())
y = model(x.contiguous().view(-1, size[-1]))
size[-1] = y.size(-1)
y = y.view(torch.Size(size))
return y
def get_int_dim_index(name):
if isinstance(name, int):
return name
name_list ... | 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 math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.a... | C-SUNSHINE/TOQ-Nets-PyTorch-Release | BinaryPrimitivesSomethingElse | false | 17,182 | [
"MIT"
] | 6 | 05e06bf633fb3c6b610dda9a5126ecd7af1db02f | https://github.com/C-SUNSHINE/TOQ-Nets-PyTorch-Release/tree/05e06bf633fb3c6b610dda9a5126ecd7af1db02f | import math
import torch
from torch import nn
def apply_last_dim(model, x):
size = list(x.size())
y = model(x.contiguous().view(-1, size[-1]))
size[-1] = y.size(-1)
y = y.view(torch.Size(size))
return y
def get_int_dim_index(name):
if isinstance(name, int):
return name
name_list ... |
DQN_xy2 | # 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
import torch.utils.data
class DQN_xy2(nn.Module):
"""
A MLP for DQN learning.
Note: Uses a one hot board representation
"""
def __init__(self):
super(DQN_xy2, self).__init__()
self.fc1 = nn.Linear(4, 100)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | CoAxLab/azad | DQN_xy2 | false | 17,183 | [
"MIT"
] | 6 | d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 | https://github.com/CoAxLab/azad/tree/d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
class Model(nn.Module):
"""
A MLP for DQN learning.
Note: Uses a one hot board representation
"""
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(4, 100)
self.fc2 =... |
MMDLoss | # 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.parallel
def gaussian_kernel(source, target, kernel_mul=2.0, kernel_num=5, fix_sigma
=None):
n_samples = int(source.size()[0]) + int(target.size()[0])
total = torch.cat([source, target], dim=0)
total0 = total.unsqueeze(0).expand(int(total.size(0)), int... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
import torch.nn.parallel
assert_size_stride = torch.... | DA4EVENT/home | MMDLoss | false | 17,184 | [
"MIT"
] | 5 | 18cc93a795ce132e05b886aa34565a102915b1c6 | https://github.com/DA4EVENT/home/tree/18cc93a795ce132e05b886aa34565a102915b1c6 | import torch
from torch import nn
import torch.nn.parallel
def gaussian_kernel(source, target, kernel_mul=2.0, kernel_num=5, fix_sigma
=None):
n_samples = int(source.size()[0]) + int(target.size()[0])
total = torch.cat([source, target], dim=0)
total0 = total.unsqueeze(0).expand(int(total.size(0)), int... |
TransposedConvLayer | # 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.parallel
class TransposedConvLayer(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, activation='relu', norm=None):
super(TransposedConvLayer, self).__init__()
bias = False if norm == 'BN' else 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
from torch._inductor.runtime import triton_helpers
from torch import nn
import t... | DA4EVENT/home | TransposedConvLayer | false | 17,185 | [
"MIT"
] | 5 | 18cc93a795ce132e05b886aa34565a102915b1c6 | https://github.com/DA4EVENT/home/tree/18cc93a795ce132e05b886aa34565a102915b1c6 | import torch
from torch import nn
import torch.nn.parallel
class Model(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, activation='relu', norm=None):
super().__init__()
bias = False if norm == 'BN' else True
self.transposed_conv2d = nn.C... |
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
from torch import nn
import torch.nn.parallel
class ConvLayer(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, activation='relu', norm=None):
super(ConvLayer, self).__init__()
bias = False if norm == 'BN' else True
self.conv2... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
import t... | DA4EVENT/home | ConvLayer | false | 17,186 | [
"MIT"
] | 5 | 18cc93a795ce132e05b886aa34565a102915b1c6 | https://github.com/DA4EVENT/home/tree/18cc93a795ce132e05b886aa34565a102915b1c6 | import torch
from torch import nn
import torch.nn.parallel
class Model(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, activation='relu', norm=None):
super().__init__()
bias = False if norm == 'BN' else True
self.conv2d = nn.Conv2d(in_ch... |
DQN_xy4 | # 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
import torch.utils.data
class DQN_xy4(nn.Module):
"""
A MLP for DQN learning.
Note: Uses a one hot board representation
"""
def __init__(self):
super(DQN_xy4, self).__init__()
self.fc1 = nn.Linear(4, 100)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | CoAxLab/azad | DQN_xy4 | false | 17,187 | [
"MIT"
] | 6 | d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 | https://github.com/CoAxLab/azad/tree/d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
class Model(nn.Module):
"""
A MLP for DQN learning.
Note: Uses a one hot board representation
"""
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(4, 100)
self.fc2 =... |
DQN_hot5 | # 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
import torch.utils.data
class DQN_hot5(nn.Module):
"""
A MLP for DQN learning.
Note: Uses a one hot board representation
Params
------
m, n: int
Board size
num_actions: int
Number of action-valu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | CoAxLab/azad | DQN_hot5 | false | 17,188 | [
"MIT"
] | 6 | d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 | https://github.com/CoAxLab/azad/tree/d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
class Model(nn.Module):
"""
A MLP for DQN learning.
Note: Uses a one hot board representation
Params
------
m, n: int
Board size
num_actions: int
Number of action-value t... |
DQN_xy5 | # 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
import torch.utils.data
class DQN_xy5(nn.Module):
"""
A MLP for DQN learning.
Note: Uses a one hot board representation
Params
------
m, n: int
Board size
num_actions: int
Number of action-value... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | CoAxLab/azad | DQN_xy5 | false | 17,189 | [
"MIT"
] | 6 | d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 | https://github.com/CoAxLab/azad/tree/d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
class Model(nn.Module):
"""
A MLP for DQN learning.
Note: Uses a one hot board representation
Params
------
m, n: int
Board size
num_actions: int
Number of action-value t... |
DQN_xy1 | # 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
import torch.utils.data
class DQN_xy1(nn.Module):
"""
A MLP for DQN learning.
Note: Uses a (x,y) coordinate board/action representation.
"""
def __init__(self):
super(DQN_xy1, self).__init__()
self.fc1 = nn.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | CoAxLab/azad | DQN_xy1 | false | 17,190 | [
"MIT"
] | 6 | d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 | https://github.com/CoAxLab/azad/tree/d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
class Model(nn.Module):
"""
A MLP for DQN learning.
Note: Uses a (x,y) coordinate board/action representation.
"""
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(4, 15)
... |
down | # 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 import nn
import torch.nn.parallel
assert_size_stride = torch._C._dyn... | DA4EVENT/home | down | false | 17,191 | [
"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 Model(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 ... |
MaskUpdate | # 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.parameter import Parameter
class MaskUpdate(nn.Module):
def __init__(self, alpha):
super(MaskUpdate, self).__init__()
self.updateFunc = nn.ReLU(False)
self.alpha = Parameter(torch.tensor(alpha, dtype=torch.float32))
def forward(self, in... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch ... | DLwbm123/LBAM_inpainting | MaskUpdate | false | 17,192 | [
"MIT"
] | 7 | c809c3cedf09cda7c175e930c7834ac39d8f526f | https://github.com/DLwbm123/LBAM_inpainting/tree/c809c3cedf09cda7c175e930c7834ac39d8f526f | import torch
from torch import nn
from torch.nn.parameter import Parameter
class Model(nn.Module):
def __init__(self, alpha):
super().__init__()
self.updateFunc = nn.ReLU(False)
self.alpha = Parameter(torch.tensor(alpha, dtype=torch.float32))
def forward(self, inputMaskMap):
... |
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 import nn
import torch.nn.parallel
class ResidualBlock(nn.Module):
def __init__(self, in_channels, out_channels, stride=1, downsample=None,
norm=None):
super(ResidualBlock, self).__init__()
bias = False if norm == 'BN' else True
self.conv1 = nn.Conv2d(in_ch... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
import t... | DA4EVENT/home | ResidualBlock | false | 17,193 | [
"MIT"
] | 5 | 18cc93a795ce132e05b886aa34565a102915b1c6 | https://github.com/DA4EVENT/home/tree/18cc93a795ce132e05b886aa34565a102915b1c6 | import torch
from torch import nn
import torch.nn.parallel
class Model(nn.Module):
def __init__(self, in_channels, out_channels, stride=1, downsample=None,
norm=None):
super().__init__()
bias = False if norm == 'BN' else True
self.conv1 = nn.Conv2d(in_channels, out_channels, kerne... |
DQN_mlp | # 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
import torch.utils.data
class DQN_mlp(nn.Module):
"""Layers for a Deep Q Network, based on a simple MLP."""
def __init__(self, m, n, num_actions, num_hidden1=1000, num_hidden2=2000):
super(DQN_mlp, self).__init__()
self.m = m
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | CoAxLab/azad | DQN_mlp | false | 17,194 | [
"MIT"
] | 6 | d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 | https://github.com/CoAxLab/azad/tree/d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
class Model(nn.Module):
"""Layers for a Deep Q Network, based on a simple MLP."""
def __init__(self, m, n, num_actions, num_hidden1=1000, num_hidden2=2000):
super().__init__()
self.m = m
self.n ... |
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, n_h):
super(Attention, self).__init__()
self.linear = nn.Linear(n_h * 2, 1)
self.softmax = nn.Softmax(dim=2)
def forward(self, x):
curr_node = x[:, :, 0, :].unsqueeze(2).expand_as(x)
sta... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | CrowdDynamicsLab/InfoMotif | Attention | false | 17,195 | [
"BSD-3-Clause"
] | 7 | cca1ffa14cc94408a5c4c50b7b1707c608e3bc9b | https://github.com/CrowdDynamicsLab/InfoMotif/tree/cca1ffa14cc94408a5c4c50b7b1707c608e3bc9b | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, n_h):
super().__init__()
self.linear = nn.Linear(n_h * 2, 1)
self.softmax = nn.Softmax(dim=2)
def forward(self, x):
curr_node = x[:, :, 0, :].unsqueeze(2).expand_as(x)
stacked_x = torch.cat(... |
MCDO | # 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 MCDO(nn.Module):
def __init__(self, in_dim, out_dim, n_layers=1, hid_dim=50, p=0.05):
super().__init__()
self.n_layers = n_layers
self.linear_in = nn.Linear(in_dim, hid_dim)
nn.init.normal_(self.linear_in.weight, std=1 / (4 * hid_dim) ** 0.5... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | Daniil-Selikhanovych/bnn-vi | MCDO | false | 17,196 | [
"MIT"
] | 3 | 6788edc1438c66609abca249e33a81da7a0ff1a2 | https://github.com/Daniil-Selikhanovych/bnn-vi/tree/6788edc1438c66609abca249e33a81da7a0ff1a2 | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, in_dim, out_dim, n_layers=1, hid_dim=50, p=0.05):
super().__init__()
self.n_layers = n_layers
self.linear_in = nn.Linear(in_dim, hid_dim)
nn.init.normal_(self.linear_in.weight, std=1 / (4 * hid_dim) ** 0.... |
GaussActivation | # 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.parameter import Parameter
class GaussActivation(nn.Module):
def __init__(self, a, mu, sigma1, sigma2):
super(GaussActivation, self).__init__()
self.a = Parameter(torch.tensor(a, dtype=torch.float32))
self.mu = Parameter(torch.tensor(mu, dty... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
f... | DLwbm123/LBAM_inpainting | GaussActivation | false | 17,197 | [
"MIT"
] | 7 | c809c3cedf09cda7c175e930c7834ac39d8f526f | https://github.com/DLwbm123/LBAM_inpainting/tree/c809c3cedf09cda7c175e930c7834ac39d8f526f | import torch
from torch import nn
from torch.nn.parameter import Parameter
class Model(nn.Module):
def __init__(self, a, mu, sigma1, sigma2):
super().__init__()
self.a = Parameter(torch.tensor(a, dtype=torch.float32))
self.mu = Parameter(torch.tensor(mu, dtype=torch.float32))
self... |
FactorizationMachine | # 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... | from torch.nn import Module
import torch
from torch import FloatTensor
from torch.nn import Parameter
class SecondOrderInteraction(Module):
"""
Factorized parameters for the Second Order Interactions
Parameters
----------
n_features: int
Length of the input vector.
n_factors: int, opt... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
from torch import FloatTensor
from torch.nn import P... | DanielMorales9/FactorizationPyTorch | FactorizationMachine | false | 17,198 | [
"MIT"
] | 4 | 50f0644fdb4a903550fb3f1ba78fb9fb8649ceb1 | https://github.com/DanielMorales9/FactorizationPyTorch/tree/50f0644fdb4a903550fb3f1ba78fb9fb8649ceb1 | from torch.nn import Module
import torch
from torch import FloatTensor
from torch.nn import Parameter
class SecondOrderInteraction(Module):
"""
Factorized parameters for the Second Order Interactions
Parameters
----------
n_features: int
Length of the input vector.
n_factors: int, opt... |
GEGLU | # 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 GEGLU(nn.Module):
def forward(self, x):
x, gate = x.chunk(2, dim=-1)
return F.gelu(gate) * x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | DannielSilva/MMBERT | GEGLU | false | 17,199 | [
"MIT"
] | 4 | 2c9069b59b66b8f3fec6de2e68ec42b489a3a437 | https://github.com/DannielSilva/MMBERT/tree/2c9069b59b66b8f3fec6de2e68ec42b489a3a437 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def forward(self, x):
x, gate = x.chunk(2, dim=-1)
return F.gelu(gate) * x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return []
|
SecondOrderInteraction | # 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... | from torch.nn import Module
import torch
from torch.nn import Parameter
class SecondOrderInteraction(Module):
"""
Factorized parameters for the Second Order Interactions
Parameters
----------
n_features: int
Length of the input vector.
n_factors: int, optional
Number of factor... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
from torch.nn import Parameter
assert_size_stride = ... | DanielMorales9/FactorizationPyTorch | SecondOrderInteraction | false | 17,200 | [
"MIT"
] | 4 | 50f0644fdb4a903550fb3f1ba78fb9fb8649ceb1 | https://github.com/DanielMorales9/FactorizationPyTorch/tree/50f0644fdb4a903550fb3f1ba78fb9fb8649ceb1 | from torch.nn import Module
import torch
from torch.nn import Parameter
class Model(Module):
"""
Factorized parameters for the Second Order Interactions
Parameters
----------
n_features: int
Length of the input vector.
n_factors: int, optional
Number of factors of the factoriz... |
GatedConv2d | # 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 GatedConv2d(nn.Module):
def __init__(self, input_channels, output_channels, kernel_size, stride,
padding, dilation=1, activation=None):
super(GatedConv2d, self).__init__()
self.activation = activation
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
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | Daulbaev/IRDM | GatedConv2d | false | 17,201 | [
"MIT"
] | 10 | 4bb60191ac0072e4349ca47092675d06b39a979a | https://github.com/Daulbaev/IRDM/tree/4bb60191ac0072e4349ca47092675d06b39a979a | import torch
import torch.nn as nn
import torch.utils.data
class Model(nn.Module):
def __init__(self, input_channels, output_channels, kernel_size, stride,
padding, dilation=1, activation=None):
super().__init__()
self.activation = activation
self.sigmoid = nn.Sigmoid()
se... |
SERF | # 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 SERF(nn.Module):
def __init__(self, thresh=50):
super().__init__()
self.thresh = thresh
None
def forward(self, x):
return self.serf_log1pexp(x)
def serf(self, x):
return x * torch.erf(torch.log(1 + torch.exp(x)))
def ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | DannielSilva/MMBERT | SERF | false | 17,202 | [
"MIT"
] | 4 | 2c9069b59b66b8f3fec6de2e68ec42b489a3a437 | https://github.com/DannielSilva/MMBERT/tree/2c9069b59b66b8f3fec6de2e68ec42b489a3a437 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, thresh=50):
super().__init__()
self.thresh = thresh
None
def forward(self, x):
return self.serf_log1pexp(x)
def serf(self, x):
return x * torch.erf(torch.log(1 + torch.exp(x)))
def... |
BCEWithLogitsWithClassWeightLoss | # 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 Tensor
from typing import NoReturn
from torch import nn
class BCEWithLogitsWithClassWeightLoss(nn.BCEWithLogitsLoss):
""" finished, checked,
"""
__name__ = 'BCEWithLogitsWithClassWeightsLoss'
def __init__(self, class_weight: 'Tensor') ->NoReturn:
""" finished, c... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch ... | DeepPSP/torch_ecg | BCEWithLogitsWithClassWeightLoss | false | 17,203 | [
"MIT"
] | 9 | 6db5ffb063d0e8fb4ce97029a0d184a658f43a37 | https://github.com/DeepPSP/torch_ecg/tree/6db5ffb063d0e8fb4ce97029a0d184a658f43a37 | import torch
from torch import Tensor
from typing import NoReturn
from torch import nn
class Model(nn.BCEWithLogitsLoss):
""" finished, checked,
"""
__name__ = 'BCEWithLogitsWithClassWeightsLoss'
def __init__(self, class_weight: 'Tensor') ->NoReturn:
""" finished, checked,
Parameters... |
MLP | # 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
import torch.utils.data
def create_all_possible_moves(m, n):
"""Create all moves on a (m,n) board."""
moves = []
for i in range(m):
for j in range(n):
moves.append((i, j))
return list(set(moves))
class MLP(nn.Modu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | CoAxLab/azad | MLP | false | 17,204 | [
"MIT"
] | 6 | d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 | https://github.com/CoAxLab/azad/tree/d1498069dd8856e93ae077b34dd7c9f1c7ce80e6 | import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
def create_all_possible_moves(m, n):
"""Create all moves on a (m,n) board."""
moves = []
for i in range(m):
for j in range(n):
moves.append((i, j))
return list(set(moves))
class Model(nn.Mo... |
PatchEmbedding | # 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 PatchEmbedding(nn.Module):
def __init__(self, image_size, patch_size, embed_dim, channels):
super().__init__()
self.image_size = image_size
if image_size[0] % patch_size != 0 or image_size[1] % patch_size != 0:
raise ValueError(
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Curli-quan/fewshot-select | PatchEmbedding | false | 17,205 | [
"Apache-2.0"
] | 7 | 34f8ce5069ed1fbd01c1fa73a3ef264c98dadafe | https://github.com/Curli-quan/fewshot-select/tree/34f8ce5069ed1fbd01c1fa73a3ef264c98dadafe | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, image_size, patch_size, embed_dim, channels):
super().__init__()
self.image_size = image_size
if image_size[0] % patch_size != 0 or image_size[1] % patch_size != 0:
raise ValueError(
... |
h_swish | # 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 h_swish(nn.Module):
def __init__(self, inplace=True):
super(h_swish, self).__init__()
self.inplace = inplace
def forward(self, x):
out = F.relu6(x + 3.0, inplace=self.inplace) / 6.0
return out * x
def ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | DandelionLau/NetworkCollections | h_swish | false | 17,206 | [
"Apache-2.0"
] | 8 | 29e5cd2091f7085b3241209ed9447f2baadbce41 | https://github.com/DandelionLau/NetworkCollections/tree/29e5cd2091f7085b3241209ed9447f2baadbce41 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, inplace=True):
super().__init__()
self.inplace = inplace
def forward(self, x):
out = F.relu6(x + 3.0, inplace=self.inplace) / 6.0
return out * x
def get_inputs():
... |
Net1 | # 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
from torch import nn
class Net1(nn.Module):
def __init__(self):
super(Net1, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=0)
self.pool1 = nn.MaxPool2d(kernel_size=3, stride=2)
self.conv2 = nn.Conv2d(64,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | DPBayes/DP-cross-silo-federated-learning | Net1 | false | 17,207 | [
"Apache-2.0"
] | 8 | 6707db703de5fae48c06116ae8ceee0685c9615d | https://github.com/DPBayes/DP-cross-silo-federated-learning/tree/6707db703de5fae48c06116ae8ceee0685c9615d | import torch
import torch.nn.functional as F
from torch import nn
class Model(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=0)
self.pool1 = nn.MaxPool2d(kernel_size=3, stride=2)
self.conv2 = nn.Conv2d(64, 64, kern... |
Upsample | # 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.utils.data.distributed
import torch._utils
class Upsample(nn.Module):
def __init__(self, stride=2):
super(Upsample, self).__init__()
self.stride = stride
def forward(self, x):
stride = self.stride
assert ... | 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.utils.data.distributed
import torch._utils
assert_size_stride = torch._C._dynamo.... | DatatangAILAB/SuanFaShiXun04 | Upsample | false | 17,209 | [
"Apache-2.0"
] | 5 | f478e40dd84240ac71cbb54e6bacf9ff556fbb3e | https://github.com/DatatangAILAB/SuanFaShiXun04/tree/f478e40dd84240ac71cbb54e6bacf9ff556fbb3e | import torch
import torch.nn as nn
import torch.utils.data
import torch.utils.data.distributed
import torch._utils
class Model(nn.Module):
def __init__(self, stride=2):
super().__init__()
self.stride = stride
def forward(self, x):
stride = self.stride
assert x.data.dim() == 4... |
PatchEmbed | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class PatchEmbed(nn.Module):
""" Image to Patch Embedding
"""
def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
super().__init__()
num_patches = img_size // patch_size * (img_size // patch_size)
self.img_size = img_size
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Curli-quan/fewshot-select | PatchEmbed | false | 17,210 | [
"Apache-2.0"
] | 7 | 34f8ce5069ed1fbd01c1fa73a3ef264c98dadafe | https://github.com/Curli-quan/fewshot-select/tree/34f8ce5069ed1fbd01c1fa73a3ef264c98dadafe | import torch
import torch.nn as nn
class Model(nn.Module):
""" Image to Patch Embedding
"""
def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
super().__init__()
num_patches = img_size // patch_size * (img_size // patch_size)
self.img_size = img_size
... |
ExtResNetBlock | # 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
def padding(im, patch_size, fill_value=0):
H, W = im.size(2), im.size(3)
pad_h, pad_w = 0, 0
if H % patch_size > 0:
pad_h = patch_size - H % patch_size
if W % patch_size > 0:
pad_w = patch_size - W % patch_size
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch.nn impor... | Curli-quan/fewshot-select | ExtResNetBlock | false | 17,211 | [
"Apache-2.0"
] | 7 | 34f8ce5069ed1fbd01c1fa73a3ef264c98dadafe | https://github.com/Curli-quan/fewshot-select/tree/34f8ce5069ed1fbd01c1fa73a3ef264c98dadafe | import torch
from torch.nn import functional as F
import torch.nn as nn
def padding(im, patch_size, fill_value=0):
H, W = im.size(2), im.size(3)
pad_h, pad_w = 0, 0
if H % patch_size > 0:
pad_h = patch_size - H % patch_size
if W % patch_size > 0:
pad_w = patch_size - W % patch_size
... |
HardSwish | # 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 HardSwish(nn.Module):
def __init__(self, inplace=False):
super(HardSwish, self).__init__()
self.act = nn.ReLU6(inplace)
"""forward"""
def forward(self, x):
return x * self.act(x + 3) / 6
def get_inputs():
return [torch.rand([4, 4, 4,... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | DetectionBLWX/WSDDN.pytorch | HardSwish | false | 17,212 | [
"MIT"
] | 7 | 05020d9d0445af90ba0af3f095aa12b18e3da7d2 | https://github.com/DetectionBLWX/WSDDN.pytorch/tree/05020d9d0445af90ba0af3f095aa12b18e3da7d2 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, inplace=False):
super().__init__()
self.act = nn.ReLU6(inplace)
"""forward"""
def forward(self, x):
return x * self.act(x + 3) / 6
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_ini... |
ReverseMaskConv | # 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.parameter import Parameter
def weights_init(init_type='gaussian'):
def init_fun(m):
classname = m.__class__.__name__
if (classname.find('Conv') == 0 or classname.find('Linear') == 0
) and hasattr(m, 'weight'):
if ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | DLwbm123/LBAM_inpainting | ReverseMaskConv | false | 17,213 | [
"MIT"
] | 7 | c809c3cedf09cda7c175e930c7834ac39d8f526f | https://github.com/DLwbm123/LBAM_inpainting/tree/c809c3cedf09cda7c175e930c7834ac39d8f526f | import math
import torch
from torch import nn
from torch.nn.parameter import Parameter
def weights_init(init_type='gaussian'):
def init_fun(m):
classname = m.__class__.__name__
if (classname.find('Conv') == 0 or classname.find('Linear') == 0
) and hasattr(m, 'weight'):
if ... |
ReOrgLayer | # 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.utils.data.distributed
import torch._utils
class ReOrgLayer(nn.Module):
def __init__(self, stride=2):
super(ReOrgLayer, self).__init__()
self.stride = stride
def forward(self, x):
assert x.data.dim() == 4
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
import torch.utils.data.distributed
import torch._utils
assert_size_stride = torch._C._dynamo.... | DatatangAILAB/SuanFaShiXun04 | ReOrgLayer | false | 17,214 | [
"Apache-2.0"
] | 5 | f478e40dd84240ac71cbb54e6bacf9ff556fbb3e | https://github.com/DatatangAILAB/SuanFaShiXun04/tree/f478e40dd84240ac71cbb54e6bacf9ff556fbb3e | import torch
import torch.nn as nn
import torch.utils.data
import torch.utils.data.distributed
import torch._utils
class Model(nn.Module):
def __init__(self, stride=2):
super().__init__()
self.stride = stride
def forward(self, x):
assert x.data.dim() == 4
B, C, H, W = x.data.... |
Net | # 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 Net(nn.Module):
def __init__(self, x_d, w_d, out_d, hidden_d1=256, hidden_d2=512,
hidden_d3=256, is_discrete_input=False, is_discrete_output=False,
embedding_dim=None):
super().__init__()
self._x_d = x_d
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | DataCanvasIO/YLearn | Net | false | 17,215 | [
"Apache-2.0"
] | 3 | d65b5afb83deed154c710de9096317165d95014a | https://github.com/DataCanvasIO/YLearn/tree/d65b5afb83deed154c710de9096317165d95014a | import torch
import torch.nn.functional as F
import torch.nn as nn
class Model(nn.Module):
def __init__(self, x_d, w_d, out_d, hidden_d1=256, hidden_d2=512,
hidden_d3=256, is_discrete_input=False, is_discrete_output=False,
embedding_dim=None):
super().__init__()
self._x_d = x_d
... |
HardSigmoid | # 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 HardSigmoid(nn.Module):
def __init__(self, bias=1.0, divisor=2.0, min_value=0.0, max_value=1.0):
super(HardSigmoid, self).__init__()
assert divisor != 0, 'divisor is not allowed to be equal to zero'
self.bias = bias
self.divisor = divisor
... | 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... | DetectionBLWX/WSDDN.pytorch | HardSigmoid | false | 17,216 | [
"MIT"
] | 7 | 05020d9d0445af90ba0af3f095aa12b18e3da7d2 | https://github.com/DetectionBLWX/WSDDN.pytorch/tree/05020d9d0445af90ba0af3f095aa12b18e3da7d2 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, bias=1.0, divisor=2.0, min_value=0.0, max_value=1.0):
super().__init__()
assert divisor != 0, 'divisor is not allowed to be equal to zero'
self.bias = bias
self.divisor = divisor
self.min_value =... |
MaxPoolStride1 | # 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.utils.data.distributed
import torch.nn.functional as F
import torch._utils
class MaxPoolStride1(nn.Module):
def __init__(self, kernel_size):
super(MaxPoolStride1, self).__init__()
self.kernel_size = kernel_size
self.p... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.data
import torch.utils.data.distributed
import ... | DatatangAILAB/SuanFaShiXun04 | MaxPoolStride1 | false | 17,217 | [
"Apache-2.0"
] | 5 | f478e40dd84240ac71cbb54e6bacf9ff556fbb3e | https://github.com/DatatangAILAB/SuanFaShiXun04/tree/f478e40dd84240ac71cbb54e6bacf9ff556fbb3e | import torch
import torch.nn as nn
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.functional as F
import torch._utils
class Model(nn.Module):
def __init__(self, kernel_size):
super().__init__()
self.kernel_size = kernel_size
self.pad = kernel_size - 1
def... |
SamePadConv2d | # 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 SamePadConv2d(nn.Conv2d):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
dilation=1, groups=1, bias=True):
super(SamePadConv2d, self).__init__(in_channels, out_channels,
kernel_size, stride,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | DandelionLau/NetworkCollections | SamePadConv2d | false | 17,218 | [
"Apache-2.0"
] | 8 | 29e5cd2091f7085b3241209ed9447f2baadbce41 | https://github.com/DandelionLau/NetworkCollections/tree/29e5cd2091f7085b3241209ed9447f2baadbce41 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Conv2d):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
dilation=1, groups=1, bias=True):
super().__init__(in_channels, out_channels,
kernel_size, stride, 0, dilation, groups, bias)... |
NormedLinear | # 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.utils.data
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Parameter
class NormedLinear(nn.Module):
def __init__(self, in_features, out_features):
super(NormedLinear, self).__init__()
self.weight = Parameter(torch.Tensor(in_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._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Data-Designer/Feature-Space-Augmentation-for-Long-Tailed-Data | NormedLinear | false | 17,219 | [
"MIT"
] | 9 | ac6bced6269d6ebaa3fc0935603d905a7f11a6fa | https://github.com/Data-Designer/Feature-Space-Augmentation-for-Long-Tailed-Data/tree/ac6bced6269d6ebaa3fc0935603d905a7f11a6fa | import torch
import torch.utils.data
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Parameter
class Model(nn.Module):
def __init__(self, in_features, out_features):
super().__init__()
self.weight = Parameter(torch.Tensor(in_features, out_features))
... |
L | # 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
def drop_none(**kwargs):
r = {k: v for k, v in kwargs.items() if v is not None}
return r
class L(nn.Module):
def __init__(self, num_linear, input_features, output_features, dtype=
None, device=None):
super().__init__()
options = dro... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.a... | DataCanvasIO/YLearn | L | false | 17,220 | [
"Apache-2.0"
] | 3 | d65b5afb83deed154c710de9096317165d95014a | https://github.com/DataCanvasIO/YLearn/tree/d65b5afb83deed154c710de9096317165d95014a | import math
import torch
import torch.nn as nn
def drop_none(**kwargs):
r = {k: v for k, v in kwargs.items() if v is not None}
return r
class Model(nn.Module):
def __init__(self, num_linear, input_features, output_features, dtype=
None, device=None):
super().__init__()
options =... |
Downsample | # 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 Downsample(nn.Module):
def __init__(self, dim):
super().__init__()
self.conv = nn.Conv2d(dim, dim, 3, 2, 1)
def forward(self, x):
return self.conv(x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | DavidRuhe/simple-variational-diffusion-models | Downsample | false | 17,221 | [
"MIT"
] | 4 | a32355bf052a8f08e9c1919080588d0b22c8de4e | https://github.com/DavidRuhe/simple-variational-diffusion-models/tree/a32355bf052a8f08e9c1919080588d0b22c8de4e | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, dim):
super().__init__()
self.conv = nn.Conv2d(dim, dim, 3, 2, 1)
def forward(self, x):
return self.conv(x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [4]
|
Upsample | # 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 Upsample(nn.Module):
def __init__(self, dim):
super().__init__()
self.conv = nn.ConvTranspose2d(dim, dim, 4, 2, 1)
def forward(self, x):
return self.conv(x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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... | DavidRuhe/simple-variational-diffusion-models | Upsample | false | 17,222 | [
"MIT"
] | 4 | a32355bf052a8f08e9c1919080588d0b22c8de4e | https://github.com/DavidRuhe/simple-variational-diffusion-models/tree/a32355bf052a8f08e9c1919080588d0b22c8de4e | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, dim):
super().__init__()
self.conv = nn.ConvTranspose2d(dim, dim, 4, 2, 1)
def forward(self, x):
return self.conv(x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
ret... |
SqueezeExcitate | # 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 SqueezeExcitate(nn.Module):
def __init__(self, in_channels, se_size, activation=None):
super(SqueezeExcitate, self).__init__()
self.dim_reduce = nn.Conv2d(in_channels=in_channels, out_channels=
se_size, kernel_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 import nn
import t... | Dogy06/COVID-Efficientnet-Pytorch-1 | SqueezeExcitate | false | 17,223 | [
"MIT"
] | 4 | 3c1f7d9513abe59783152efca28dc886bf4afc2f | https://github.com/Dogy06/COVID-Efficientnet-Pytorch-1/tree/3c1f7d9513abe59783152efca28dc886bf4afc2f | import torch
from torch import nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, in_channels, se_size, activation=None):
super().__init__()
self.dim_reduce = nn.Conv2d(in_channels=in_channels, out_channels=
se_size, kernel_size=1)
self.dim_restore =... |
LabelSmoothingCrossEntropyBCE | # 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 LabelSmoothingCrossEntropyBCE(nn.Module):
def __init__(self, smoothing=0.1):
super(LabelSmoothingCrossEntropyBCE, self).__init__()
assert smoothing < 1.0
self.smoothing = smoothing
self.confidence = 1.0 - smo... | 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... | Diyago/Graph-clasification-by-computer-vision | LabelSmoothingCrossEntropyBCE | false | 17,224 | [
"Apache-2.0"
] | 9 | 703c44b98f9875d7a7b6db1c2b96372e11e256d6 | https://github.com/Diyago/Graph-clasification-by-computer-vision/tree/703c44b98f9875d7a7b6db1c2b96372e11e256d6 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, smoothing=0.1):
super().__init__()
assert smoothing < 1.0
self.smoothing = smoothing
self.confidence = 1.0 - smoothing
def forward(self, x, target):
target = ... |
PositiveLinear | # 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 PositiveLinear(nn.Module):
def __init__(self, in_features: 'int', out_features: 'int') ->None:
super().__init__()
self.weight = nn.Parameter(torch.randn(in_features, out_features))
self.bias = nn.Parameter(torch.zeros(out_features))
self.sof... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
fr... | DavidRuhe/simple-variational-diffusion-models | PositiveLinear | false | 17,225 | [
"MIT"
] | 4 | a32355bf052a8f08e9c1919080588d0b22c8de4e | https://github.com/DavidRuhe/simple-variational-diffusion-models/tree/a32355bf052a8f08e9c1919080588d0b22c8de4e | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, in_features: 'int', out_features: 'int') ->None:
super().__init__()
self.weight = nn.Parameter(torch.randn(in_features, out_features))
self.bias = nn.Parameter(torch.zeros(out_features))
self.softplus = n... |
Residual_Covolution | # 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 Residual_Covolution(nn.Module):
def __init__(self, icol, ocol, num_classes):
super(Residual_Covolution, self).__init__()
self.conv1 = nn.Conv2d(icol, ocol, kernel_size=3, stride=1, padding
=12, dilation=12, bias=True)
self.conv2 = nn.Co... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Dayan-Guan/SVMin | Residual_Covolution | false | 17,227 | [
"MIT"
] | 6 | d72b21f65958b1fda0abdbb60bd78d01e9d9cc8f | https://github.com/Dayan-Guan/SVMin/tree/d72b21f65958b1fda0abdbb60bd78d01e9d9cc8f | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, icol, ocol, num_classes):
super().__init__()
self.conv1 = nn.Conv2d(icol, ocol, kernel_size=3, stride=1, padding
=12, dilation=12, bias=True)
self.conv2 = nn.Conv2d(ocol, num_classes, kernel_size=3, ... |
GroupNorm | # 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 GroupNorm(nn.Module):
def __init__(self, num_features, num_groups=32, eps=1e-05):
super(GroupNorm, self).__init__()
self.weight = nn.Parameter(torch.ones(1, num_features, 1, 1))
self.bias = nn.Parameter(torch.zeros(1, num_features, 1, 1))
s... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | E-Dreamer-LQ/Astronomical_Target_Detection | GroupNorm | false | 17,228 | [
"MIT"
] | 6 | 0c2d6c2e516ff1efa28d44582442123c3a03f079 | https://github.com/E-Dreamer-LQ/Astronomical_Target_Detection/tree/0c2d6c2e516ff1efa28d44582442123c3a03f079 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, num_features, num_groups=32, eps=1e-05):
super().__init__()
self.weight = nn.Parameter(torch.ones(1, num_features, 1, 1))
self.bias = nn.Parameter(torch.zeros(1, num_features, 1, 1))
self.num_groups = nu... |
FCUDown | # 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
from functools import partial
class FCUDown(nn.Module):
""" CNN feature maps -> Transformer patch embeddings
"""
def __init__(self, inplanes, outplanes, dw_stride, act_layer=nn.GELU,
norm_layer=partial(nn.LayerNorm, eps=1e-06)):
super(FCUDown, self).__in... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | Curli-quan/fewshot-select | FCUDown | false | 17,229 | [
"Apache-2.0"
] | 7 | 34f8ce5069ed1fbd01c1fa73a3ef264c98dadafe | https://github.com/Curli-quan/fewshot-select/tree/34f8ce5069ed1fbd01c1fa73a3ef264c98dadafe | import torch
import torch.nn as nn
from functools import partial
class Model(nn.Module):
""" CNN feature maps -> Transformer patch embeddings
"""
def __init__(self, inplanes, outplanes, dw_stride, act_layer=nn.GELU,
norm_layer=partial(nn.LayerNorm, eps=1e-06)):
super().__init__()
... |
LINEAR_LOGSOFTMAX | # 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 LINEAR_LOGSOFTMAX(nn.Module):
def __init__(self, input_dim, nclass):
super(LINEAR_LOGSOFTMAX, self).__init__()
self.fc = nn.Linear(input_dim, nclass)
self.logic = nn.LogSoftmax(dim=1)
self.lossfunction = nn.NLLLoss()
def forward(self, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Drkun/Lifelong-Zero-Shot-Learning | LINEAR_LOGSOFTMAX | false | 17,231 | [
"Apache-2.0"
] | 9 | 5cea07c25e14aed1c544c83863f4733a8213ddb0 | https://github.com/Drkun/Lifelong-Zero-Shot-Learning/tree/5cea07c25e14aed1c544c83863f4733a8213ddb0 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, input_dim, nclass):
super().__init__()
self.fc = nn.Linear(input_dim, nclass)
self.logic = nn.LogSoftmax(dim=1)
self.lossfunction = nn.NLLLoss()
def forward(self, x):
o = self.logic(self.fc(... |
GroupBatchnorm2d | # 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 GroupBatchnorm2d(nn.Module):
def __init__(self, c_num, group_num=16, eps=1e-10):
super(GroupBatchnorm2d, self).__init__()
self.group_num = group_num
self.gamma = nn.Parameter(torch.ones(c_num, 1, 1))
self.beta = nn.Parameter(torch.zeros(c_n... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | E-Dreamer-LQ/Astronomical_Target_Detection | GroupBatchnorm2d | false | 17,232 | [
"MIT"
] | 6 | 0c2d6c2e516ff1efa28d44582442123c3a03f079 | https://github.com/E-Dreamer-LQ/Astronomical_Target_Detection/tree/0c2d6c2e516ff1efa28d44582442123c3a03f079 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, c_num, group_num=16, eps=1e-10):
super().__init__()
self.group_num = group_num
self.gamma = nn.Parameter(torch.ones(c_num, 1, 1))
self.beta = nn.Parameter(torch.zeros(c_num, 1, 1))
self.eps = eps... |
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
import torch.nn.functional as F
class Attention(nn.Module):
"""
Applies an attention mechanism on the output features from the decoder.
.. math::
\\begin{array}{ll}
x = context*output \\\\
attn = exp(x_i) / sum_j exp(x_j) \\\\
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | EVASHINJI/Seq2Seq-PyTorch | Attention | false | 17,233 | [
"Apache-2.0"
] | 4 | d53f8d7c240bd7c16ebd1475384774bd064b4b03 | https://github.com/EVASHINJI/Seq2Seq-PyTorch/tree/d53f8d7c240bd7c16ebd1475384774bd064b4b03 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
"""
Applies an attention mechanism on the output features from the decoder.
.. math::
\\begin{array}{ll}
x = context*output \\\\
attn = exp(x_i) / sum_j exp(x_j) \\\\
... |
InstanceNormalization | # 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 InstanceNormalization(torch.nn.Module):
"""InstanceNormalization
Improves convergence of neural-style.
ref: https://arxiv.org/pdf/1607.08022.pdf
"""
def __init__(self, dim, eps=1e-09):
super(InstanceNormalization, self).__init__()
self.scal... | 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_... | E-Dreamer-LQ/Astronomical_Target_Detection | InstanceNormalization | false | 17,234 | [
"MIT"
] | 6 | 0c2d6c2e516ff1efa28d44582442123c3a03f079 | https://github.com/E-Dreamer-LQ/Astronomical_Target_Detection/tree/0c2d6c2e516ff1efa28d44582442123c3a03f079 | import torch
import torch.nn as nn
class Model(torch.nn.Module):
"""InstanceNormalization
Improves convergence of neural-style.
ref: https://arxiv.org/pdf/1607.08022.pdf
"""
def __init__(self, dim, eps=1e-09):
super().__init__()
self.scale = nn.Parameter(torch.FloatTensor(dim))
... |
ConvLSTMCls | # 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 ConvLSTMCls(nn.Module):
def __init__(self, in_channels, out_channels):
"""
Convolutional LSTM block for generation network
Args:
- in_channels: Int. Number of channels of the input of Conv2D
- out_channels: Int. Number of channels ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | DveloperY0115/torch-gqn | ConvLSTMCls | false | 17,235 | [
"Apache-2.0"
] | 3 | 3d1be9d73522e3d52f15076e0e9cb485dcab638b | https://github.com/DveloperY0115/torch-gqn/tree/3d1be9d73522e3d52f15076e0e9cb485dcab638b | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, in_channels, out_channels):
"""
Convolutional LSTM block for generation network
Args:
- in_channels: Int. Number of channels of the input of Conv2D
- out_channels: Int. Number of channels of the... |
FiLMSIREN | # 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
class FiLMSIREN(nn.Module):
def __init__(self, in_features: 'int', out_features: 'int', omega_0:
'float'=30.0, is_first: 'bool'=False, bias: 'bool'=True):
super().__init__()
self.in_features = in_features
self.out_features = out_featur... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import math
f... | EasternJournalist/pi-GAN | FiLMSIREN | false | 17,236 | [
"MIT"
] | 4 | 3d57611e1c8fca2f3cd00fde1989ec1f9dd94d55 | https://github.com/EasternJournalist/pi-GAN/tree/3d57611e1c8fca2f3cd00fde1989ec1f9dd94d55 | import math
import torch
from torch import nn
class Model(nn.Module):
def __init__(self, in_features: 'int', out_features: 'int', omega_0:
'float'=30.0, is_first: 'bool'=False, bias: 'bool'=True):
super().__init__()
self.in_features = in_features
self.out_features = out_features
... |
IOUloss | # 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 IOUloss(nn.Module):
def __init__(self, reduction='none', loss_type='iou'):
super(IOUloss, self).__init__()
self.reduction = reduction
self.loss_type = loss_type
def forward(self, pred, target):
assert pred.shape[0] == target.shape[0]
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | DerryHub/ByteTrack-attack | IOUloss | false | 17,237 | [
"MIT"
] | 6 | f237894c7985863c0830401933ebd89ca92bde96 | https://github.com/DerryHub/ByteTrack-attack/tree/f237894c7985863c0830401933ebd89ca92bde96 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, reduction='none', loss_type='iou'):
super().__init__()
self.reduction = reduction
self.loss_type = loss_type
def forward(self, pred, target):
assert pred.shape[0] == target.shape[0]
pred = p... |
NetEnd | # 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 NetEnd(nn.Module):
def __init__(self, num_classes: 'int'):
super(NetEnd, self).__init__()
self.num_classes = num_classes
self.fc_net1 = nn.Conv2d(21, self.num_classes, kernel_size=1, stride=1)
assert self.num_classes > 0, 'The number of cla... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | EadCat/Road-Extraction | NetEnd | false | 17,238 | [
"MIT"
] | 4 | 9d4831b6c3a5ef07676cbe1c79b03045fda427ea | https://github.com/EadCat/Road-Extraction/tree/9d4831b6c3a5ef07676cbe1c79b03045fda427ea | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, num_classes: 'int'):
super().__init__()
self.num_classes = num_classes
self.fc_net1 = nn.Conv2d(21, self.num_classes, kernel_size=1, stride=1)
assert self.num_classes > 0, 'The number of classes must be ... |
IN_self | # 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 IN_self(nn.Module):
def __init__(self, num_features):
super(IN_self, self).__init__()
self.num_features = num_features
self.gamma = nn.Parameter(torch.Tensor(1, num_features, 1, 1),
requires_grad=True)
self.beta = nn.Parameter(t... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | EkdeepSLubana/BeyondBatchNorm | IN_self | false | 17,239 | [
"MIT"
] | 10 | 2ab1626a1ebfdfe55f0a4bc6ac24c8bbdd4e0196 | https://github.com/EkdeepSLubana/BeyondBatchNorm/tree/2ab1626a1ebfdfe55f0a4bc6ac24c8bbdd4e0196 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, num_features):
super().__init__()
self.num_features = num_features
self.gamma = nn.Parameter(torch.Tensor(1, num_features, 1, 1),
requires_grad=True)
self.beta = nn.Parameter(torch.Tensor(1, ... |
LN_self | # 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 LN_self(nn.Module):
def __init__(self, num_features):
super().__init__()
shape = 1, num_features, 1, 1
self.gamma = nn.Parameter(torch.ones(shape))
self.beta = nn.Parameter(torch.zeros(shape))
def forward(self, X, eps=1e-05):
v... | 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_... | EkdeepSLubana/BeyondBatchNorm | LN_self | false | 17,240 | [
"MIT"
] | 10 | 2ab1626a1ebfdfe55f0a4bc6ac24c8bbdd4e0196 | https://github.com/EkdeepSLubana/BeyondBatchNorm/tree/2ab1626a1ebfdfe55f0a4bc6ac24c8bbdd4e0196 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, num_features):
super().__init__()
shape = 1, num_features, 1, 1
self.gamma = nn.Parameter(torch.ones(shape))
self.beta = nn.Parameter(torch.zeros(shape))
def forward(self, X, eps=1e-05):
var... |
TLU | # 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 TLU(nn.Module):
def __init__(self, num_features):
"""max(y, tau) = max(y - tau, 0) + tau = ReLU(y - tau) + tau"""
super(TLU, self).__init__()
self.num_features = num_features
self.tau = nn.Parameter(torch.Tensor(1, num_features, 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 import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | EkdeepSLubana/BeyondBatchNorm | TLU | false | 17,241 | [
"MIT"
] | 10 | 2ab1626a1ebfdfe55f0a4bc6ac24c8bbdd4e0196 | https://github.com/EkdeepSLubana/BeyondBatchNorm/tree/2ab1626a1ebfdfe55f0a4bc6ac24c8bbdd4e0196 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, num_features):
"""max(y, tau) = max(y - tau, 0) + tau = ReLU(y - tau) + tau"""
super().__init__()
self.num_features = num_features
self.tau = nn.Parameter(torch.Tensor(1, num_features, 1, 1),
... |
Net | # 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 Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.lin1 = nn.Linear(4, 50)
self.lin2 = nn.Linear(50, 50)
self.out = nn.Linear(50, 3)
def forward(self, x):
x = F.relu(self.lin1(x... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Elli1993/custom_net_on_movidius | Net | false | 17,242 | [
"MIT"
] | 9 | cd7ed784e6d38fe696c1ae1ff0e5a31d1b52c7dc | https://github.com/Elli1993/custom_net_on_movidius/tree/cd7ed784e6d38fe696c1ae1ff0e5a31d1b52c7dc | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self):
super().__init__()
self.lin1 = nn.Linear(4, 50)
self.lin2 = nn.Linear(50, 50)
self.out = nn.Linear(50, 3)
def forward(self, x):
x = F.relu(self.lin1(x))
... |
CoughNet | # 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 CoughNet(torch.nn.Module):
def __init__(self, input_size):
super(CoughNet, self).__init__()
self.l1 = torch.nn.Linear(input_size, 512)
self.l2 = torch.nn.Linear(512, 256)
self.l3 = torch.nn.Linear(256, 128)
self.l4 = torch.nn.Linear(128, 64)
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
assert_size_stride = torch._C... | DerWaldi/COVID-19-Cough-Classification | CoughNet | false | 17,243 | [
"MIT"
] | 7 | 40f85133b0b8973c088dc2730c592af1b89b29b7 | https://github.com/DerWaldi/COVID-19-Cough-Classification/tree/40f85133b0b8973c088dc2730c592af1b89b29b7 | import torch
class Model(torch.nn.Module):
def __init__(self, input_size):
super().__init__()
self.l1 = torch.nn.Linear(input_size, 512)
self.l2 = torch.nn.Linear(512, 256)
self.l3 = torch.nn.Linear(256, 128)
self.l4 = torch.nn.Linear(128, 64)
self.l5 = torch.nn.Li... |
FRN_self | # 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 FRN_self(nn.Module):
def __init__(self, num_features, eps=1e-05, is_eps_learnable=True):
super(FRN_self, self).__init__()
self.num_features = num_features
self.init_eps = eps
self.is_eps_learnable = is_eps_learnable
self.gamma = nn.... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.gu... | EkdeepSLubana/BeyondBatchNorm | FRN_self | false | 17,244 | [
"MIT"
] | 10 | 2ab1626a1ebfdfe55f0a4bc6ac24c8bbdd4e0196 | https://github.com/EkdeepSLubana/BeyondBatchNorm/tree/2ab1626a1ebfdfe55f0a4bc6ac24c8bbdd4e0196 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, num_features, eps=1e-05, is_eps_learnable=True):
super().__init__()
self.num_features = num_features
self.init_eps = eps
self.is_eps_learnable = is_eps_learnable
self.gamma = nn.Parameter(torch.T... |
custom_embedding | # 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
def weight_init(m):
if isinstance(m, nn.Linear):
size = m.weight.size()
size[0]
size[1]
variance = 0.001
m.weight.data.normal_(0.0, variance)
try:
m.bias.data.normal_(0.0, 0.0001)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | EpiSci/SoCRATES | custom_embedding | false | 17,245 | [
"MIT"
] | 6 | 901a896c5a765e3cb56f290188cde71c8707192d | https://github.com/EpiSci/SoCRATES/tree/901a896c5a765e3cb56f290188cde71c8707192d | import torch
import torch.nn as nn
import torch.nn.functional as F
def weight_init(m):
if isinstance(m, nn.Linear):
size = m.weight.size()
size[0]
size[1]
variance = 0.001
m.weight.data.normal_(0.0, variance)
try:
m.bias.data.normal_(0.0, 0.0001)
... |
ClassifierEnd | # 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 ClassifierEnd(nn.Module):
def __init__(self, num_classes: 'int'):
super(ClassifierEnd, self).__init__()
self.num_classes = num_classes
self.fc_net1 = nn.Conv2d(21, self.num_classes, kernel_size=1, stride=1)
self.fc_net2 = nn.Conv2d(self.num... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | EadCat/Road-Extraction | ClassifierEnd | false | 17,246 | [
"MIT"
] | 4 | 9d4831b6c3a5ef07676cbe1c79b03045fda427ea | https://github.com/EadCat/Road-Extraction/tree/9d4831b6c3a5ef07676cbe1c79b03045fda427ea | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, num_classes: 'int'):
super().__init__()
self.num_classes = num_classes
self.fc_net1 = nn.Conv2d(21, self.num_classes, kernel_size=1, stride=1)
self.fc_net2 = nn.Conv2d(self.num_classes, self.num_classes,... |
CoordConv2D | # 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 CoordConv2D(nn.Module):
def __init__(self, in_channels: 'int', out_channels: 'int', kernel_size:
'int'=3, stride: 'int'=1, padding: 'int'=1, with_r: 'bool'=False):
super().__init__()
self.in_channel = in_channels
self.with_r = with_r
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | EasternJournalist/pi-GAN | CoordConv2D | false | 17,247 | [
"MIT"
] | 4 | 3d57611e1c8fca2f3cd00fde1989ec1f9dd94d55 | https://github.com/EasternJournalist/pi-GAN/tree/3d57611e1c8fca2f3cd00fde1989ec1f9dd94d55 | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, in_channels: 'int', out_channels: 'int', kernel_size:
'int'=3, stride: 'int'=1, padding: 'int'=1, with_r: 'bool'=False):
super().__init__()
self.in_channel = in_channels
self.with_r = with_r
self.... |
DoubleSymLayer | # 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 copy
import math
import torch
import torch.nn as nn
def normalInit(dims):
"""
Essentially, PyTorch's init.xavier_normal_ but clamped
:param K: tensor to be initialized/overwritten
:return: initialized tensor on the device in the nn.Parameter wrapper
"""
K = torch.zeros(dims)
fan_in... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import copy
import math
impor... | EmoryMLIP/DynamicBlocks | DoubleSymLayer | false | 17,248 | [
"MIT"
] | 9 | 52acc9fbc1a2640c6ac8922fa18105279ccaea97 | https://github.com/EmoryMLIP/DynamicBlocks/tree/52acc9fbc1a2640c6ac8922fa18105279ccaea97 | import copy
import math
import torch
import torch.nn as nn
def normalInit(dims):
"""
Essentially, PyTorch's init.xavier_normal_ but clamped
:param K: tensor to be initialized/overwritten
:return: initialized tensor on the device in the nn.Parameter wrapper
"""
K = torch.zeros(dims)
fan_in... |
MolDQN | # 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 MolDQN(nn.Module):
def __init__(self, input_length, output_length):
super(MolDQN, self).__init__()
self.linear_1 = nn.Linear(input_length, 1024)
self.linear_2 = nn.Linear(1024, 512)
self.linear_3 = nn.Linear(512, 128)
self.linear_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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | EXJUSTICE/MolDQN-pytorch | MolDQN | false | 17,249 | [
"MIT"
] | 4 | 86828f898461e9f7722ac8a1e0b9fede2c45afe0 | https://github.com/EXJUSTICE/MolDQN-pytorch/tree/86828f898461e9f7722ac8a1e0b9fede2c45afe0 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, input_length, output_length):
super().__init__()
self.linear_1 = nn.Linear(input_length, 1024)
self.linear_2 = nn.Linear(1024, 512)
self.linear_3 = nn.Linear(512, 128)
self.linear_4 = nn.Linear(1... |
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
def weight_init(m):
if isinstance(m, nn.Linear):
size = m.weight.size()
size[0]
size[1]
variance = 0.001
m.weight.data.normal_(0.0, variance)
try:
m.bias.data.normal_(0.0, 0.0001)
except:
pass
clas... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | EpiSci/SoCRATES | Attention | false | 17,250 | [
"MIT"
] | 6 | 901a896c5a765e3cb56f290188cde71c8707192d | https://github.com/EpiSci/SoCRATES/tree/901a896c5a765e3cb56f290188cde71c8707192d | import torch
import torch.nn as nn
def weight_init(m):
if isinstance(m, nn.Linear):
size = m.weight.size()
size[0]
size[1]
variance = 0.001
m.weight.data.normal_(0.0, variance)
try:
m.bias.data.normal_(0.0, 0.0001)
except:
pass
clas... |
LinearAttention | # 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 LinearAttention(nn.Module):
def __init__(self, dim, heads=4, dim_head=32):
super().__init__()
self.heads = heads
self.dim_head = dim_head
self.hidden_dim = dim_head * heads
self.to_qkv = nn.Conv2d(dim, self.hidden_dim * 3, 1, bias=Fa... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | DavidRuhe/simple-variational-diffusion-models | LinearAttention | false | 17,251 | [
"MIT"
] | 4 | a32355bf052a8f08e9c1919080588d0b22c8de4e | https://github.com/DavidRuhe/simple-variational-diffusion-models/tree/a32355bf052a8f08e9c1919080588d0b22c8de4e | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, dim, heads=4, dim_head=32):
super().__init__()
self.heads = heads
self.dim_head = dim_head
self.hidden_dim = dim_head * heads
self.to_qkv = nn.Conv2d(dim, self.hidden_dim * 3, 1, bias=False)
... |
EltwiseSubEmbed | # 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
class EltwiseSubEmbed(nn.Module):
def __init__(self, nonlinearity='square', use_batch_norm=False,
use_classifier=False, num_features=0, num_classes=0):
super(EltwiseSubEmbed, self).__init__()
self.nonlinearity = nonlinearity
if nonlinearity is not... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | Event0511/curling-reid | EltwiseSubEmbed | false | 17,252 | [
"Apache-2.0"
] | 3 | 1494d0faeed951e495573c694362f001df5bf6fd | https://github.com/Event0511/curling-reid/tree/1494d0faeed951e495573c694362f001df5bf6fd | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, nonlinearity='square', use_batch_norm=False,
use_classifier=False, num_features=0, num_classes=0):
super().__init__()
self.nonlinearity = nonlinearity
if nonlinearity is not None and nonlinearity not in [... |
SNRNetwork | # 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 PositiveLinear(nn.Module):
def __init__(self, in_features: 'int', out_features: 'int') ->None:
super().__init__()
self.weight = nn.Parameter(torch.randn(in_features, out_features))
self.bias = nn.Parameter(torch.zeros(out_features))
self.sof... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
fr... | DavidRuhe/simple-variational-diffusion-models | SNRNetwork | false | 17,253 | [
"MIT"
] | 4 | a32355bf052a8f08e9c1919080588d0b22c8de4e | https://github.com/DavidRuhe/simple-variational-diffusion-models/tree/a32355bf052a8f08e9c1919080588d0b22c8de4e | import torch
from torch import nn
class PositiveLinear(nn.Module):
def __init__(self, in_features: 'int', out_features: 'int') ->None:
super().__init__()
self.weight = nn.Parameter(torch.randn(in_features, out_features))
self.bias = nn.Parameter(torch.zeros(out_features))
self.sof... |
EmbeddingLayer | # 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
def weight_init(m):
if isinstance(m, nn.Linear):
size = m.weight.size()
size[0]
size[1]
variance = 0.001
m.weight.data.normal_(0.0, variance)
try:
m.bias.data.normal_(0.0, 0.0001)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | EpiSci/SoCRATES | EmbeddingLayer | false | 17,254 | [
"MIT"
] | 6 | 901a896c5a765e3cb56f290188cde71c8707192d | https://github.com/EpiSci/SoCRATES/tree/901a896c5a765e3cb56f290188cde71c8707192d | import torch
import torch.nn as nn
import torch.nn.functional as F
def weight_init(m):
if isinstance(m, nn.Linear):
size = m.weight.size()
size[0]
size[1]
variance = 0.001
m.weight.data.normal_(0.0, variance)
try:
m.bias.data.normal_(0.0, 0.0001)
... |
FakeReLUM | # 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
class FakeReLU(torch.autograd.Function):
@staticmethod
def forward(ctx, input):
return input.clamp(min=0)
@staticmethod
def backward(ctx, grad_output):
return grad_output
class FakeReLUM(nn.Module):
def forward(self, x... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guard... | Ethos-lab/robust-representation-matching | FakeReLUM | false | 17,255 | [
"MIT"
] | 3 | 80d98f11846468c31278146583b9ef4750190211 | https://github.com/Ethos-lab/robust-representation-matching/tree/80d98f11846468c31278146583b9ef4750190211 | import torch
import torch.nn as nn
import torch.utils.data
class FakeReLU(torch.autograd.Function):
@staticmethod
def forward(ctx, input):
return input.clamp(min=0)
@staticmethod
def backward(ctx, grad_output):
return grad_output
class Model(nn.Module):
def forward(self, x):
... |
ConditionalBatchNorm | # 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 ConditionalBatchNorm(torch.nn.Module):
def __init__(self, no, z_dim):
super().__init__()
self.no = no
self.bn = torch.nn.InstanceNorm2d(no, affine=False)
self.condition = torch.nn.Linear(z_dim, 2 * no)
def forward(self, x, z):
cond = self.condition(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | ElementAI/beyond-trivial-explanations | ConditionalBatchNorm | false | 17,256 | [
"Apache-2.0"
] | 3 | c517d7bdbab68b6a26f74cee4d15e948b3b47238 | https://github.com/ElementAI/beyond-trivial-explanations/tree/c517d7bdbab68b6a26f74cee4d15e948b3b47238 | import torch
class Model(torch.nn.Module):
def __init__(self, no, z_dim):
super().__init__()
self.no = no
self.bn = torch.nn.InstanceNorm2d(no, affine=False)
self.condition = torch.nn.Linear(z_dim, 2 * no)
def forward(self, x, z):
cond = self.condition(z).view(-1, 2 *... |
mlp_layer | # 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
def weight_init(m):
if isinstance(m, nn.Linear):
size = m.weight.size()
size[0]
size[1]
variance = 0.001
m.weight.data.normal_(0.0, variance)
try:
m.bias.data.normal_(0.0, 0.0001)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | EpiSci/SoCRATES | mlp_layer | false | 17,257 | [
"MIT"
] | 6 | 901a896c5a765e3cb56f290188cde71c8707192d | https://github.com/EpiSci/SoCRATES/tree/901a896c5a765e3cb56f290188cde71c8707192d | import torch
import torch.nn as nn
import torch.nn.functional as F
def weight_init(m):
if isinstance(m, nn.Linear):
size = m.weight.size()
size[0]
size[1]
variance = 0.001
m.weight.data.normal_(0.0, variance)
try:
m.bias.data.normal_(0.0, 0.0001)
... |
MultVAE_encoder | # 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.sparse
import torch.nn as nn
class MultVAE_encoder(nn.Module):
def __init__(self, item_dim: 'int', hidden_dim=600, latent_dim=200,
n_hidden_layers=1, dropout=0.5, nonlinearity=nn.Tanh):
super(MultVAE_encoder, self).__init__()
self.item_dim = item_dim
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.sparse... | EricHe98/sad_final_project | MultVAE_encoder | false | 17,258 | [
"MIT"
] | 3 | 4b2b57e44f939840eede6f134493c5f8d809b1a7 | https://github.com/EricHe98/sad_final_project/tree/4b2b57e44f939840eede6f134493c5f8d809b1a7 | import torch
import torch.sparse
import torch.nn as nn
class Model(nn.Module):
def __init__(self, item_dim: 'int', hidden_dim=600, latent_dim=200,
n_hidden_layers=1, dropout=0.5, nonlinearity=nn.Tanh):
super().__init__()
self.item_dim = item_dim
self.latent_dim = latent_dim
... |
VAE | # 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.utils.data
import torch.nn as nn
class VAE(nn.Module):
def __init__(self, input_size, latent_size):
super(VAE, self).__init__()
self.latent_size = latent_size
self.input_size = input_size
self.mu_layer = nn.Linear(self.input_size, self.... | import torch
from torch import device
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math... | ErikHumphrey/sustain-seq2seq | VAE | false | 17,259 | [
"Apache-2.0"
] | 4 | c4787f0ca1047d01385e4fa4ffde59c6a8ab4cc4 | https://github.com/ErikHumphrey/sustain-seq2seq/tree/c4787f0ca1047d01385e4fa4ffde59c6a8ab4cc4 | import torch
import numpy as np
import torch.utils.data
import torch.nn as nn
class Model(nn.Module):
def __init__(self, input_size, latent_size):
super().__init__()
self.latent_size = latent_size
self.input_size = input_size
self.mu_layer = nn.Linear(self.input_size, self.latent_... |
MLP | # 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 MLP(torch.nn.Module):
def __init__(self, ni, no, nhidden, nlayers):
super().__init__()
self.nlayers = nlayers
for i in range(nlayers):
if i == 0:
setattr(self, 'linear%d' % i, torch.nn.Linear(ni, nhidden,
bias=False))
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride ... | ElementAI/beyond-trivial-explanations | MLP | false | 17,260 | [
"Apache-2.0"
] | 3 | c517d7bdbab68b6a26f74cee4d15e948b3b47238 | https://github.com/ElementAI/beyond-trivial-explanations/tree/c517d7bdbab68b6a26f74cee4d15e948b3b47238 | import torch
class Model(torch.nn.Module):
def __init__(self, ni, no, nhidden, nlayers):
super().__init__()
self.nlayers = nlayers
for i in range(nlayers):
if i == 0:
setattr(self, 'linear%d' % i, torch.nn.Linear(ni, nhidden,
bias=False))
... |
merge_layer | # 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
def weight_init(m):
if isinstance(m, nn.Linear):
size = m.weight.size()
size[0]
size[1]
variance = 0.001
m.weight.data.normal_(0.0, variance)
try:
m.bias.data.normal_(0.0, 0.0001)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | EpiSci/SoCRATES | merge_layer | false | 17,261 | [
"MIT"
] | 6 | 901a896c5a765e3cb56f290188cde71c8707192d | https://github.com/EpiSci/SoCRATES/tree/901a896c5a765e3cb56f290188cde71c8707192d | import torch
import torch.nn as nn
import torch.nn.functional as F
def weight_init(m):
if isinstance(m, nn.Linear):
size = m.weight.size()
size[0]
size[1]
variance = 0.001
m.weight.data.normal_(0.0, variance)
try:
m.bias.data.normal_(0.0, 0.0001)
... |
PreactDoubleLayer | # 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 copy
import math
import torch
import torch.nn as nn
def normalInit(dims):
"""
Essentially, PyTorch's init.xavier_normal_ but clamped
:param K: tensor to be initialized/overwritten
:return: initialized tensor on the device in the nn.Parameter wrapper
"""
K = torch.zeros(dims)
fan_in... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import copy
import math
impor... | EmoryMLIP/DynamicBlocks | PreactDoubleLayer | false | 17,262 | [
"MIT"
] | 9 | 52acc9fbc1a2640c6ac8922fa18105279ccaea97 | https://github.com/EmoryMLIP/DynamicBlocks/tree/52acc9fbc1a2640c6ac8922fa18105279ccaea97 | import copy
import math
import torch
import torch.nn as nn
def normalInit(dims):
"""
Essentially, PyTorch's init.xavier_normal_ but clamped
:param K: tensor to be initialized/overwritten
:return: initialized tensor on the device in the nn.Parameter wrapper
"""
K = torch.zeros(dims)
fan_in... |
conv_head_pooling | # 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 conv_head_pooling(nn.Module):
def __init__(self, in_feature, out_feature, stride, padding_mode='zeros'):
super(conv_head_pooling, self).__init__()
self.conv = nn.Conv2d(in_feature, out_feature, kernel_size=stride +
1, padding=stride // 2, strid... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | Equationliu/GA-Attack | conv_head_pooling | false | 17,263 | [
"MIT"
] | 8 | b0280674a211f6451774ec6b1d4cee2fc19a4de6 | https://github.com/Equationliu/GA-Attack/tree/b0280674a211f6451774ec6b1d4cee2fc19a4de6 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, in_feature, out_feature, stride, padding_mode='zeros'):
super().__init__()
self.conv = nn.Conv2d(in_feature, out_feature, kernel_size=stride +
1, padding=stride // 2, stride=stride, padding_mode=
... |
Denoise_NormalizeLayer | # 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 Denoise_NormalizeLayer(nn.Module):
def __init__(self):
super(Denoise_NormalizeLayer, self).__init__()
def forward(self, inputs: 'torch.tensor'):
permute_RGBtoBGR = [2, 1, 0]
inputs = inputs[:, permute_RGBtoBGR, :, :]
out = inputs / 0.5... | 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... | Equationliu/GA-Attack | Denoise_NormalizeLayer | false | 17,264 | [
"MIT"
] | 8 | b0280674a211f6451774ec6b1d4cee2fc19a4de6 | https://github.com/Equationliu/GA-Attack/tree/b0280674a211f6451774ec6b1d4cee2fc19a4de6 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, inputs: 'torch.tensor'):
permute_RGBtoBGR = [2, 1, 0]
inputs = inputs[:, permute_RGBtoBGR, :, :]
out = inputs / 0.5 - 1
return out
def get_inputs():
... |
CEL | # 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
class CEL(nn.Module):
def __init__(self):
super(CEL, self).__init__()
None
self.eps = 1e-06
def forward(self, pred, target):
pred = pred.sigmoid()
intersection = pred * target
numerator = (pred - intersection).sum() + (target ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | Farzanehkaji/MINet | CEL | false | 17,265 | [
"MIT"
] | 9 | cc2852cb2b3b20208f5edf38ec6952363a9b04a7 | https://github.com/Farzanehkaji/MINet/tree/cc2852cb2b3b20208f5edf38ec6952363a9b04a7 | import torch
from torch import nn
class Model(nn.Module):
def __init__(self):
super().__init__()
None
self.eps = 1e-06
def forward(self, pred, target):
pred = pred.sigmoid()
intersection = pred * target
numerator = (pred - intersection).sum() + (target - inter... |
Conv2d | # 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
class Conv2d(nn.Conv2d):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=True):
super(Conv2d, self).__init__(in_channels, out_channels, kernel_size,
stride... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | Farzanehkaji/MINet | Conv2d | false | 17,266 | [
"MIT"
] | 9 | cc2852cb2b3b20208f5edf38ec6952363a9b04a7 | https://github.com/Farzanehkaji/MINet/tree/cc2852cb2b3b20208f5edf38ec6952363a9b04a7 | import torch
from torch import nn
from torch.nn import functional as F
class Model(nn.Conv2d):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=True):
super().__init__(in_channels, out_channels, kernel_size,
stride, padding, di... |
ChannelPool | # 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 ChannelPool(nn.Module):
def forward(self, x):
return torch.cat((torch.max(x, 1)[0].unsqueeze(1), torch.mean(x, 1)
.unsqueeze(1)), dim=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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | FVL2020/2DImage_BMI_estimation | ChannelPool | false | 17,267 | [
"MIT"
] | 4 | 3ae8469c3c86aac1afd09b3ba1716ecd94f5ec3f | https://github.com/FVL2020/2DImage_BMI_estimation/tree/3ae8469c3c86aac1afd09b3ba1716ecd94f5ec3f | import torch
import torch.nn as nn
class Model(nn.Module):
def forward(self, x):
return torch.cat((torch.max(x, 1)[0].unsqueeze(1), torch.mean(x, 1)
.unsqueeze(1)), dim=1)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return []
|
ScaleToModel | # 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.cuda
from torch import linalg as linalg
class ScaleToModel(nn.Module):
"""
This class acts as an adapter module that scales pixel values from the test run domain to the model domain.
"""
def __init__(self, model_value_range, test_value_range):
"... | 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.cuda
from torch import linalg as linalg
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
e... | Flunzmas/vp-suite | ScaleToModel | false | 17,268 | [
"MIT"
] | 3 | 391570121b5bd9e3fd23aca9a0945a63c4173a24 | https://github.com/Flunzmas/vp-suite/tree/391570121b5bd9e3fd23aca9a0945a63c4173a24 | import torch
import torch.nn as nn
import torch.cuda
from torch import linalg as linalg
class Model(nn.Module):
"""
This class acts as an adapter module that scales pixel values from the test run domain to the model domain.
"""
def __init__(self, model_value_range, test_value_range):
"""
... |
MultVae | # 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.sparse
import torch.nn as nn
class MultVAE_encoder(nn.Module):
def __init__(self, item_dim: 'int', hidden_dim=600, latent_dim=200,
n_hidden_layers=1, dropout=0.5, nonlinearity=nn.Tanh):
super(MultVAE_encoder, self).__init__()
self.item_dim = item_dim
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.... | EricHe98/sad_final_project | MultVae | false | 17,269 | [
"MIT"
] | 3 | 4b2b57e44f939840eede6f134493c5f8d809b1a7 | https://github.com/EricHe98/sad_final_project/tree/4b2b57e44f939840eede6f134493c5f8d809b1a7 | import torch
import torch.sparse
import torch.nn as nn
class MultVAE_encoder(nn.Module):
def __init__(self, item_dim: 'int', hidden_dim=600, latent_dim=200,
n_hidden_layers=1, dropout=0.5, nonlinearity=nn.Tanh):
super().__init__()
self.item_dim = item_dim
self.latent_dim = latent_... |
CrossEntropyLoss | # 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 CrossEntropyLoss(nn.Module):
"""
cross entropy loss
"""
def __init__(self):
super().__init__()
def forward(self, logits, labels):
return F.cross_entropy(logits, labels, reduction='none')
def get_inputs():
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | Equationliu/GA-Attack | CrossEntropyLoss | false | 17,270 | [
"MIT"
] | 8 | b0280674a211f6451774ec6b1d4cee2fc19a4de6 | https://github.com/Equationliu/GA-Attack/tree/b0280674a211f6451774ec6b1d4cee2fc19a4de6 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
"""
cross entropy loss
"""
def __init__(self):
super().__init__()
def forward(self, logits, labels):
return F.cross_entropy(logits, labels, reduction='none')
def get_inputs():
return ... |
MultiHeadAttn | # 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
import torch.nn.functional as F
class MultiHeadAttn(nn.Module):
def __init__(self, d_model, n_head, dropout=0.1, scale=False):
super().__init__()
assert d_model % n_head == 0
self.n_head = n_head
self.qkv_linear = nn.Linear(d_model, 3 ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | EmanuelaBoros/stacked-ner | MultiHeadAttn | false | 17,271 | [
"MIT"
] | 4 | b57e4fcf777a5ad2519ffa7223364e383975bf7d | https://github.com/EmanuelaBoros/stacked-ner/tree/b57e4fcf777a5ad2519ffa7223364e383975bf7d | import math
import torch
from torch import nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, d_model, n_head, dropout=0.1, scale=False):
super().__init__()
assert d_model % n_head == 0
self.n_head = n_head
self.qkv_linear = nn.Linear(d_model, 3 * d_mode... |
KLLoss | # 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
class KLLoss(nn.Module):
def __init__(self, size_average=False):
super().__init__()
self.size_average = size_average
def forward(self, mu, logvar):
loss = 0.5 * (mu.pow(2) + logvar.exp() - logvar - 1)
if self.size_ave... | 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
... | ForrestPi/VAEGAN | KLLoss | false | 17,272 | [
"MIT"
] | 8 | c2cfeedcc2dcfad6258468611536d9a8222eb8a3 | https://github.com/ForrestPi/VAEGAN/tree/c2cfeedcc2dcfad6258468611536d9a8222eb8a3 | import torch
import torch.nn as nn
import torch.utils.data
class Model(nn.Module):
def __init__(self, size_average=False):
super().__init__()
self.size_average = size_average
def forward(self, mu, logvar):
loss = 0.5 * (mu.pow(2) + logvar.exp() - logvar - 1)
if self.size_aver... |
AttentionHead | # 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.utils.data
import torch.nn as nn
class AttentionHead(nn.Module):
def __init__(self, d_model, d_k, d_v, device):
super(AttentionHead, self).__init__()
self.dk = math.sqrt(d_k)
self.query_layer = nn.Linear(d_model, d_k)
self.key_layer = 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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ErikHumphrey/sustain-seq2seq | AttentionHead | false | 17,273 | [
"Apache-2.0"
] | 4 | c4787f0ca1047d01385e4fa4ffde59c6a8ab4cc4 | https://github.com/ErikHumphrey/sustain-seq2seq/tree/c4787f0ca1047d01385e4fa4ffde59c6a8ab4cc4 | import math
import torch
import torch.utils.data
import torch.nn as nn
class Model(nn.Module):
def __init__(self, d_model, d_k, d_v, device):
super().__init__()
self.dk = math.sqrt(d_k)
self.query_layer = nn.Linear(d_model, d_k)
self.key_layer = nn.Linear(d_model, d_k)
sel... |
AdditiveAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.utils.data
import torch.nn as nn
class AdditiveAttention(nn.Module):
def __init__(self, enc_hidden_dim, dec_hidden_dim):
super(AdditiveAttention, self).__init__()
self.attention_w1 = nn.Linear(enc_hidden_dim, enc_hidden_dim)
self.attention_w2 = nn.Linear(dec_hidd... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ErikHumphrey/sustain-seq2seq | AdditiveAttention | false | 17,274 | [
"Apache-2.0"
] | 4 | c4787f0ca1047d01385e4fa4ffde59c6a8ab4cc4 | https://github.com/ErikHumphrey/sustain-seq2seq/tree/c4787f0ca1047d01385e4fa4ffde59c6a8ab4cc4 | import torch
import torch.utils.data
import torch.nn as nn
class Model(nn.Module):
def __init__(self, enc_hidden_dim, dec_hidden_dim):
super().__init__()
self.attention_w1 = nn.Linear(enc_hidden_dim, enc_hidden_dim)
self.attention_w2 = nn.Linear(dec_hidden_dim, enc_hidden_dim)
sel... |
SimpleLoss | # 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
class SimpleLoss(torch.nn.Module):
def forward(self, output, target):
return output / target
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | FranciscoShi/piepline | SimpleLoss | false | 17,276 | [
"MIT"
] | 5 | 6105788339fc18bab39ea07625b5fd26ad687254 | https://github.com/FranciscoShi/piepline/tree/6105788339fc18bab39ea07625b5fd26ad687254 | import torch
class Model(torch.nn.Module):
def forward(self, output, target):
return output / target
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return []
|
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
import torch.nn as nn
import torch.optim
import torch.autograd
import torch.nn
import torch.nn.init
class ScaledDotProductAttention(nn.Module):
def __init__(self, d_model, attention_dropout=0.1):
super(ScaledDotProductAttention, self).__init__()
self.temper = d_model ** 0.5
s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | FilippoC/-deep-syntactic-dependency-parsing-release | ScaledDotProductAttention | false | 17,277 | [
"MIT"
] | 4 | 30e2571ea930c2fd81559f5a2a971e3738cc6d39 | https://github.com/FilippoC/-deep-syntactic-dependency-parsing-release/tree/30e2571ea930c2fd81559f5a2a971e3738cc6d39 | import torch
import torch.nn as nn
import torch.optim
import torch.autograd
import torch.nn
import torch.nn.init
class Model(nn.Module):
def __init__(self, d_model, attention_dropout=0.1):
super().__init__()
self.temper = d_model ** 0.5
self.dropout = nn.Dropout(attention_dropout)
... |
ConcatELU | # 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 ConcatELU(nn.Module):
"""
Activation function that applies ELU in both direction (inverted and plain).
Allows non-linearity while providing strong gradients for any input (important for final convolution)
"""
def forward(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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | GBATZOLIS/CAFLOW | ConcatELU | false | 17,278 | [
"MIT"
] | 6 | ea33f84c424bd8e46999be59cd5d52bd8f0a3a77 | https://github.com/GBATZOLIS/CAFLOW/tree/ea33f84c424bd8e46999be59cd5d52bd8f0a3a77 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
"""
Activation function that applies ELU in both direction (inverted and plain).
Allows non-linearity while providing strong gradients for any input (important for final convolution)
"""
def forward(self, x... |
HDRLoss | # 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 HDRLoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, out_img, ref_img):
return torch.mean((out_img - ref_img) ** 2)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | Galaxies99/NHDRRNet-pytorch | HDRLoss | false | 17,279 | [
"MIT"
] | 10 | b20aae987e586a6cf9c9c52fc07b0884ce6fdf37 | https://github.com/Galaxies99/NHDRRNet-pytorch/tree/b20aae987e586a6cf9c9c52fc07b0884ce6fdf37 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, out_img, ref_img):
return torch.mean((out_img - ref_img) ** 2)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
... |
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
import torch.optim
import torch.autograd
import torch.nn
import torch.nn.init
class LayerNorm(nn.Module):
def __init__(self, dim, mean=0.0, std=1.0, fixed=False, eps=1e-06, ball
=False):
super(LayerNorm, self).__init__()
self.eps = eps
self.ball ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.optim
import torch.autograd
import torch.nn
... | FilippoC/-deep-syntactic-dependency-parsing-release | LayerNorm | false | 17,280 | [
"MIT"
] | 4 | 30e2571ea930c2fd81559f5a2a971e3738cc6d39 | https://github.com/FilippoC/-deep-syntactic-dependency-parsing-release/tree/30e2571ea930c2fd81559f5a2a971e3738cc6d39 | import torch
import torch.nn as nn
import torch.optim
import torch.autograd
import torch.nn
import torch.nn.init
class Model(nn.Module):
def __init__(self, dim, mean=0.0, std=1.0, fixed=False, eps=1e-06, ball
=False):
super().__init__()
self.eps = eps
self.ball = ball
if f... |
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