entry_point stringlengths 1 65 | original_triton_python_code stringlengths 208 619k | optimised_triton_code stringlengths 1.15k 275k | repo_name stringlengths 7 115 | module_name stringlengths 1 65 | synthetic bool 1
class | uuid int64 0 18.5k | licenses listlengths 1 6 | stars int64 0 19.8k | sha stringlengths 40 40 | repo_link stringlengths 72 180 |
|---|---|---|---|---|---|---|---|---|---|---|
SimpleACosModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleACosModule(torch.nn.Module):
def __init__(self):
super(SimpleACosModule, self).__init__()
def forward(self, a):
return torch.acos(a + a)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._... | andreas-hommel/glow | SimpleACosModule | false | 3,311 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
SimpleAbsModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleAbsModule(torch.nn.Module):
def __init__(self):
super(SimpleAbsModule, self).__init__()
def forward(self, a):
return torch.abs(a + a)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = t... | andreas-hommel/glow | SimpleAbsModule | false | 3,312 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
OneTupleModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class OneTupleModule(torch.nn.Module):
def __init__(self):
super(OneTupleModule, self).__init__()
def forward(self, x):
y = 2 * x
return y,
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs(... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | andreas-hommel/glow | OneTupleModule | false | 3,313 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
SimpleASinModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleASinModule(torch.nn.Module):
def __init__(self):
super(SimpleASinModule, self).__init__()
def forward(self, a):
return torch.asin(a + a)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._... | andreas-hommel/glow | SimpleASinModule | false | 3,314 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
RepeatModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class RepeatModule(torch.nn.Module):
def __init__(self, repeats):
super(RepeatModule, self).__init__()
self.repeats = repeats
def forward(self, tensor):
tensor = tensor + tensor
return tensor.repeat(self.repeats)... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | andreas-hommel/glow | RepeatModule | false | 3,315 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
MLP | import torch
import torch.nn as nn
from collections import OrderedDict
class MLP(nn.Module):
def __init__(self, input_dims, n_hiddens, n_class):
super(MLP, self).__init__()
assert isinstance(input_dims, int), 'Please provide int for input_dims'
self.input_dims = input_dims
current... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from co... | coreylammie/pytorch-playground | MLP | false | 3,316 | [
"MIT"
] | 0 | ff7dd3a6c40481326120895065e120b4fefa1c9e | https://github.com/coreylammie/pytorch-playground/tree/ff7dd3a6c40481326120895065e120b4fefa1c9e |
SimpleAvgPool1dModule | import torch
import torch.nn.functional as F
import torch.jit
import torch.onnx
import torch.nn
class SimpleAvgPool1dModule(torch.nn.Module):
def __init__(self, kernel_size, stride=None, padding=0):
super(SimpleAvgPool1dModule, self).__init__()
self.kernel_size = kernel_size
self.padding ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | andreas-hommel/glow | SimpleAvgPool1dModule | false | 3,317 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
SimpleATanModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleATanModule(torch.nn.Module):
def __init__(self):
super(SimpleATanModule, self).__init__()
def forward(self, a):
return torch.atan(a + a)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._... | andreas-hommel/glow | SimpleATanModule | false | 3,318 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
SimpleCumSumModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleCumSumModule(torch.nn.Module):
def __init__(self, dim):
super(SimpleCumSumModule, self).__init__()
self.dim = dim
def forward(self, tensor):
return torch.cumsum(tensor, self.dim)
def get_inputs():
retur... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | andreas-hommel/glow | SimpleCumSumModule | false | 3,319 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
SimpleAvgPool2dModule | import torch
import torch.nn.functional as F
import torch.jit
import torch.onnx
import torch.nn
class SimpleAvgPool2dModule(torch.nn.Module):
def __init__(self, kernel_size, stride=None, padding=0):
super(SimpleAvgPool2dModule, self).__init__()
self.kernel_size = kernel_size
self.padding ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | andreas-hommel/glow | SimpleAvgPool2dModule | false | 3,320 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
SimpleCeilModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleCeilModule(torch.nn.Module):
def forward(self, a, b):
c = a + b
return torch.ceil(c)
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
from torch._inductor.runtime.triton_helpers import libdevice
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._... | andreas-hommel/glow | SimpleCeilModule | false | 3,321 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
SimpleAndModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleAndModule(torch.nn.Module):
def __init__(self):
super(SimpleAndModule, self).__init__()
def forward(self, a, b):
c = torch.logical_and(a, b)
return torch.logical_and(c, c)
def get_inputs():
return [torc... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | andreas-hommel/glow | SimpleAndModule | false | 3,322 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
SimpleFmodModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleFmodModule(torch.nn.Module):
def __init__(self):
super(SimpleFmodModule, self).__init__()
def forward(self, a, b):
if b.size() == torch.Size([]):
c = a.fmod(b.item())
else:
c = a.fmod(... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._... | andreas-hommel/glow | SimpleFmodModule | false | 3,323 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
SimpleClampMinModel | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleClampMinModel(torch.nn.Module):
def __init__(self, min):
super(SimpleClampMinModel, self).__init__()
self.min = min
def forward(self, input):
return torch.clamp_min(input, self.min)
def get_inputs():
re... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.... | andreas-hommel/glow | SimpleClampMinModel | false | 3,324 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
SimpleConvTranspose2dModule | import torch
import torch.nn.functional as F
import torch.jit
import torch.onnx
import torch.nn
class SimpleConvTranspose2dModule(torch.nn.Module):
def __init__(self, stride=1, padding=0, output_padding=0, dilation=1,
groups=1):
super(SimpleConvTranspose2dModule, self).__init__()
self.str... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.jit
import torch... | andreas-hommel/glow | SimpleConvTranspose2dModule | false | 3,325 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
SubsequentSpanEncoder | import torch
from torch import Tensor
from torch.nn.modules.transformer import TransformerEncoderLayer
class SubsequentSpanEncoder(TransformerEncoderLayer):
"""
The subsequent layers for the Segmental Transformer Encoder. The encoded
representations from previous layers attend over all unmasked positions ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | cmdowney88/XLSLM | SubsequentSpanEncoder | false | 3,326 | [
"MIT"
] | 0 | 7fe266bd0f0ad8a79a30052a18104b974d1c32e8 | https://github.com/cmdowney88/XLSLM/tree/7fe266bd0f0ad8a79a30052a18104b974d1c32e8 |
SimpleBmmModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleBmmModule(torch.nn.Module):
def forward(self, a, b):
return (a + a).bmm(b)
def get_inputs():
return [torch.rand([4, 4, 4]), torch.rand([4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C... | andreas-hommel/glow | SimpleBmmModule | false | 3,327 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
SPPblock | import torch
import torch.nn as nn
import torch.nn.functional as F
class SPPblock(nn.Module):
def __init__(self, in_channels):
super(SPPblock, self).__init__()
self.pool1 = nn.MaxPool2d(kernel_size=[2, 2], stride=2)
self.pool2 = nn.MaxPool2d(kernel_size=[3, 3], stride=3)
self.pool... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | coolservices/rock-fracture-identification | SPPblock | false | 3,328 | [
"Apache-2.0"
] | 0 | 3e7349be7e76dc87800c630f53f8d1ad5673d28b | https://github.com/coolservices/rock-fracture-identification/tree/3e7349be7e76dc87800c630f53f8d1ad5673d28b |
SimpleClampModel | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleClampModel(torch.nn.Module):
def __init__(self, min, max):
super(SimpleClampModel, self).__init__()
self.min = min
self.max = max
def forward(self, input):
return torch.clamp(input, self.min, self.max... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.... | andreas-hommel/glow | SimpleClampModel | false | 3,329 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
SimpleCosModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleCosModule(torch.nn.Module):
def __init__(self):
super(SimpleCosModule, self).__init__()
def forward(self, a):
return torch.cos(a + a)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = t... | andreas-hommel/glow | SimpleCosModule | false | 3,330 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
SimpleLogModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleLogModule(torch.nn.Module):
def __init__(self, *dimensions):
super(SimpleLogModule, self).__init__()
def forward(self, a):
b = torch.log(a)
return torch.log(b)
def get_inputs():
return [torch.rand([4, 4... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = t... | andreas-hommel/glow | SimpleLogModule | false | 3,331 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
SimpleConv2dModule | import torch
import torch.nn.functional as F
import torch.jit
import torch.onnx
import torch.nn
class SimpleConv2dModule(torch.nn.Module):
def __init__(self, stride=1, padding=0, dilation=1, groups=1):
super(SimpleConv2dModule, self).__init__()
self.stride = stride
self.padding = padding
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.jit
import torch... | andreas-hommel/glow | SimpleConv2dModule | false | 3,332 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
SimpleGeluModule | import torch
import torch.nn.functional as F
import torch.jit
import torch.onnx
import torch.nn
class SimpleGeluModule(torch.nn.Module):
def forward(self, tensor):
return F.gelu(tensor + tensor)
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.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._... | andreas-hommel/glow | SimpleGeluModule | false | 3,333 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
SimpleExpModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleExpModule(torch.nn.Module):
def forward(self, input):
other = torch.exp(input)
return torch.exp(other)
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 math as tl_math
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = t... | andreas-hommel/glow | SimpleExpModule | false | 3,334 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
PositionalEncoding | import math
import torch
import torch.nn as nn
class PositionalEncoding(nn.Module):
"""Implement the PE function."""
def __init__(self, d_model, max_len=10000):
super(PositionalEncoding, self).__init__()
pe = torch.zeros(max_len, d_model)
position = torch.arange(0, max_len).unsqueeze(... | 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 math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guar... | cmiras/BSL-segmentation | PositionalEncoding | false | 3,335 | [
"MIT"
] | 0 | 35a66d6c758dcf4734adb0ebc5a40ea7238d7a1d | https://github.com/cmiras/BSL-segmentation/tree/35a66d6c758dcf4734adb0ebc5a40ea7238d7a1d |
Foo | import torch
import torch.jit
import torch.onnx
import torch.nn
class Foo(torch.nn.Module):
def __init__(self):
super(Foo, self).__init__()
self.conv1 = torch.nn.Conv2d(3, 6, 3)
self.relu = torch.nn.ReLU()
self.conv2 = torch.nn.Conv2d(6, 16, 3)
def forward(self, x):
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.jit
import torch... | andreas-hommel/glow | Foo | false | 3,336 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
SimpleAddMmModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleAddMmModule(torch.nn.Module):
def __init__(self, alpha=1, beta=1):
super(SimpleAddMmModule, self).__init__()
self.alpha = alpha
self.beta = beta
def forward(self, a, b, c):
return (a + a).addmm(b, c)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C... | andreas-hommel/glow | SimpleAddMmModule | false | 3,337 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
SimpleMaxModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleMaxModule(torch.nn.Module):
def __init__(self):
super(SimpleMaxModule, self).__init__()
def forward(self, a, b):
return torch.max(a + a, b + b)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([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.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.... | andreas-hommel/glow | SimpleMaxModule | false | 3,338 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
SimpleMatmulModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleMatmulModule(torch.nn.Module):
def __init__(self):
super(SimpleMatmulModule, self).__init__()
def forward(self, a, b):
return a.matmul(b + b)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C... | andreas-hommel/glow | SimpleMatmulModule | false | 3,339 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
SimpleLogSoftmaxModel | import torch
import torch.nn.functional as F
import torch.jit
import torch.onnx
import torch.nn
class SimpleLogSoftmaxModel(torch.nn.Module):
def __init__(self, dimension):
super(SimpleLogSoftmaxModel, self).__init__()
self.dimension = dimension
def forward(self, tensor):
return F.lo... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.jit
impor... | andreas-hommel/glow | SimpleLogSoftmaxModel | false | 3,340 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
SimpleModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleModule(torch.nn.Module):
def __init__(self):
super(SimpleModule, self).__init__()
def forward(self, x):
y = x + x
y = y + 2
return y
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | andreas-hommel/glow | SimpleModule | false | 3,341 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
SimpleMinModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleMinModule(torch.nn.Module):
def __init__(self):
super(SimpleMinModule, self).__init__()
def forward(self, a, b):
return torch.min(a + a, b + b)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([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.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.... | andreas-hommel/glow | SimpleMinModule | false | 3,342 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
SimpleMulModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleMulModule(torch.nn.Module):
def __init__(self):
super(SimpleMulModule, self).__init__()
def forward(self, left, right):
other = left.mul(right.item() if right.size() == torch.Size([]) else
right)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | andreas-hommel/glow | SimpleMulModule | false | 3,343 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
SimpleSoftmaxModel | import torch
import torch.nn.functional as F
import torch.jit
import torch.onnx
import torch.nn
class SimpleSoftmaxModel(torch.nn.Module):
def __init__(self, dimension):
super(SimpleSoftmaxModel, self).__init__()
self.dimension = dimension
def forward(self, tensor):
return F.softmax(... | 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.jit
impor... | andreas-hommel/glow | SimpleSoftmaxModel | false | 3,344 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
SimpleNormModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleNormModule(torch.nn.Module):
def __init__(self, *args, **kwargs):
super(SimpleNormModule, self).__init__()
self.args = args
self.kwargs = kwargs
def forward(self, tensor):
return torch.norm(tensor, *s... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.jit
import torc... | andreas-hommel/glow | SimpleNormModule | false | 3,345 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
SimpleOrModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleOrModule(torch.nn.Module):
def __init__(self):
super(SimpleOrModule, self).__init__()
def forward(self, a, b):
c = torch.logical_or(a, b)
return torch.logical_or(c, c)
def get_inputs():
return [torch.ra... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | andreas-hommel/glow | SimpleOrModule | false | 3,346 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
BboxHead | import torch
import torch.nn as nn
from itertools import product as product
class BboxHead(nn.Module):
def __init__(self, inchannels=512, num_anchors=2):
super(BboxHead, self).__init__()
self.conv1x1 = nn.Conv2d(inchannels, num_anchors * 4, kernel_size=(
1, 1), stride=1, padding=0)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from itertools import product as product
assert_size_strid... | chennnnnnnnn/face_detection | BboxHead | false | 3,347 | [
"MIT"
] | 0 | 77d5a9098d9e1a65ac5093a23620ed5d99dc0723 | https://github.com/chennnnnnnnn/face_detection/tree/77d5a9098d9e1a65ac5093a23620ed5d99dc0723 |
ClassHead | import torch
import torch.nn as nn
from itertools import product as product
class ClassHead(nn.Module):
def __init__(self, inchannels=512, num_anchors=2):
super(ClassHead, self).__init__()
self.num_anchors = num_anchors
self.conv1x1 = nn.Conv2d(inchannels, self.num_anchors * 2,
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from itertools import product as product
assert_size_strid... | chennnnnnnnn/face_detection | ClassHead | false | 3,348 | [
"MIT"
] | 0 | 77d5a9098d9e1a65ac5093a23620ed5d99dc0723 | https://github.com/chennnnnnnnn/face_detection/tree/77d5a9098d9e1a65ac5093a23620ed5d99dc0723 |
SimpleNotModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleNotModule(torch.nn.Module):
def __init__(self):
super(SimpleNotModule, self).__init__()
def forward(self, a):
b = torch.logical_not(a)
return torch.logical_not(b)
def get_inputs():
return [torch.rand([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.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | andreas-hommel/glow | SimpleNotModule | false | 3,349 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
SimpleReshapeModel | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleReshapeModel(torch.nn.Module):
def __init__(self, shape):
super(SimpleReshapeModel, self).__init__()
self.shape = shape
def forward(self, tensor):
combined = tensor + tensor
return combined.reshape(se... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | andreas-hommel/glow | SimpleReshapeModel | false | 3,350 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
SimpleReluModel | import torch
import torch.nn.functional as F
import torch.jit
import torch.onnx
import torch.nn
class SimpleReluModel(torch.nn.Module):
def __init__(self, inplace=False):
super(SimpleReluModel, self).__init__()
self.inplace = inplace
def forward(self, tensor):
other = F.relu(tensor, ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.... | andreas-hommel/glow | SimpleReluModel | false | 3,351 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
SimplePowModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimplePowModule(torch.nn.Module):
def __init__(self, power):
super(SimplePowModule, self).__init__()
self.power = power
def forward(self, tensor):
return torch.pow(tensor, self.power)
def get_inputs():
return... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | andreas-hommel/glow | SimplePowModule | false | 3,352 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
OutputBlock | import torch
class OutputBlock(torch.nn.Module):
"""Flatten output channels using 1x1x1 convolutions"""
def __init__(self, ks, channels_in, channels_out):
super(OutputBlock, self).__init__()
self.convflat = torch.nn.Conv3d(in_channels=channels_in,
out_channels=channels_out, kernel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
reinterpret_tens... | conlain-k/RLN_elasticity | OutputBlock | false | 3,353 | [
"MIT"
] | 0 | d8574c83d62f675960a7f8b86ddb553e9a7b1ca7 | https://github.com/conlain-k/RLN_elasticity/tree/d8574c83d62f675960a7f8b86ddb553e9a7b1ca7 |
SimpleSumModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleSumModule(torch.nn.Module):
def __init__(self, dtype=None):
super(SimpleSumModule, self).__init__()
self.dtype = dtype
def forward(self, a):
b = a + a
return torch.sum(b, dtype=self.dtype)
def get_i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.... | andreas-hommel/glow | SimpleSumModule | false | 3,354 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
LandmarkHead | import torch
import torch.nn as nn
from itertools import product as product
class LandmarkHead(nn.Module):
def __init__(self, inchannels=512, num_anchors=2):
super(LandmarkHead, self).__init__()
self.conv1x1 = nn.Conv2d(inchannels, num_anchors * 10, kernel_size=
(1, 1), stride=1, padd... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from itertools import product as product
assert_size_strid... | chennnnnnnnn/face_detection | LandmarkHead | false | 3,355 | [
"MIT"
] | 0 | 77d5a9098d9e1a65ac5093a23620ed5d99dc0723 | https://github.com/chennnnnnnnn/face_detection/tree/77d5a9098d9e1a65ac5093a23620ed5d99dc0723 |
Qux | import torch
import torch.jit
import torch.onnx
import torch.nn
class Qux(torch.nn.Module):
def __init__(self, x):
super(Qux, self).__init__()
self.x = x
def forward(self, a, b):
return a - b - self.x
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 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
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | andreas-hommel/glow | Qux | false | 3,356 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
SimpleTanhModel | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleTanhModel(torch.nn.Module):
def __init__(self, inplace=False):
super(SimpleTanhModel, self).__init__()
self.inplace = inplace
def forward(self, tensor):
tensor = tensor + tensor
return tensor.tanh_() ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._... | andreas-hommel/glow | SimpleTanhModel | false | 3,357 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
SimpleSinModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleSinModule(torch.nn.Module):
def __init__(self):
super(SimpleSinModule, self).__init__()
def forward(self, a):
return torch.sin(a + a)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = t... | andreas-hommel/glow | SimpleSinModule | false | 3,358 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
CircPad | import torch
import torch.nn.functional as F
class CircPad(torch.nn.Module):
def __init__(self, pad_size):
super(CircPad, self).__init__()
if type(pad_size) == tuple:
self.padding = pad_size
else:
self.padding = tuple(pad_size for i in range(6))
def forward(se... | 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... | conlain-k/RLN_elasticity | CircPad | false | 3,359 | [
"MIT"
] | 0 | d8574c83d62f675960a7f8b86ddb553e9a7b1ca7 | https://github.com/conlain-k/RLN_elasticity/tree/d8574c83d62f675960a7f8b86ddb553e9a7b1ca7 |
SimpleReciprocalModel | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleReciprocalModel(torch.nn.Module):
def __init__(self, inplace=False):
super(SimpleReciprocalModel, self).__init__()
self.inplace = inplace
def forward(self, tensor):
other = tensor + tensor
return othe... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | andreas-hommel/glow | SimpleReciprocalModel | false | 3,360 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
SqueezeExcitation | import torch
from torch import Tensor
from torch import nn
from torch.nn import functional as F
from torchvision.models.mobilenetv2 import _make_divisible
class SqueezeExcitation(nn.Module):
def __init__(self, input_channels: 'int', squeeze_factor: 'int'=4):
super().__init__()
squeeze_channels = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import Tensor
from... | connernam/lightweight-human-pose-estimation.pytorch | SqueezeExcitation | false | 3,361 | [
"Apache-2.0"
] | 0 | ea30c43dce0d9439345e014e00a5cf7ef34db9e1 | https://github.com/connernam/lightweight-human-pose-estimation.pytorch/tree/ea30c43dce0d9439345e014e00a5cf7ef34db9e1 |
Conv | import torch
import torch.nn as nn
class Conv(nn.Module):
def __init__(self, input_size, output_size, kernel_size, pad_type):
super(Conv, self).__init__()
padding = (kernel_size - 1, 0) if pad_type == 'left' else (
kernel_size // 2, (kernel_size - 1) // 2)
self.pad = nn.Consta... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | crazyofapple/bert | Conv | false | 3,362 | [
"Apache-2.0"
] | 0 | 09f6afffc064687f7ac85d847f082e1c8d1f3ffa | https://github.com/crazyofapple/bert/tree/09f6afffc064687f7ac85d847f082e1c8d1f3ffa |
ResidualBlock | import torch
import torch.nn.functional as F
import torch.nn as nn
class ResidualBlock(nn.Module):
def __init__(self, channels):
super(ResidualBlock, self).__init__()
self.channels = channels
self.conv1 = nn.Conv2d(channels, channels, kernel_size=3, padding=1)
self.conv2 = nn.Conv... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | crisnyoung/awesome-DeepLearning | ResidualBlock | false | 3,363 | [
"Apache-2.0"
] | 0 | 0f4d0e8cc6f6c662c9a058d4af7610bf1d2a947d | https://github.com/crisnyoung/awesome-DeepLearning/tree/0f4d0e8cc6f6c662c9a058d4af7610bf1d2a947d |
L1Loss | import torch
class L1Loss(torch.nn.Module):
def __init__(self):
super(L1Loss, self).__init__()
self.loss = torch.nn.L1Loss(reduction='mean')
def forward(self, cleaned_images, images):
return self.loss(cleaned_images, images)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), t... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
assert_size_stride = t... | cviaai/MARL-NIR | L1Loss | false | 3,364 | [
"MIT"
] | 0 | f90f2353b03023546110c08ab1a24cf8edafb5fb | https://github.com/cviaai/MARL-NIR/tree/f90f2353b03023546110c08ab1a24cf8edafb5fb |
SimpleXorModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleXorModule(torch.nn.Module):
def __init__(self):
super(SimpleXorModule, self).__init__()
def forward(self, a, b):
c = torch.logical_xor(a, b)
return torch.logical_xor(c, c)
def get_inputs():
return [torc... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | andreas-hommel/glow | SimpleXorModule | false | 3,365 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
SimpleTypeasModel | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleTypeasModel(torch.nn.Module):
def __init__(self):
super(SimpleTypeasModel, self).__init__()
def forward(self, tensor, other=None):
other = tensor if other is None else other
if tensor.dtype != torch.bool:
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | andreas-hommel/glow | SimpleTypeasModel | false | 3,366 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
VGG16 | import torch
import numpy as np
import torchvision.transforms.functional as F
import torch.nn as nn
import torch.nn.functional as F
class Normalize:
def __init__(self, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)):
self.mean = mean
self.std = std
def undo(self, imgarr):
proc... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
import tor... | candacelax/1-stage-wseg | VGG16 | false | 3,367 | [
"Apache-2.0"
] | 0 | 7a24791a3a78454e6611399ba55a808491551543 | https://github.com/candacelax/1-stage-wseg/tree/7a24791a3a78454e6611399ba55a808491551543 |
SimpleStackModel | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleStackModel(torch.nn.Module):
def __init__(self, dim):
super(SimpleStackModel, self).__init__()
self.dim = dim
def forward(self, a, b):
c = b + b
return torch.stack((a, c), dim=self.dim)
def get_inpu... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | andreas-hommel/glow | SimpleStackModel | false | 3,368 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
ResidualBlock | import torch
import torch.nn as nn
class ResidualBlock(nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
self.in_channels, self.out_channels = in_channels, out_channels
self.blocks = nn.Identity()
self.shortcut = nn.Identity()
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
@triton.jit
def triton_poi_fused_add_0(in_ptr0, out_... | d222nguy/gcn_research | ResidualBlock | false | 3,369 | [
"MIT"
] | 0 | 83ced4f7d9f7840e48900e62c1eabec0444c5fa2 | https://github.com/d222nguy/gcn_research/tree/83ced4f7d9f7840e48900e62c1eabec0444c5fa2 |
TaylorNet | import math
import torch
import torch.nn as nn
class TaylorNet(nn.Module):
"""Applies a non-linear multiplicative transformation to the incoming data,
in order to generate output features that can be quadratic and linear in the
input features:
:math:`y = (x W_2^T) * (x W_1^T) + x W_1^T + b`
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | dalessioluca/TaylorNet | TaylorNet | false | 3,370 | [
"MIT"
] | 0 | 342bc0d9ee5dd81b7fe3baf9e457b56ef1df5879 | https://github.com/dalessioluca/TaylorNet/tree/342bc0d9ee5dd81b7fe3baf9e457b56ef1df5879 |
Res | import torch
from torch import nn
import torch.distributions
class Res(nn.Module):
def __init__(self, H):
super().__init__()
self.u1 = nn.Linear(H, H)
self.u2 = nn.Linear(H, H)
self.v1 = nn.Linear(H, H)
self.v2 = nn.Linear(H, H)
self.w = nn.Linear(H, H)
def fo... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
import t... | da03/torch_struct | Res | false | 3,371 | [
"MIT"
] | 0 | 08713b61b0cfe8438e52e82e07c88cf094feb73a | https://github.com/da03/torch_struct/tree/08713b61b0cfe8438e52e82e07c88cf094feb73a |
GCN | from torch.nn import Module
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
class GraphConvolution(Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
"""
def __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
from torch._inductor.runtime.... | d222nguy/gcn_research | GCN | false | 3,372 | [
"MIT"
] | 0 | 83ced4f7d9f7840e48900e62c1eabec0444c5fa2 | https://github.com/d222nguy/gcn_research/tree/83ced4f7d9f7840e48900e62c1eabec0444c5fa2 |
Biaffine | import torch
import torch.nn as nn
import torch.utils.data.dataloader
import torch.nn
class Biaffine(nn.Module):
def __init__(self, n_in, n_out=1, bias_x=True, bias_y=True, diagonal=False
):
super(Biaffine, self).__init__()
self.n_in = n_in
self.n_out = n_out
self.bias_x =... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data.dataloader
import torch.nn
assert_... | ciaochiaociao/CLNER | Biaffine | false | 3,373 | [
"MIT"
] | 0 | a31fb1c3bfdaa5d62147dc892489d29a85e6b385 | https://github.com/ciaochiaociao/CLNER/tree/a31fb1c3bfdaa5d62147dc892489d29a85e6b385 |
cnn_layer | import torch
import torch.nn as nn
import torch.utils.data.dataloader
import torch.nn
class cnn_layer(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, bias=True):
super(cnn_layer, self).__init__()
self.conv = torch.nn.Conv1d(in_channels=in_channe... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | ciaochiaociao/CLNER | cnn_layer | false | 3,374 | [
"MIT"
] | 0 | a31fb1c3bfdaa5d62147dc892489d29a85e6b385 | https://github.com/ciaochiaociao/CLNER/tree/a31fb1c3bfdaa5d62147dc892489d29a85e6b385 |
ffnn | import torch
import torch.nn as nn
import torch.utils.data.dataloader
import torch.nn
def get_shape(t):
return list(t.shape)
class ffnn(nn.Module):
def __init__(self, emb_size, num_layers, hidden_size, output_size,
dropout, output_weights_initializer=None):
super(ffnn, self).__init__()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data.dataloader
import torch.nn
assert_... | ciaochiaociao/CLNER | ffnn | false | 3,375 | [
"MIT"
] | 0 | a31fb1c3bfdaa5d62147dc892489d29a85e6b385 | https://github.com/ciaochiaociao/CLNER/tree/a31fb1c3bfdaa5d62147dc892489d29a85e6b385 |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 32, 3, 1)
self.conv2 = nn.Conv2d(32, 64, 3, 1)
self.dropout1 = nn.Dropout2d(0.25)
self.dropout2 = nn.Dropout2d... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | cyliu0204/petastorm | Net | false | 3,376 | [
"Apache-2.0"
] | 0 | 589d40ce372c311382f37e4271a5169dafba5db1 | https://github.com/cyliu0204/petastorm/tree/589d40ce372c311382f37e4271a5169dafba5db1 |
BertSelfOutput | from _paritybench_helpers import _mock_config
import torch
from torch import nn
import torch.onnx
class BertLayerNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-12):
"""Construct a layernorm module in the TF style (epsilon inside the square root).
"""
super(BertLayerNorm, 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
from torch import n... | Splendon/examples | BertSelfOutput | false | 3,377 | [
"MIT"
] | 0 | ed4a8a01857b6ddca49559141acf5d0986eb01e1 | https://github.com/Splendon/examples/tree/ed4a8a01857b6ddca49559141acf5d0986eb01e1 |
HexaLinearScore | import math
import torch
import torch.nn as nn
import torch.utils.data.dataloader
import torch.nn
class HexaLinearScore(nn.Module):
"""
Outer product version of hexalinear function for sequence labeling.
"""
def __init__(self, wemb_size, tagset_size, temb_size=20, rank=396, std=
0.1545, norma... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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
import torch.utils.data.dataloader
import torc... | ciaochiaociao/CLNER | HexaLinearScore | false | 3,378 | [
"MIT"
] | 0 | a31fb1c3bfdaa5d62147dc892489d29a85e6b385 | https://github.com/ciaochiaociao/CLNER/tree/a31fb1c3bfdaa5d62147dc892489d29a85e6b385 |
QuadriLinearScore | import math
import torch
import torch.nn as nn
import torch.utils.data.dataloader
import torch.nn
class QuadriLinearScore(nn.Module):
"""
Outer product version of quadrilinear function for sequence labeling.
"""
def __init__(self, wemb_size, tagset_size, temb_size=20, rank=396, std=
0.1545, w... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.nn as nn
import torch.utils.data.dataloader
import torc... | ciaochiaociao/CLNER | QuadriLinearScore | false | 3,379 | [
"MIT"
] | 0 | a31fb1c3bfdaa5d62147dc892489d29a85e6b385 | https://github.com/ciaochiaociao/CLNER/tree/a31fb1c3bfdaa5d62147dc892489d29a85e6b385 |
MaxMinGroup | import torch
import torch.nn as nn
def process_maxmin_groupsize(x, group_size, axis=-1):
size = list(x.size())
num_channels = size[axis]
if num_channels % group_size:
raise ValueError(
'number of features({}) is not a multiple of group_size({})'.
format(num_channels, num_ch... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | david-klindt/invertible-resnet | MaxMinGroup | false | 3,380 | [
"MIT"
] | 0 | ac6756a7ba5d0dbcb6b4cec43f8b86079318fd89 | https://github.com/david-klindt/invertible-resnet/tree/ac6756a7ba5d0dbcb6b4cec43f8b86079318fd89 |
ResidualDenseBlock | import torch
from torch import Tensor
import torch.nn as nn
class ResidualDenseBlock(nn.Module):
"""Achieves densely connected convolutional layers.
`Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993v5.pdf>` paper.
Args:
channels (int): The number of channels in the ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | cyun-404/PieESRGAN | ResidualDenseBlock | false | 3,381 | [
"Apache-2.0"
] | 0 | 22ffe683bf2389b646429494d1bc88e61a9d72c5 | https://github.com/cyun-404/PieESRGAN/tree/22ffe683bf2389b646429494d1bc88e61a9d72c5 |
Split | import torch
import torch.nn as nn
class Split(nn.Module):
def __init__(self):
super(Split, self).__init__()
def forward(self, x):
n = int(x.size(1) / 2)
x1 = x[:, :n, :, :].contiguous()
x2 = x[:, n:, :, :].contiguous()
return x1, x2
def inverse(self, x1, x2):
... | 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... | david-klindt/invertible-resnet | Split | false | 3,382 | [
"MIT"
] | 0 | ac6756a7ba5d0dbcb6b4cec43f8b86079318fd89 | https://github.com/david-klindt/invertible-resnet/tree/ac6756a7ba5d0dbcb6b4cec43f8b86079318fd89 |
ActNorm | import torch
import torch.nn as nn
from torch.nn import Parameter
from torch.nn.parameter import Parameter
class ActNorm(nn.Module):
def __init__(self, num_channels, eps=1e-05):
super(ActNorm, self).__init__()
self.eps = eps
self.num_channels = num_channels
self._log_scale = Param... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
from torch.nn import Parameter
from torch.nn.parame... | david-klindt/invertible-resnet | ActNorm | false | 3,383 | [
"MIT"
] | 0 | ac6756a7ba5d0dbcb6b4cec43f8b86079318fd89 | https://github.com/david-klindt/invertible-resnet/tree/ac6756a7ba5d0dbcb6b4cec43f8b86079318fd89 |
BiaffineAttention | import torch
import torch.nn as nn
import torch.utils.data.dataloader
from torch.nn import Parameter
from torch.nn.parameter import Parameter
import torch.nn
class BiaffineAttention(nn.Module):
"""
Adopted from NeuroNLP2:
https://github.com/XuezheMax/NeuroNLP2/blob/master/neuronlp2/nn/modules/attentio... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | ciaochiaociao/CLNER | BiaffineAttention | false | 3,384 | [
"MIT"
] | 0 | a31fb1c3bfdaa5d62147dc892489d29a85e6b385 | https://github.com/ciaochiaociao/CLNER/tree/a31fb1c3bfdaa5d62147dc892489d29a85e6b385 |
VNMaxPool | import torch
import torch.nn as nn
from itertools import product as product
class VNMaxPool(nn.Module):
def __init__(self, in_channels):
super(VNMaxPool, self).__init__()
self.map_to_dir = nn.Linear(in_channels, in_channels, bias=False)
def forward(self, x):
"""
x: point feat... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from itertools import product as product
assert_size_strid... | david1309/SynergyNet_bonseyes | VNMaxPool | false | 3,385 | [
"MIT"
] | 0 | 9d675f6e0c78222e1fa55e6598c3d11aa5dc799b | https://github.com/david1309/SynergyNet_bonseyes/tree/9d675f6e0c78222e1fa55e6598c3d11aa5dc799b |
Conv2dZeroInit | import torch
import torch.nn as nn
class Conv2dZeroInit(nn.Conv2d):
def __init__(self, channels_in, channels_out, filter_size, stride=1,
padding=0, logscale=3.0):
super().__init__(channels_in, channels_out, filter_size, stride=
stride, padding=padding)
self.register_parameter(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.... | david-klindt/invertible-resnet | Conv2dZeroInit | false | 3,386 | [
"MIT"
] | 0 | ac6756a7ba5d0dbcb6b4cec43f8b86079318fd89 | https://github.com/david-klindt/invertible-resnet/tree/ac6756a7ba5d0dbcb6b4cec43f8b86079318fd89 |
SqueezeExcite | import torch
import torch.nn as nn
import torch.nn.functional as F
from itertools import product as product
def _make_divisible(v, divisor, min_value=None):
"""
This function is taken from the original tf repo.
It ensures that all layers have a channel number that is divisible by 8
It can be seen here... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | david1309/SynergyNet_bonseyes | SqueezeExcite | false | 3,387 | [
"MIT"
] | 0 | 9d675f6e0c78222e1fa55e6598c3d11aa5dc799b | https://github.com/david1309/SynergyNet_bonseyes/tree/9d675f6e0c78222e1fa55e6598c3d11aa5dc799b |
MeanVarFC | import torch
import torch.nn as nn
class MeanVarFC(nn.Module):
def __init__(self, input_shape):
super(MeanVarFC, self).__init__()
shape = list(input_shape)
shape[0] = 1
shape[1] *= 2
self.param = nn.Parameter(0.01 * torch.randn(shape))
def forward(self, x):
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | david-klindt/invertible-resnet | MeanVarFC | false | 3,388 | [
"MIT"
] | 0 | ac6756a7ba5d0dbcb6b4cec43f8b86079318fd89 | https://github.com/david-klindt/invertible-resnet/tree/ac6756a7ba5d0dbcb6b4cec43f8b86079318fd89 |
injective_pad | import torch
import torch.nn as nn
class injective_pad(nn.Module):
def __init__(self, pad_size):
super(injective_pad, self).__init__()
self.pad_size = pad_size
self.pad = nn.ZeroPad2d((0, 0, 0, pad_size))
def forward(self, x):
x = x.permute(0, 2, 1, 3)
x = self.pad(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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | david-klindt/invertible-resnet | injective_pad | false | 3,389 | [
"MIT"
] | 0 | ac6756a7ba5d0dbcb6b4cec43f8b86079318fd89 | https://github.com/david-klindt/invertible-resnet/tree/ac6756a7ba5d0dbcb6b4cec43f8b86079318fd89 |
Bias | import torch
import torch.nn as nn
class Bias(nn.Module):
def __init__(self, size):
super().__init__()
self.bias = nn.Parameter(torch.Tensor(size))
self.reset_parameters()
def reset_parameters(self):
nn.init.zeros_(self.bias)
def forward(self, x):
return x + self... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | daviddavini/cs-260-project | Bias | false | 3,390 | [
"MIT"
] | 0 | 9e1067f8ff85c8c573262589bbe52740ef11275d | https://github.com/daviddavini/cs-260-project/tree/9e1067f8ff85c8c573262589bbe52740ef11275d |
SimpleLinearModule | import torch
import torch.nn.functional as F
import torch.jit
import torch.onnx
import torch.nn
class SimpleLinearModule(torch.nn.Module):
def __init__(self):
super(SimpleLinearModule, self).__init__()
def forward(self, input, weight, bias=None):
return F.linear(input + input, weight, bias)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C... | andreas-hommel/glow | SimpleLinearModule | false | 3,391 | [
"Apache-2.0"
] | 0 | 2bbbf8188a2a941e85677c83f2146bbd076a262e | https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e |
Dense | import torch
import torch.nn as nn
import torch.nn.functional as F
class Dense(nn.Module):
def __init__(self, in_features):
super(Dense, self).__init__()
self.fc1 = nn.Linear(in_features, 152)
self.fc2 = nn.Linear(152, 48)
self.fc3 = nn.Linear(48, 1)
nn.init.kaiming_unifor... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | cdslabamotong/GCNPP | Dense | false | 3,392 | [
"MIT"
] | 0 | 8445ed3f960e986e12e5a4d65e99e4125e6153c1 | https://github.com/cdslabamotong/GCNPP/tree/8445ed3f960e986e12e5a4d65e99e4125e6153c1 |
ResidualResidualDenseBlock | import torch
from torch import Tensor
import torch.nn as nn
class ResidualDenseBlock(nn.Module):
"""Achieves densely connected convolutional layers.
`Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993v5.pdf>` paper.
Args:
channels (int): The number of channels in the ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import 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 Tensor
import torch.nn as nn
assert_size_stride = torch._C._dy... | cyun-404/PieESRGAN | ResidualResidualDenseBlock | false | 3,393 | [
"Apache-2.0"
] | 0 | 22ffe683bf2389b646429494d1bc88e61a9d72c5 | https://github.com/cyun-404/PieESRGAN/tree/22ffe683bf2389b646429494d1bc88e61a9d72c5 |
my_Hingeloss | import torch
import torch.nn as nn
class my_Hingeloss(nn.Module):
def __init__(self):
super(my_Hingeloss, self).__init__()
def forward(self, output, target):
pos = torch.sum(output * target, 2)
neg = torch.max((1 - target) * output, 2)
loss = neg[0] - pos + 1
loss[los... | 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... | carsault/chord_extraction_prediction_lib | my_Hingeloss | false | 3,394 | [
"MIT"
] | 0 | 6de09eef9f2852b56b04874d2e42eb504c96d33f | https://github.com/carsault/chord_extraction_prediction_lib/tree/6de09eef9f2852b56b04874d2e42eb504c96d33f |
MatchingNetwork | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.optim
import torch.nn.functional as F
import torch.nn.parallel
class MatchingNetwork(nn.Module):
def __init__(self, opt):
super(MatchingNetwork, self).__init__()
scale_cls = opt['scale_cls'] if 'scale_cl... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Basasuya/FewShotWithoutForgetting | MatchingNetwork | false | 3,395 | [
"MIT"
] | 0 | eecc70e416ed82999124ddfca1b145f6dbcd74a6 | https://github.com/Basasuya/FewShotWithoutForgetting/tree/eecc70e416ed82999124ddfca1b145f6dbcd74a6 |
GroupNorm32 | import torch
import torch.nn as nn
import torch.nn.functional as F
class GroupNorm32(nn.GroupNorm):
def __init__(self, num_groups, num_channels, swish, eps=1e-05):
super().__init__(num_groups=num_groups, num_channels=num_channels,
eps=eps)
self.swish = swish
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.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | dbanys/glide-text2im | GroupNorm32 | false | 3,396 | [
"MIT"
] | 0 | 5177545ec62f1fddc3075a8a69b63df3eb2256a5 | https://github.com/dbanys/glide-text2im/tree/5177545ec62f1fddc3075a8a69b63df3eb2256a5 |
Actor | import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class Actor(nn.Module):
"""Actor (Policy) Model."""
def __init__(self, state_size, action_size, seed, f... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | david-varela/continuous_control | Actor | false | 3,397 | [
"MIT"
] | 0 | 2bce9ea958fb21e88ac2f129ba8911e95dd7b1d2 | https://github.com/david-varela/continuous_control/tree/2bce9ea958fb21e88ac2f129ba8911e95dd7b1d2 |
Discriminator | import torch
import torch.nn.functional as F
import torch.nn as nn
class Discriminator(nn.Module):
def __init__(self, n_layersDecod, hidden_size, output_size=2):
super(Discriminator, self).__init__()
self.map1 = nn.Linear(n_layersDecod * hidden_size, hidden_size)
self.map2 = nn.Linear(hid... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | carsault/chord_extraction_prediction_lib | Discriminator | false | 3,398 | [
"MIT"
] | 0 | 6de09eef9f2852b56b04874d2e42eb504c96d33f | https://github.com/carsault/chord_extraction_prediction_lib/tree/6de09eef9f2852b56b04874d2e42eb504c96d33f |
my_BinaryCross | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class my_BinaryCross(nn.Module):
def __init__(self, args):
super(my_BinaryCross, self).__init__()
self.args = args
def forward(self, output, target, beat):
modif_beat = 1.0 / torch.exp(beat) * 10
... | 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
... | carsault/chord_extraction_prediction_lib | my_BinaryCross | false | 3,399 | [
"MIT"
] | 0 | 6de09eef9f2852b56b04874d2e42eb504c96d33f | https://github.com/carsault/chord_extraction_prediction_lib/tree/6de09eef9f2852b56b04874d2e42eb504c96d33f |
Perceptron | import torch
import torch.nn as nn
class Perceptron(nn.Module):
def __init__(self, input_dim):
super().__init__()
self.fc1 = nn.Linear(input_dim, 1)
def forward(self, x_in):
return torch.sigmoid(self.fc1(x_in)).squeeze()
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | dbradf/nlp-pytorch | Perceptron | false | 3,400 | [
"Apache-2.0"
] | 0 | 957e3c5a1edf1f2ae9a8e281729395bed886bc87 | https://github.com/dbradf/nlp-pytorch/tree/957e3c5a1edf1f2ae9a8e281729395bed886bc87 |
BasicNN | import torch
import numpy as np
from torch import nn
from torch.autograd import Variable
import torch.nn.functional as F
class BasicNN(nn.Module):
def __init__(self):
super(BasicNN, self).__init__()
self.net = nn.Linear(28 * 28, 2)
def forward(self, x):
if isinstance(x, np.ndarray):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | dbczumar/clipper | BasicNN | false | 3,401 | [
"Apache-2.0"
] | 0 | 80c97d27a38d60caaebb2a1ae6a995dd7ff1c82d | https://github.com/dbczumar/clipper/tree/80c97d27a38d60caaebb2a1ae6a995dd7ff1c82d |
MultiheadAttention | import math
import torch
import torch.nn as nn
import torch as th
class QKVMultiheadAttention(nn.Module):
def __init__(self, n_heads: 'int', n_ctx: 'int'):
super().__init__()
self.n_heads = n_heads
self.n_ctx = n_ctx
def forward(self, qkv):
bs, n_ctx, width = qkv.shape
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | dbanys/glide-text2im | MultiheadAttention | false | 3,402 | [
"MIT"
] | 0 | 5177545ec62f1fddc3075a8a69b63df3eb2256a5 | https://github.com/dbanys/glide-text2im/tree/5177545ec62f1fddc3075a8a69b63df3eb2256a5 |
QPCnet | import torch
import torch.nn as nn
class QPCnet(nn.Module):
def __init__(self, num_classes=2):
super(QPCnet, self).__init__()
self.conv1 = nn.Conv2d(2, 8, 3, [1, 2], 1)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(8, 16, 3, 1, 1)
self.conv3 = nn.Conv2d(16, 32, 2, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | davidwangtgs/CNN_PAC | QPCnet | false | 3,403 | [
"MIT"
] | 0 | d3824fc269ad5c86a962336e140b222856f26a2c | https://github.com/davidwangtgs/CNN_PAC/tree/d3824fc269ad5c86a962336e140b222856f26a2c |
ConvBlock | import torch
import torch.nn as nn
class Block(nn.Module):
def __init__(self):
"""Initialisation for a lower-level DeepLPF conv block
:returns: N/A
:rtype: N/A
"""
super(Block, self).__init__()
def conv3x3(self, in_channels, out_channels, stride=1):
"""Repre... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | deshwalmahesh/CURL---cpu-gpu | ConvBlock | false | 3,404 | [
"BSD-3-Clause"
] | 0 | f4e87275b6cce556b9e04a188cf7ae13d810d82a | https://github.com/deshwalmahesh/CURL---cpu-gpu/tree/f4e87275b6cce556b9e04a188cf7ae13d810d82a |
Joiner | import torch
from torch import nn
import torch.nn.functional as F
class Joiner(nn.Module):
def __init__(self, input_dim: 'int', output_dim: 'int'):
super().__init__()
self.output_linear = nn.Linear(input_dim, output_dim)
def forward(self, encoder_out: 'torch.Tensor', decoder_out: 'torch.Tens... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | desh2608/icefall | Joiner | false | 3,405 | [
"Apache-2.0"
] | 0 | 1603744469d167d848e074f2ea98c587153205fa | https://github.com/desh2608/icefall/tree/1603744469d167d848e074f2ea98c587153205fa |
WeldonPooling | import torch
import torch.nn as nn
class WeldonPooling(nn.Module):
def __init__(self, nMax=1, nMin=None):
super(WeldonPooling, self).__init__()
self.nMax = nMax
if nMin is None:
self.nMin = nMax
else:
self.nMin = nMin
self.input = torch.Tensor()
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | debayan/dsve-loc | WeldonPooling | false | 3,406 | [
"BSD-3-Clause-Clear"
] | 0 | 21b1e1837668b6daa0881514d0756e9bec039fcb | https://github.com/debayan/dsve-loc/tree/21b1e1837668b6daa0881514d0756e9bec039fcb |
HardNegativeContrastiveLoss | import torch
import torch.nn as nn
class HardNegativeContrastiveLoss(nn.Module):
def __init__(self, nmax=1, margin=0.2):
super(HardNegativeContrastiveLoss, self).__init__()
self.margin = margin
self.nmax = nmax
def forward(self, imgs, caps):
scores = torch.mm(imgs, caps.t())
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | debayan/dsve-loc | HardNegativeContrastiveLoss | false | 3,407 | [
"BSD-3-Clause-Clear"
] | 0 | 21b1e1837668b6daa0881514d0756e9bec039fcb | https://github.com/debayan/dsve-loc/tree/21b1e1837668b6daa0881514d0756e9bec039fcb |
ResidualAttentionBlock | import math
import torch
import torch.nn as nn
import torch as th
class LayerNorm(nn.LayerNorm):
"""
Implementation that supports fp16 inputs but fp32 gains/biases.
"""
def forward(self, x: 'th.Tensor'):
return super().forward(x.float())
class QKVMultiheadAttention(nn.Module):
def __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
from torch._inductor.runtime.... | dbanys/glide-text2im | ResidualAttentionBlock | false | 3,408 | [
"MIT"
] | 0 | 5177545ec62f1fddc3075a8a69b63df3eb2256a5 | https://github.com/dbanys/glide-text2im/tree/5177545ec62f1fddc3075a8a69b63df3eb2256a5 |
MidNet2 | import torch
import torch.nn as nn
class MidNet2(nn.Module):
def forward(self, x_in):
"""Network with dilation rate 2
:param x_in: input convolutional features
:returns: processed convolutional features
:rtype: Tensor
"""
x = self.lrelu(self.conv1... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | deshwalmahesh/CURL---cpu-gpu | MidNet2 | false | 3,409 | [
"BSD-3-Clause"
] | 0 | f4e87275b6cce556b9e04a188cf7ae13d810d82a | https://github.com/deshwalmahesh/CURL---cpu-gpu/tree/f4e87275b6cce556b9e04a188cf7ae13d810d82a |
PredictionConvolutions | import torch
import torch.utils.data
from torch import nn
from itertools import product as product
import torch.optim
class PredictionConvolutions(nn.Module):
"""
Convolutions to predict class scores and bounding boxes using lower and higher-level feature maps.
The bounding boxes (locations) are predicte... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
from torch import nn
from itertools import product as pr... | adityag6994/pytorch_ssd_training | PredictionConvolutions | false | 3,410 | [
"MIT"
] | 0 | 404f3cbef815e314337ec2c1b4f06a2403a7ce03 | https://github.com/adityag6994/pytorch_ssd_training/tree/404f3cbef815e314337ec2c1b4f06a2403a7ce03 |
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