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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
DiceLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.optim.lr_scheduler
import torch.utils.data
from torchvision.transforms import *
class DiceLoss(torch.nn.Module):
def init(self):
super(DiceLoss, self).init()
def forward(self, pred, target):
smooth = 1.0
iflat = pred.contiguous().view(-1)
tflat = tar... | 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.optim.lr_scheduler
import torch.utils.data
from torchvision.transforms impor... | csharpshooter/DeepLearning | DiceLoss | false | 1,750 | [
"MIT"
] | 0 | c1d20660c32076468970f7376931e1fcd0d2644e | https://github.com/csharpshooter/DeepLearning/tree/c1d20660c32076468970f7376931e1fcd0d2644e | import torch
import torch.optim.lr_scheduler
import torch.utils.data
from torchvision.transforms import *
class Model(torch.nn.Module):
def init(self):
super(DiceLoss, self).init()
def forward(self, pred, target):
smooth = 1.0
iflat = pred.contiguous().view(-1)
tflat = target... |
WeightedFeatureFusion | # 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.lr_scheduler
import torch.utils.data
from torchvision.transforms import *
class WeightedFeatureFusion(nn.Module):
def __init__(self, layers, weight=False):
super(WeightedFeatureFusion, self).__init__()
self.layers = layers
self.weight ... | 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.optim.lr_scheduler
import torch.utils.data
from torchvision.transforms import *
assert_size_stride = torc... | csharpshooter/DeepLearning | WeightedFeatureFusion | false | 1,751 | [
"MIT"
] | 0 | c1d20660c32076468970f7376931e1fcd0d2644e | https://github.com/csharpshooter/DeepLearning/tree/c1d20660c32076468970f7376931e1fcd0d2644e | import torch
import torch.nn as nn
import torch.optim.lr_scheduler
import torch.utils.data
from torchvision.transforms import *
class Model(nn.Module):
def __init__(self, layers, weight=False):
super().__init__()
self.layers = layers
self.weight = weight
self.n = len(layers) + 1
... |
CondInjection | # 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.cpp_extension
class CondInjection(nn.Module):
def __init__(self):
super().__init__()
self.weight = nn.Parameter(torch.zeros(1))
def forward(self, image, labels, noise=None):
if noise is None:
batch, _, height, width = ... | 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.cpp_extension
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = ... | crobbins327/semanticGAN_WSI | CondInjection | false | 1,752 | [
"BSD-2-Clause",
"MIT"
] | 0 | 4046ddc822f463e03952402247f79d540bf7be95 | https://github.com/crobbins327/semanticGAN_WSI/tree/4046ddc822f463e03952402247f79d540bf7be95 | import torch
import torch.nn as nn
import torch.utils.cpp_extension
class Model(nn.Module):
def __init__(self):
super().__init__()
self.weight = nn.Parameter(torch.zeros(1))
def forward(self, image, labels, noise=None):
if noise is None:
batch, _, height, width = image.sh... |
Attention | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import math
import torch
import torch.nn.functional as F
import torch.utils.data
def restricted_softmax(src, dim=-1, margin=0):
src_max = torch.clamp(src.max(dim=dim, keepdim=True)[0], min=0)
out = (src - src_max).exp()
out = out / (out.sum(dim=dim, keepdim=True) + (margin - src_max).exp())
return out... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | cshjin/pytorch_geometric | Attention | false | 1,753 | [
"MIT"
] | 0 | 8dd0e76beb72135949a275edd851f80f7b97648f | https://github.com/cshjin/pytorch_geometric/tree/8dd0e76beb72135949a275edd851f80f7b97648f | import math
import torch
import torch.nn.functional as F
import torch.utils.data
def restricted_softmax(src, dim=-1, margin=0):
src_max = torch.clamp(src.max(dim=dim, keepdim=True)[0], min=0)
out = (src - src_max).exp()
out = out / (out.sum(dim=dim, keepdim=True) + (margin - src_max).exp())
return out... |
ActFirstResBlock | # 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 AdaptiveInstanceNorm2d(nn.Module):
def __init__(self, num_features, eps=1e-05, momentum=0.1):
super(AdaptiveInstanceNorm2d, self).__init__()
self.num_features = num_features
self.eps = eps
self.momentum = mome... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | cplusx/SIGN | ActFirstResBlock | false | 1,754 | [
"Apache-2.0"
] | 0 | 9777fc3ddd4c6f799caeefe1e72482d5b1ecd8ae | https://github.com/cplusx/SIGN/tree/9777fc3ddd4c6f799caeefe1e72482d5b1ecd8ae | import torch
import torch.nn.functional as F
from torch import nn
class AdaptiveInstanceNorm2d(nn.Module):
def __init__(self, num_features, eps=1e-05, momentum=0.1):
super().__init__()
self.num_features = num_features
self.eps = eps
self.momentum = momentum
self.weight = N... |
SoftmaxLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.utils.cpp_extension
class SoftmaxLoss(torch.nn.Module):
def __init__(self, tau=1.0):
super().__init__()
self.tau = tau
self.ce_loss = torch.nn.CrossEntropyLoss()
def forward(self, pred, true):
logits = pred / self.tau
l = self.ce_loss(logits,... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.utils.cpp... | crobbins327/semanticGAN_WSI | SoftmaxLoss | false | 1,755 | [
"BSD-2-Clause",
"MIT"
] | 0 | 4046ddc822f463e03952402247f79d540bf7be95 | https://github.com/crobbins327/semanticGAN_WSI/tree/4046ddc822f463e03952402247f79d540bf7be95 | import torch
import torch.utils.cpp_extension
class Model(torch.nn.Module):
def __init__(self, tau=1.0):
super().__init__()
self.tau = tau
self.ce_loss = torch.nn.CrossEntropyLoss()
def forward(self, pred, true):
logits = pred / self.tau
l = self.ce_loss(logits, true)... |
Gate | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
class Gate(nn.Module):
"""Gate Unit
g = sigmoid(Wx)
x = g * x
"""
def __init__(self, input_size):
super(Gate, self).__init__()
self.linear = nn.Linear(input_size, input_size, bias=False)
def forward(self, x):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | csarron/QAModels | Gate | false | 1,756 | [
"BSD-3-Clause"
] | 0 | 2db2d7b0f546b88211e111b42744408bbf9b6f35 | https://github.com/csarron/QAModels/tree/2db2d7b0f546b88211e111b42744408bbf9b6f35 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
"""Gate Unit
g = sigmoid(Wx)
x = g * x
"""
def __init__(self, input_size):
super().__init__()
self.linear = nn.Linear(input_size, input_size, bias=False)
def forward(self, x):
"... |
Linear | # 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 Tensor
from torch.nn import Linear
from torch.nn import Parameter
import torch.utils.data
def uniform(size, tensor):
bound = 1.0 / math.sqrt(size)
if tensor is not None:
tensor.data.uniform_(-bound, bound)
def kaiming_uniform(tensor, fan, a):
if tensor ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
from torch import Tensor
from torch.nn import Parameter
import torch... | cshjin/pytorch_geometric | Linear | false | 1,757 | [
"MIT"
] | 0 | 8dd0e76beb72135949a275edd851f80f7b97648f | https://github.com/cshjin/pytorch_geometric/tree/8dd0e76beb72135949a275edd851f80f7b97648f | import math
import torch
from torch import Tensor
from torch.nn import Linear
from torch.nn import Parameter
import torch.utils.data
def uniform(size, tensor):
bound = 1.0 / math.sqrt(size)
if tensor is not None:
tensor.data.uniform_(-bound, bound)
def kaiming_uniform(tensor, fan, a):
if tensor ... |
SFU | # 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 SFU(nn.Module):
"""Semantic Fusion Unit
The ouput vector is expected to not only retrieve correlative information from fusion vectors,
but also retain partly unchange as the input vector
"""
def __init__(self, input_size, fu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | csarron/QAModels | SFU | false | 1,758 | [
"BSD-3-Clause"
] | 0 | 2db2d7b0f546b88211e111b42744408bbf9b6f35 | https://github.com/csarron/QAModels/tree/2db2d7b0f546b88211e111b42744408bbf9b6f35 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
"""Semantic Fusion Unit
The ouput vector is expected to not only retrieve correlative information from fusion vectors,
but also retain partly unchange as the input vector
"""
def __init__(self, input_size, ... |
DecoderLayer | # 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
import torch.nn.functional as F
def attention(q, k, v, d_k, mask=None, dropout=None):
scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(d_k)
if mask is not None:
mask = mask.unsqueeze(1)
scores = scores.masked_fill(mask == 0, -1000000000.0... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | congson1293/Transformer | DecoderLayer | false | 1,759 | [
"Apache-2.0"
] | 0 | 249638f3287e0ed11c71496178fe2ceac2d758df | https://github.com/congson1293/Transformer/tree/249638f3287e0ed11c71496178fe2ceac2d758df | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
def attention(q, k, v, d_k, mask=None, dropout=None):
scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(d_k)
if mask is not None:
mask = mask.unsqueeze(1)
scores = scores.masked_fill(mask == 0, -1000000000.0... |
DenseSAGEConv | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
import torch.nn.functional as F
from torch.nn import Parameter
import torch.utils.data
def uniform(size, tensor):
bound = 1.0 / math.sqrt(size)
if tensor is not None:
tensor.data.uniform_(-bound, bound)
class DenseSAGEConv(torch.nn.Module):
"""See :class:`torch_geometric... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import math
from torch.nn imp... | cshjin/pytorch_geometric | DenseSAGEConv | false | 1,760 | [
"MIT"
] | 0 | 8dd0e76beb72135949a275edd851f80f7b97648f | https://github.com/cshjin/pytorch_geometric/tree/8dd0e76beb72135949a275edd851f80f7b97648f | import math
import torch
import torch.nn.functional as F
from torch.nn import Parameter
import torch.utils.data
def uniform(size, tensor):
bound = 1.0 / math.sqrt(size)
if tensor is not None:
tensor.data.uniform_(-bound, bound)
class Model(torch.nn.Module):
"""See :class:`torch_geometric.nn.conv... |
LinearExcitability | # 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 linearExcitability(input, weight, excitability=None, bias=None):
"""Applies a linear transformation to the incoming data: :math:`y = c(xA^T) + b`.
Shape:
- input: :math:`(N, *, in_features)`
- we... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
from torch import nn
from torch.nn.parameter import Parameter
assert... | cuongpxu/continual-learning | LinearExcitability | false | 1,761 | [
"MIT"
] | 0 | 0f799ddc0efe7e6df7038d2e97303add8d5e01fd | https://github.com/cuongpxu/continual-learning/tree/0f799ddc0efe7e6df7038d2e97303add8d5e01fd | import math
import torch
from torch import nn
from torch.nn.parameter import Parameter
def linearExcitability(input, weight, excitability=None, bias=None):
"""Applies a linear transformation to the incoming data: :math:`y = c(xA^T) + b`.
Shape:
- input: :math:`(N, *, in_features)`
- we... |
EncoderLayer | # 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
import torch.nn.functional as F
def attention(q, k, v, d_k, mask=None, dropout=None):
scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(d_k)
if mask is not None:
mask = mask.unsqueeze(1)
scores = scores.masked_fill(mask == 0, -1000000000.0... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | congson1293/Transformer | EncoderLayer | false | 1,762 | [
"Apache-2.0"
] | 0 | 249638f3287e0ed11c71496178fe2ceac2d758df | https://github.com/congson1293/Transformer/tree/249638f3287e0ed11c71496178fe2ceac2d758df | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
def attention(q, k, v, d_k, mask=None, dropout=None):
scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(d_k)
if mask is not None:
mask = mask.unsqueeze(1)
scores = scores.masked_fill(mask == 0, -1000000000.0... |
DenseGraphConv | # 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.nn import Parameter
import torch.utils.data
def uniform(size, tensor):
bound = 1.0 / math.sqrt(size)
if tensor is not None:
tensor.data.uniform_(-bound, bound)
class DenseGraphConv(torch.nn.Module):
"""See :class:`torch_geometric.nn.conv.GraphConv`.
"""
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
from torch.nn import Parameter
import torch.utils.data
assert_size_s... | cshjin/pytorch_geometric | DenseGraphConv | false | 1,763 | [
"MIT"
] | 0 | 8dd0e76beb72135949a275edd851f80f7b97648f | https://github.com/cshjin/pytorch_geometric/tree/8dd0e76beb72135949a275edd851f80f7b97648f | import math
import torch
from torch.nn import Parameter
import torch.utils.data
def uniform(size, tensor):
bound = 1.0 / math.sqrt(size)
if tensor is not None:
tensor.data.uniform_(-bound, bound)
class Model(torch.nn.Module):
"""See :class:`torch_geometric.nn.conv.GraphConv`.
"""
def __... |
BasicConv | # 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 time
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.tensorboard import SummaryWriter
def singleton(cls):
obj = cls()
cls.__new__ = staticmethod(lambda cls: obj)
try:
del cls.__init__
except AttributeError:
pass
return cls
@singleton
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
import time
import torch.nn a... | Zikoat/musweeper | BasicConv | false | 1,764 | [
"MIT"
] | 0 | 07e3e5e5e5e4edad4d8b1b6bb05aee2f33f8d9cb | https://github.com/Zikoat/musweeper/tree/07e3e5e5e5e4edad4d8b1b6bb05aee2f33f8d9cb | import time
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.tensorboard import SummaryWriter
def singleton(cls):
obj = cls()
cls.__new__ = staticmethod(lambda cls: obj)
try:
del cls.__init__
except AttributeError:
pass
return cls
@singleton
cla... |
DenseGCNConv | # 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.nn import Parameter
import torch.utils.data
def glorot(tensor):
if tensor is not None:
stdv = math.sqrt(6.0 / (tensor.size(-2) + tensor.size(-1)))
tensor.data.uniform_(-stdv, stdv)
def zeros(tensor):
if tensor is not None:
tensor.data.fill_(0)
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.... | cshjin/pytorch_geometric | DenseGCNConv | false | 1,765 | [
"MIT"
] | 0 | 8dd0e76beb72135949a275edd851f80f7b97648f | https://github.com/cshjin/pytorch_geometric/tree/8dd0e76beb72135949a275edd851f80f7b97648f | import math
import torch
from torch.nn import Parameter
import torch.utils.data
def glorot(tensor):
if tensor is not None:
stdv = math.sqrt(6.0 / (tensor.size(-2) + tensor.size(-1)))
tensor.data.uniform_(-stdv, stdv)
def zeros(tensor):
if tensor is not None:
tensor.data.fill_(0)
cl... |
InnerProductProbe | # 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 InnerProductProbe(nn.Module):
def __init__(self, length: 'int', max_rank: 'int'=None):
super().__init__()
self.length = length
if max_rank is None:
max_rank = length
self.b = nn.Parameter(torch.empty(max_rank, length, dtype=torc... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | daemon/vizbert | InnerProductProbe | false | 1,766 | [
"MIT"
] | 0 | e40b7d1529f8857050313f8d87ff03b1b7226c9e | https://github.com/daemon/vizbert/tree/e40b7d1529f8857050313f8d87ff03b1b7226c9e | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, length: 'int', max_rank: 'int'=None):
super().__init__()
self.length = length
if max_rank is None:
max_rank = length
self.b = nn.Parameter(torch.empty(max_rank, length, dtype=torch.
... |
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
from torch import nn as nn
def conv3d(in_channels, out_channels, kernel_size, bias, padding=1):
return nn.Conv3d(in_channels, out_channels, kernel_size, padding=
padding, bias=bias)
def create_conv(in_channels, out_channels, kernel_size, order, num_groups,
padding=1):
"""
Create... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | cviaai/TMJ_tracking | Encoder | false | 1,767 | [
"MIT"
] | 0 | 464ca21dbeb538dc9504bd5d0e5c4d92591e69c4 | https://github.com/cviaai/TMJ_tracking/tree/464ca21dbeb538dc9504bd5d0e5c4d92591e69c4 | import torch
from torch import nn as nn
def conv3d(in_channels, out_channels, kernel_size, bias, padding=1):
return nn.Conv3d(in_channels, out_channels, kernel_size, padding=
padding, bias=bias)
def create_conv(in_channels, out_channels, kernel_size, order, num_groups,
padding=1):
"""
Create... |
MultiHead | # 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 Tensor
from torch.nn import Linear
import torch.nn.functional as F
from torch.nn import Parameter
import torch.utils.data
def uniform(size, tensor):
bound = 1.0 / math.sqrt(size)
if tensor is not None:
tensor.data.uniform_(-bound, bound)
def kaiming_uniform... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | cshjin/pytorch_geometric | MultiHead | false | 1,768 | [
"MIT"
] | 0 | 8dd0e76beb72135949a275edd851f80f7b97648f | https://github.com/cshjin/pytorch_geometric/tree/8dd0e76beb72135949a275edd851f80f7b97648f | import math
import torch
from torch import Tensor
from torch.nn import Linear
import torch.nn.functional as F
from torch.nn import Parameter
import torch.utils.data
def uniform(size, tensor):
bound = 1.0 / math.sqrt(size)
if tensor is not None:
tensor.data.uniform_(-bound, bound)
def kaiming_uniform... |
MinibatchStdDev | # 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 MinibatchStdDev(nn.Module):
"""
Minibatch standard deviation layer for the discriminator, try to prevent mode collapse
From https://github.com/akanimax/BMSG-GAN/
"""
def __init__(self):
"""
derived class constructor
"""
supe... | 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_... | cwza/deep_t2i | MinibatchStdDev | false | 1,769 | [
"Apache-2.0"
] | 0 | 22877fdd28ad407984ddc3bc4d57109c54c22fc0 | https://github.com/cwza/deep_t2i/tree/22877fdd28ad407984ddc3bc4d57109c54c22fc0 | import torch
import torch.nn as nn
class Model(nn.Module):
"""
Minibatch standard deviation layer for the discriminator, try to prevent mode collapse
From https://github.com/akanimax/BMSG-GAN/
"""
def __init__(self):
"""
derived class constructor
"""
super().__init... |
Linker | # 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 Linker(nn.Module):
def __init__(self, inplanes, outplanes, kernel_size, strides):
super(Linker, self).__init__()
self.avgpool = nn.AvgPool2d(kernel_size, strides)
self.pad = (0, 0, 0, 0) + ((outplanes - inplanes) // ... | 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... | daniel03c1/audio_augment | Linker | false | 1,770 | [
"MIT"
] | 0 | ee73bb0844e22c57c9cbeb129560da4a3853f77d | https://github.com/daniel03c1/audio_augment/tree/ee73bb0844e22c57c9cbeb129560da4a3853f77d | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, inplanes, outplanes, kernel_size, strides):
super().__init__()
self.avgpool = nn.AvgPool2d(kernel_size, strides)
self.pad = (0, 0, 0, 0) + ((outplanes - inplanes) // 2,) * 2
... |
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
import torch.nn as nn
class Swish(nn.Module):
"""Applies the element-wise function:
.. math::
\\text{Swish}(x) = x * \\text{Sigmoid}(\\alpha * x) for constant value alpha.
Citation: Searching for Activation Functions, Ramachandran et al., 2017, https://arxiv.org/abs/... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.... | danielschulz/MONAI | Swish | false | 1,772 | [
"Apache-2.0"
] | 0 | 54ef6e9e700f0de3d50184c0148f953be871a58e | https://github.com/danielschulz/MONAI/tree/54ef6e9e700f0de3d50184c0148f953be871a58e | import torch
import torch.nn
import torch.nn as nn
class Model(nn.Module):
"""Applies the element-wise function:
.. math::
\\text{Swish}(x) = x * \\text{Sigmoid}(\\alpha * x) for constant value alpha.
Citation: Searching for Activation Functions, Ramachandran et al., 2017, https://arxiv.org/abs/... |
DistanceMatrixLoss | # 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 DistanceMatrixLoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, scores, labels, mask):
sq_lengths = mask.view(mask.size(0), -1).sum(1)
l1_diff = (mask * torch.abs(scores - labels)).view(labels.size(0), -1
).... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | daemon/vizbert | DistanceMatrixLoss | false | 1,773 | [
"MIT"
] | 0 | e40b7d1529f8857050313f8d87ff03b1b7226c9e | https://github.com/daemon/vizbert/tree/e40b7d1529f8857050313f8d87ff03b1b7226c9e | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, scores, labels, mask):
sq_lengths = mask.view(mask.size(0), -1).sum(1)
l1_diff = (mask * torch.abs(scores - labels)).view(labels.size(0), -1
).sum(1)
... |
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 import nn as nn
def conv3d(in_channels, out_channels, kernel_size, bias, padding=1):
return nn.Conv3d(in_channels, out_channels, kernel_size, padding=
padding, bias=bias)
def create_conv(in_channels, out_channels, kernel_size, order, num_groups,
padding=1):
"""
Create... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | cviaai/TMJ_tracking | ExtResNetBlock | false | 1,774 | [
"MIT"
] | 0 | 464ca21dbeb538dc9504bd5d0e5c4d92591e69c4 | https://github.com/cviaai/TMJ_tracking/tree/464ca21dbeb538dc9504bd5d0e5c4d92591e69c4 | import torch
from torch import nn as nn
def conv3d(in_channels, out_channels, kernel_size, bias, padding=1):
return nn.Conv3d(in_channels, out_channels, kernel_size, padding=
padding, bias=bias)
def create_conv(in_channels, out_channels, kernel_size, order, num_groups,
padding=1):
"""
Create... |
DepthwiseSeparableConv2d | # 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.lr_scheduler
import torch.utils.data
from torchvision.transforms import *
class DepthwiseSeparableConv2d(nn.Module):
def __init__(self, input, output, padding=0, bias=False):
super(DepthwiseSeparableConv2d, self).__init__()
self.depthwise = 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
import torch.nn as nn
import torch.optim.lr_scheduler
import torch.utils.data
fr... | csharpshooter/DeepLearning | DepthwiseSeparableConv2d | false | 1,775 | [
"MIT"
] | 0 | c1d20660c32076468970f7376931e1fcd0d2644e | https://github.com/csharpshooter/DeepLearning/tree/c1d20660c32076468970f7376931e1fcd0d2644e | import torch
import torch.nn as nn
import torch.optim.lr_scheduler
import torch.utils.data
from torchvision.transforms import *
class Model(nn.Module):
def __init__(self, input, output, padding=0, bias=False):
super().__init__()
self.depthwise = nn.Conv2d(input, input, kernel_size=3, padding=
... |
CARAFE | # 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 CARAFE(nn.Module):
def __init__(self, inC, outC, Kencoder=3, delta=2, Kup=5, Cm=64):
super(CARAFE, self).__init__()
self.Kencoder = Kencoder
self.delta = delta
self.Kup = Kup
self.down = nn.Conv2d(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.... | cs18chen/fbnn | CARAFE | false | 1,776 | [
"MIT"
] | 0 | 1f52c49f8d1e0e1fa7b5a04677351817c4c0e977 | https://github.com/cs18chen/fbnn/tree/1f52c49f8d1e0e1fa7b5a04677351817c4c0e977 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, inC, outC, Kencoder=3, delta=2, Kup=5, Cm=64):
super().__init__()
self.Kencoder = Kencoder
self.delta = delta
self.Kup = Kup
self.down = nn.Conv2d(in_channels=inC,... |
AMBinaryLoss | # 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 AMBinaryLoss(nn.Module):
def __init__(self, m=0.35, k=0.8, t=1, s=30, eps=1e-08, sym_adjustment=
False, auto_balance=False, label_smooth=0.0, gamma_neg=0, gamma_pos=0):
super().__init__()
self.sym_adjustment = sym_adjustment
self.gamma_neg ... | 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
... | daniil-lyakhov/deep-object-reid | AMBinaryLoss | false | 1,777 | [
"Apache-2.0"
] | 0 | b0f7d6a2d4cff8c417a66d82c09d16788d81ec67 | https://github.com/daniil-lyakhov/deep-object-reid/tree/b0f7d6a2d4cff8c417a66d82c09d16788d81ec67 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, m=0.35, k=0.8, t=1, s=30, eps=1e-08, sym_adjustment=
False, auto_balance=False, label_smooth=0.0, gamma_neg=0, gamma_pos=0):
super().__init__()
self.sym_adjustment = sym_adjustment
self.gamma_neg = gamma... |
MaxPoolBranch | # 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 MaxPoolBranch(nn.Module):
"""
InceptionV4 specific max pooling branch block.
"""
def __init__(self, kernel_size=3, stride=2, padding=0):
super(MaxPoolBranch, self).__init__()
self.pool = nn.MaxPool2d(kernel_size=kernel_size, stride=stride,
... | 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... | daniil-lyakhov/deep-object-reid | MaxPoolBranch | false | 1,778 | [
"Apache-2.0"
] | 0 | b0f7d6a2d4cff8c417a66d82c09d16788d81ec67 | https://github.com/daniil-lyakhov/deep-object-reid/tree/b0f7d6a2d4cff8c417a66d82c09d16788d81ec67 | import torch
import torch.nn as nn
class Model(nn.Module):
"""
InceptionV4 specific max pooling branch block.
"""
def __init__(self, kernel_size=3, stride=2, padding=0):
super().__init__()
self.pool = nn.MaxPool2d(kernel_size=kernel_size, stride=stride,
padding=padding)
... |
AsymmetricLoss | # 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 AsymmetricLoss(nn.Module):
""" Notice - optimized version, minimizes memory allocation and gpu uploading,
favors inplace operations"""
def __init__(self, gamma_neg=4, gamma_pos=0, probability_margin=0.05,
eps=1e-08, label_smooth=0.0):
super().__ini... | 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... | daniil-lyakhov/deep-object-reid | AsymmetricLoss | false | 1,779 | [
"Apache-2.0"
] | 0 | b0f7d6a2d4cff8c417a66d82c09d16788d81ec67 | https://github.com/daniil-lyakhov/deep-object-reid/tree/b0f7d6a2d4cff8c417a66d82c09d16788d81ec67 | import torch
import torch.nn as nn
class Model(nn.Module):
""" Notice - optimized version, minimizes memory allocation and gpu uploading,
favors inplace operations"""
def __init__(self, gamma_neg=4, gamma_pos=0, probability_margin=0.05,
eps=1e-08, label_smooth=0.0):
super().__init__()
... |
CBOW | # 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 CBOW(nn.Module):
def __init__(self, input_size, hidden_size):
super().__init__()
self.fc1 = nn.Linear(input_size, hidden_size)
self.fc2 = nn.Linear(hidden_size, input_size)
def forward(self, x):
x = sum([*x]).float()
x = self.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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | danny-1k/autocomplete_hist | CBOW | false | 1,780 | [
"BSD-2-Clause"
] | 0 | 0a553ea59e08f2ddca60a1f35e9cf14d43370100 | https://github.com/danny-1k/autocomplete_hist/tree/0a553ea59e08f2ddca60a1f35e9cf14d43370100 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, input_size, hidden_size):
super().__init__()
self.fc1 = nn.Linear(input_size, hidden_size)
self.fc2 = nn.Linear(hidden_size, input_size)
def forward(self, x):
x = sum([*x]).float()
x = self.... |
GumbelSigmoid | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
def gumbel(x, eps=1e-20):
return -torch.log(-torch.log(torch.rand_like(x) + eps) + eps)
class GumbelSigmoid(nn.Module):
def __init__(self, scale=1.0):
super().__init__()
self.scale = float(scale)
def forward(self, logits):
y = logits + gumbel(... | 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... | daniil-lyakhov/deep-object-reid | GumbelSigmoid | false | 1,781 | [
"Apache-2.0"
] | 0 | b0f7d6a2d4cff8c417a66d82c09d16788d81ec67 | https://github.com/daniil-lyakhov/deep-object-reid/tree/b0f7d6a2d4cff8c417a66d82c09d16788d81ec67 | import torch
import torch.nn as nn
def gumbel(x, eps=1e-20):
return -torch.log(-torch.log(torch.rand_like(x) + eps) + eps)
class Model(nn.Module):
def __init__(self, scale=1.0):
super().__init__()
self.scale = float(scale)
def forward(self, logits):
y = logits + gumbel(logits) ... |
TorchDiceLoss | # 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
def soft_dice_loss(outputs, targets, per_image=False):
batch_size = outputs.size()[0]
eps = 1e-05
if not per_image:
batch_size = 1
dice_target = targets.contiguous().view(batch_size, -1).float()
dice_output = outputs.contiguous().view(batch_size, -1)
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
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | dannyjeck-matroid/solaris | TorchDiceLoss | false | 1,782 | [
"Apache-2.0"
] | 0 | 463d220c1fe14f811cbbbf528a7353022538006e | https://github.com/dannyjeck-matroid/solaris/tree/463d220c1fe14f811cbbbf528a7353022538006e | import torch
from torch import nn
def soft_dice_loss(outputs, targets, per_image=False):
batch_size = outputs.size()[0]
eps = 1e-05
if not per_image:
batch_size = 1
dice_target = targets.contiguous().view(batch_size, -1).float()
dice_output = outputs.contiguous().view(batch_size, -1)
i... |
Normalize | # 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 Normalize(nn.Module):
"""
Scale Audio to be between -1 and 1
"""
def __init__(self):
super(Normalize, self).__init__()
def forward(self, audio: 'torch.Tensor'):
if len(audio.shape) != 2:
raise ValueError('Audio should be 2D: [... | 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... | dariocazzani/vo-id | Normalize | false | 1,783 | [
"MIT"
] | 0 | 41d0f2779e7909cfa15afcb6c8222c48a5855eb8 | https://github.com/dariocazzani/vo-id/tree/41d0f2779e7909cfa15afcb6c8222c48a5855eb8 | import torch
import torch.nn as nn
class Model(nn.Module):
"""
Scale Audio to be between -1 and 1
"""
def __init__(self):
super().__init__()
def forward(self, audio: 'torch.Tensor'):
if len(audio.shape) != 2:
raise ValueError('Audio should be 2D: [batch_size X audio_... |
LCTGate | # 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 LCTGate(nn.Module):
def __init__(self, channels, groups=16):
super(LCTGate, self).__init__()
assert channels > 0
assert groups > 0
while channels % groups != 0:
groups //= 2
self.gn = nn.GroupNorm(groups, channels, affin... | 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_... | daniil-lyakhov/deep-object-reid | LCTGate | false | 1,784 | [
"Apache-2.0"
] | 0 | b0f7d6a2d4cff8c417a66d82c09d16788d81ec67 | https://github.com/daniil-lyakhov/deep-object-reid/tree/b0f7d6a2d4cff8c417a66d82c09d16788d81ec67 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, channels, groups=16):
super().__init__()
assert channels > 0
assert groups > 0
while channels % groups != 0:
groups //= 2
self.gn = nn.GroupNorm(groups, channels, affine=True)
... |
DiceLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import collections
import torch
import warnings
from typing import Optional
from typing import Union
from typing import Any
from typing import Callable
from typing import Tuple
import torch.nn
from torch.nn.modules.loss import _Loss
from enum import Enum
import collections.abc
def issequenceiterable(obj: 'Any') ->boo... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import collections
from typing import Optional
from typing import Union
from typing import Any
from typing import Callable
from typing impor... | danielschulz/MONAI | DiceLoss | false | 1,785 | [
"Apache-2.0"
] | 0 | 54ef6e9e700f0de3d50184c0148f953be871a58e | https://github.com/danielschulz/MONAI/tree/54ef6e9e700f0de3d50184c0148f953be871a58e | import collections
import torch
import warnings
from typing import Optional
from typing import Union
from typing import Any
from typing import Callable
from typing import Tuple
import torch.nn
from torch.nn.modules.loss import _Loss
from enum import Enum
import collections.abc
def issequenceiterable(obj: 'Any') ->boo... |
TorchJaccardLoss | # 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 TorchJaccardLoss(torch.nn.modules.Module):
def __init__(self):
super(TorchJaccardLoss, self).__init__()
def forward(self, outputs, targets):
eps = 1e-15
jaccard_target = (targets == 1).float()
jaccard_output = torch.sigmoid(outputs)
intersection = (... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | dannyjeck-matroid/solaris | TorchJaccardLoss | false | 1,786 | [
"Apache-2.0"
] | 0 | 463d220c1fe14f811cbbbf528a7353022538006e | https://github.com/dannyjeck-matroid/solaris/tree/463d220c1fe14f811cbbbf528a7353022538006e | import torch
class Model(torch.nn.modules.Module):
def __init__(self):
super().__init__()
def forward(self, outputs, targets):
eps = 1e-15
jaccard_target = (targets == 1).float()
jaccard_output = torch.sigmoid(outputs)
intersection = (jaccard_output * jaccard_target).... |
Normalize3D | # 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 Normalize3D(nn.Module):
"""
Scale Spectrogram to be between 0 and 1
"""
def __init__(self):
super(Normalize3D, self).__init__()
def forward(self, X: 'torch.Tensor'):
if len(X.shape) != 3:
raise ValueError(
'Inp... | 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... | dariocazzani/vo-id | Normalize3D | false | 1,787 | [
"MIT"
] | 0 | 41d0f2779e7909cfa15afcb6c8222c48a5855eb8 | https://github.com/dariocazzani/vo-id/tree/41d0f2779e7909cfa15afcb6c8222c48a5855eb8 | import torch
import torch.nn as nn
class Model(nn.Module):
"""
Scale Spectrogram to be between 0 and 1
"""
def __init__(self):
super().__init__()
def forward(self, X: 'torch.Tensor'):
if len(X.shape) != 3:
raise ValueError(
'Input should be 3D: [batch... |
BertPooler | # 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 _paritybench_helpers import _mock_config
import torch
from torch import nn
import torch.onnx
class BertPooler(nn.Module):
def __init__(self, config):
super(BertPooler, self).__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | Alwaysproblem/examples-1 | BertPooler | false | 1,788 | [
"MIT"
] | 0 | 9754fa63ed1931489a21ac1f5b299f945e369a5c | https://github.com/Alwaysproblem/examples-1/tree/9754fa63ed1931489a21ac1f5b299f945e369a5c | from _paritybench_helpers import _mock_config
import torch
from torch import nn
import torch.onnx
class Model(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
self.cls_position = c... |
CriticNet | # 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 CriticNet(nn.Module):
"""Critic (Value estimator) Model."""
def __init__(self, state_size, action_size, seed, fc1_units=64,
fc2_units=64):
"""Initialize parameters and build model.
Params
======
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | danthe42/drlnd_p2 | CriticNet | false | 1,789 | [
"MIT"
] | 0 | 693813feb7c99f3e01da583e5b67e4f8904639c4 | https://github.com/danthe42/drlnd_p2/tree/693813feb7c99f3e01da583e5b67e4f8904639c4 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
"""Critic (Value estimator) Model."""
def __init__(self, state_size, action_size, seed, fc1_units=64,
fc2_units=64):
"""Initialize parameters and build model.
Params
======
s... |
SetConv | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch.nn import functional as F
import torch.nn as nn
class SetConv(nn.Module):
def __init__(self, sample_feats, predicate_feats, join_feats, hid_units):
super(SetConv, self).__init__()
self.sample_mlp1 = nn.Linear(sample_feats, hid_units)
self.sample_mlp2 = nn.Linear(hi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | danield137/deep_query_optimzation | SetConv | false | 1,790 | [
"MIT"
] | 0 | 01a25c966338007f15d14dea1b37e388e47bcfe3 | https://github.com/danield137/deep_query_optimzation/tree/01a25c966338007f15d14dea1b37e388e47bcfe3 | import torch
from torch.nn import functional as F
import torch.nn as nn
class Model(nn.Module):
def __init__(self, sample_feats, predicate_feats, join_feats, hid_units):
super().__init__()
self.sample_mlp1 = nn.Linear(sample_feats, hid_units)
self.sample_mlp2 = nn.Linear(hid_units, hid_un... |
SetConv | # 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 SetConv(nn.Module):
def __init__(self, sample_feats, predicate_feats, predicate_uri_feats,
join_feats, hid_units):
super(SetConv, self).__init__()
self.sample_mlp1 = nn.Linear(sample_feats, hid_units)
self.sa... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | dacasals/learnedcardinalities | SetConv | false | 1,791 | [
"MIT"
] | 0 | ee9741ce1a7b55ed18c33fbd6047484e50068037 | https://github.com/dacasals/learnedcardinalities/tree/ee9741ce1a7b55ed18c33fbd6047484e50068037 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, sample_feats, predicate_feats, predicate_uri_feats,
join_feats, hid_units):
super().__init__()
self.sample_mlp1 = nn.Linear(sample_feats, hid_units)
self.sample_mlp2 = nn.... |
SoftArgmax2D | # 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
from typing import Optional
def create_meshgrid(x: 'torch.Tensor', normalized_coordinates: 'Optional[bool]'
) ->torch.Tensor:
assert len(x.shape) == 4, x.shape
_, _, height, width = x.shape
_device, _dtype = x.device, x.dtype
if normalized_coordinates:
xs... | 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
... | danyayay/ynet_adaptive | SoftArgmax2D | false | 1,792 | [
"MIT"
] | 0 | f1daea6f3d5ec8a7349c2ee72bf742df83786103 | https://github.com/danyayay/ynet_adaptive/tree/f1daea6f3d5ec8a7349c2ee72bf742df83786103 | import torch
import torch.nn as nn
from typing import Optional
def create_meshgrid(x: 'torch.Tensor', normalized_coordinates: 'Optional[bool]'
) ->torch.Tensor:
assert len(x.shape) == 4, x.shape
_, _, height, width = x.shape
_device, _dtype = x.device, x.dtype
if normalized_coordinates:
xs... |
FilterNorm | # 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
from torch.nn.init import calculate_gain
class FilterNorm(nn.Module):
def __init__(self, in_channels, kernel_size, filter_type, nonlinearity=
'linear', running_std=False, running_mean=False):
assert filter_type in ('spatial', 'channel')
assert in_channel... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
from torch.nn.init import calculate_gain
assert_size_stri... | danyayay/ynet_adaptive | FilterNorm | false | 1,793 | [
"MIT"
] | 0 | f1daea6f3d5ec8a7349c2ee72bf742df83786103 | https://github.com/danyayay/ynet_adaptive/tree/f1daea6f3d5ec8a7349c2ee72bf742df83786103 | import torch
import torch.nn as nn
from torch.nn.init import calculate_gain
class Model(nn.Module):
def __init__(self, in_channels, kernel_size, filter_type, nonlinearity=
'linear', running_std=False, running_mean=False):
assert filter_type in ('spatial', 'channel')
assert in_channels >= ... |
TorchFocalLoss | # 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.functional as F
from torch import nn
class TorchFocalLoss(nn.Module):
"""Implementation of Focal Loss[1]_ modified from Catalyst [2]_ .
Arguments
---------
gamma : :class:`int` or :class:`float`
Focusing parameter. See [1]_ .
alpha : :class:`int` or :class:`fl... | 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 ... | dannyjeck-matroid/solaris | TorchFocalLoss | false | 1,794 | [
"Apache-2.0"
] | 0 | 463d220c1fe14f811cbbbf528a7353022538006e | https://github.com/dannyjeck-matroid/solaris/tree/463d220c1fe14f811cbbbf528a7353022538006e | import torch
import torch.nn.functional as F
from torch import nn
class Model(nn.Module):
"""Implementation of Focal Loss[1]_ modified from Catalyst [2]_ .
Arguments
---------
gamma : :class:`int` or :class:`float`
Focusing parameter. See [1]_ .
alpha : :class:`int` or :class:`float`
... |
Cauchy | # 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.model_zoo
class Cauchy(nn.Module):
def __init__(self):
super(Cauchy, self).__init__()
self.c = 1.0
def forward(self, X, Y):
r = torch.add(X, -Y)
ra = torch.abs(r)
error = 0.5 * self.c ** 2 * torch.log(1 + (ra / 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 import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | davefiorino/EDSR-PyTorch | Cauchy | false | 1,795 | [
"MIT"
] | 0 | 97ad32a09a71816a36c45d92cdb2ea7ab42ba685 | https://github.com/davefiorino/EDSR-PyTorch/tree/97ad32a09a71816a36c45d92cdb2ea7ab42ba685 | import torch
import torch.nn as nn
import torch.utils.model_zoo
class Model(nn.Module):
def __init__(self):
super().__init__()
self.c = 1.0
def forward(self, X, Y):
r = torch.add(X, -Y)
ra = torch.abs(r)
error = 0.5 * self.c ** 2 * torch.log(1 + (ra / self.c) ** 2)
... |
Fair | # 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.model_zoo
class Fair(nn.Module):
def __init__(self):
super(Fair, self).__init__()
self.c = 1.0
def forward(self, X, Y):
r = torch.add(X, -Y)
ra = torch.abs(r)
error = self.c ** 2 * (ra / self.c - torch.log(1 + ra /... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | davefiorino/EDSR-PyTorch | Fair | false | 1,796 | [
"MIT"
] | 0 | 97ad32a09a71816a36c45d92cdb2ea7ab42ba685 | https://github.com/davefiorino/EDSR-PyTorch/tree/97ad32a09a71816a36c45d92cdb2ea7ab42ba685 | import torch
import torch.nn as nn
import torch.utils.model_zoo
class Model(nn.Module):
def __init__(self):
super().__init__()
self.c = 1.0
def forward(self, X, Y):
r = torch.add(X, -Y)
ra = torch.abs(r)
error = self.c ** 2 * (ra / self.c - torch.log(1 + ra / self.c))... |
Charbonnier | # 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.model_zoo
class Charbonnier(nn.Module):
def __init__(self):
super(Charbonnier, self).__init__()
self.eps = 1e-06
def forward(self, X, Y):
diff = torch.add(X, -Y)
error = torch.sqrt(diff * diff + self.eps)
loss = to... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import... | davefiorino/EDSR-PyTorch | Charbonnier | false | 1,797 | [
"MIT"
] | 0 | 97ad32a09a71816a36c45d92cdb2ea7ab42ba685 | https://github.com/davefiorino/EDSR-PyTorch/tree/97ad32a09a71816a36c45d92cdb2ea7ab42ba685 | import torch
import torch.nn as nn
import torch.utils.model_zoo
class Model(nn.Module):
def __init__(self):
super().__init__()
self.eps = 1e-06
def forward(self, X, Y):
diff = torch.add(X, -Y)
error = torch.sqrt(diff * diff + self.eps)
loss = torch.sum(error)
... |
Classifier | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.distributed
import torch
import torch.nn as nn
class Classifier(nn.Module):
def __init__(self, hidden_size):
super(Classifier, self).__init__()
self.linear1 = nn.Linear(hidden_size, 1)
self.sigmoid = nn.Sigmoid()
def forward(self, x, mask_cls):
h = 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
import torch.distributed
import torch
import torch.nn as nn
assert_size_stride =... | dat821168/PreSumm | Classifier | false | 1,798 | [
"MIT"
] | 0 | 3c84fc97f50a193a865ccef2300adf5683397539 | https://github.com/dat821168/PreSumm/tree/3c84fc97f50a193a865ccef2300adf5683397539 | import torch
import torch.distributed
import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, hidden_size):
super().__init__()
self.linear1 = nn.Linear(hidden_size, 1)
self.sigmoid = nn.Sigmoid()
def forward(self, x, mask_cls):
h = self.linear1(x).squeez... |
CustomBatchNormManualModule | # 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 CustomBatchNormManualFunction(torch.autograd.Function):
"""
This torch.autograd.Function implements a functional custom version of the batch norm operation for MLPs.
Using torch.autograd.Function allows you to write a custom backward function.
The function will... | 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_... | davide-belli/deep-learning-labs | CustomBatchNormManualModule | false | 1,799 | [
"MIT"
] | 0 | 1acd37a527711dccdc00c1935724cc5de7c10955 | https://github.com/davide-belli/deep-learning-labs/tree/1acd37a527711dccdc00c1935724cc5de7c10955 | import torch
import torch.nn as nn
class CustomBatchNormManualFunction(torch.autograd.Function):
"""
This torch.autograd.Function implements a functional custom version of the batch norm operation for MLPs.
Using torch.autograd.Function allows you to write a custom backward function.
The function will... |
Discriminator | # 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 Discriminator(nn.Module):
def __init__(self):
super(Discriminator, self).__init__()
self.linear1 = nn.Linear(784, 512)
self.lrelu2 = nn.LeakyReLU(0.2)
self.linear2 = nn.Linear(512, 256)
self.lrelu3 = nn.LeakyReLU(0.2)
self.l... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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... | davide-belli/deep-learning-labs | Discriminator | false | 1,800 | [
"MIT"
] | 0 | 1acd37a527711dccdc00c1935724cc5de7c10955 | https://github.com/davide-belli/deep-learning-labs/tree/1acd37a527711dccdc00c1935724cc5de7c10955 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
self.linear1 = nn.Linear(784, 512)
self.lrelu2 = nn.LeakyReLU(0.2)
self.linear2 = nn.Linear(512, 256)
self.lrelu3 = nn.LeakyReLU(0.2)
self.linear3 = nn.Linear(256, 1)
... |
ConcatPool2d | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
class ConcatPool2d(nn.Module):
"""Layer that concats `AvgPool2d` and `MaxPool2d`"""
def __init__(self, ks, stride=None, padding=0):
super().__init__()
self.ap = nn.AvgPool2d(ks, stride, padding)
self.mp = nn.MaxPool2d(ks, stride, padding)
def fo... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | davidleonfdez/face2anime | ConcatPool2d | false | 1,801 | [
"MIT"
] | 0 | 896bf85a7aa28322cc9e9e586685db8cbbf39d89 | https://github.com/davidleonfdez/face2anime/tree/896bf85a7aa28322cc9e9e586685db8cbbf39d89 | import torch
import torch.nn as nn
class Model(nn.Module):
"""Layer that concats `AvgPool2d` and `MaxPool2d`"""
def __init__(self, ks, stride=None, padding=0):
super().__init__()
self.ap = nn.AvgPool2d(ks, stride, padding)
self.mp = nn.MaxPool2d(ks, stride, padding)
def forward(s... |
CustomBatchNormAutograd | # 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 CustomBatchNormAutograd(nn.Module):
"""
This nn.module implements a custom version of the batch norm operation for MLPs.
The operations called in self.forward track the history if the input tensors have the
flag requires_grad set to True. The backward pass does... | 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_... | davide-belli/deep-learning-labs | CustomBatchNormAutograd | false | 1,802 | [
"MIT"
] | 0 | 1acd37a527711dccdc00c1935724cc5de7c10955 | https://github.com/davide-belli/deep-learning-labs/tree/1acd37a527711dccdc00c1935724cc5de7c10955 | import torch
import torch.nn as nn
class Model(nn.Module):
"""
This nn.module implements a custom version of the batch norm operation for MLPs.
The operations called in self.forward track the history if the input tensors have the
flag requires_grad set to True. The backward pass does not need to be im... |
PixelWise | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.init
class PixelWise(torch.nn.Module):
""" Implemented - https://arxiv.org/pdf/1710.10196.pdf """
def __init__(self, eps=1e-08):
super(PixelWise, self).__init__()
self.eps = eps
def forward(self, ten... | 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.utils.data
import torch.utils.data.distributed
import torch.nn.ini... | davidwagnerkc/TensorMONK | PixelWise | false | 1,803 | [
"MIT"
] | 0 | 3607836d3d6bfd0994e044536b2a51bc84b35f31 | https://github.com/davidwagnerkc/TensorMONK/tree/3607836d3d6bfd0994e044536b2a51bc84b35f31 | import torch
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.init
class Model(torch.nn.Module):
""" Implemented - https://arxiv.org/pdf/1710.10196.pdf """
def __init__(self, eps=1e-08):
super().__init__()
self.eps = eps
def forward(self, tensor):
retur... |
PositionwiseFeedForward | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
import torch.distributed
import torch
import torch.nn as nn
def gelu(x):
return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 *
torch.pow(x, 3))))
class PositionwiseFeedForward(nn.Module):
""" A two-layer Feed-Forward-Network with residual layer norm.
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import ... | dat821168/PreSumm | PositionwiseFeedForward | false | 1,804 | [
"MIT"
] | 0 | 3c84fc97f50a193a865ccef2300adf5683397539 | https://github.com/dat821168/PreSumm/tree/3c84fc97f50a193a865ccef2300adf5683397539 | import math
import torch
import torch.distributed
import torch
import torch.nn as nn
def gelu(x):
return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 *
torch.pow(x, 3))))
class Model(nn.Module):
""" A two-layer Feed-Forward-Network with residual layer norm.
Args:
d_m... |
MiniBatchStdDev | # 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 MiniBatchStdDev(nn.Module):
"""Layer that appends to every element of a batch a new ftr map containing the std of its group."""
def __init__(self, group_sz=4, unbiased_std=False):
super().__init__()
self.group_sz = group_sz
self.unbiased_std = ... | 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_... | davidleonfdez/face2anime | MiniBatchStdDev | false | 1,805 | [
"MIT"
] | 0 | 896bf85a7aa28322cc9e9e586685db8cbbf39d89 | https://github.com/davidleonfdez/face2anime/tree/896bf85a7aa28322cc9e9e586685db8cbbf39d89 | import torch
import torch.nn as nn
class Model(nn.Module):
"""Layer that appends to every element of a batch a new ftr map containing the std of its group."""
def __init__(self, group_sz=4, unbiased_std=False):
super().__init__()
self.group_sz = group_sz
self.unbiased_std = unbiased_s... |
MultiheadAttention | # 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 as th
import torch.nn as nn
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.... | dashstander/glide-text2im | MultiheadAttention | false | 1,806 | [
"MIT"
] | 0 | 58f03a871ee0567e27fccc40df98203e675a9b8e | https://github.com/dashstander/glide-text2im/tree/58f03a871ee0567e27fccc40df98203e675a9b8e | import math
import torch
import torch as th
import torch.nn as nn
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
... |
Q_Index | # 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 Q_Index(nn.Module):
"""
Quality measurement between perturbated (image with applied noise) and denoised target image.
This module works only for images with a single color channel (grayscale)
"""
def __init__(self):
super().__init__()
def forw... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | dawnofthedebayan/MedAI_Project | Q_Index | false | 1,807 | [
"Apache-2.0"
] | 0 | a7f2597c96569662f1ca9d21ffd0eb41c77211c1 | https://github.com/dawnofthedebayan/MedAI_Project/tree/a7f2597c96569662f1ca9d21ffd0eb41c77211c1 | import torch
import torch.nn as nn
class Model(nn.Module):
"""
Quality measurement between perturbated (image with applied noise) and denoised target image.
This module works only for images with a single color channel (grayscale)
"""
def __init__(self):
super().__init__()
def forwar... |
myNet | # 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 myNet(nn.Module):
def __init__(self, in_features, num_classes=10):
super(myNet, self).__init__()
self.fc1 = nn.Linear(in_features, 1000)
self.fc2 = nn.Linear(1000, 100)
self.fc3 = nn.Linear(100, num_classes)
self.relu = nn.ReLU()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | daxiongpro/pytorch-tutorial | myNet | false | 1,808 | [
"MIT"
] | 0 | abafc32f7ee1092024085f703e4ced51ce358a1b | https://github.com/daxiongpro/pytorch-tutorial/tree/abafc32f7ee1092024085f703e4ced51ce358a1b | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, in_features, num_classes=10):
super().__init__()
self.fc1 = nn.Linear(in_features, 1000)
self.fc2 = nn.Linear(1000, 100)
self.fc3 = nn.Linear(100, num_classes)
self.relu = nn.ReLU()
def forw... |
ConvolutionTranspose | # 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.utils.data.distributed
import torch.nn.init
import torch.nn as nn
import torch.nn.functional as F
def Normalizations(tensor_size=None, normalization=None, available=False,
**kwargs):
"""Does normalization on 4D tensor.
Args:
tensor_size: shape of ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
impor... | davidwagnerkc/TensorMONK | ConvolutionTranspose | false | 1,809 | [
"MIT"
] | 0 | 3607836d3d6bfd0994e044536b2a51bc84b35f31 | https://github.com/davidwagnerkc/TensorMONK/tree/3607836d3d6bfd0994e044536b2a51bc84b35f31 | import torch
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.init
import torch.nn as nn
import torch.nn.functional as F
def Normalizations(tensor_size=None, normalization=None, available=False,
**kwargs):
"""Does normalization on 4D tensor.
Args:
tensor_size: shape of ... |
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... | from torch.nn import Module
import math
import torch
from torch.nn.modules.utils import _pair
import torch.nn.functional as F
import torch.utils.data
from torch.nn.parameter import Parameter
from torch.nn.functional import pad
from torch.nn.modules import Module
def conv2d_same_padding(input, weight, bias=None, 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
from torch.nn import Module
import math
from torch.nn.modules.utils import _pair... | ddayzzz/mmdetection | Conv2d | false | 1,810 | [
"Apache-2.0"
] | 0 | b9940c56cc19b3dda7468a5fd858b9683e93a486 | https://github.com/ddayzzz/mmdetection/tree/b9940c56cc19b3dda7468a5fd858b9683e93a486 | from torch.nn import Module
import math
import torch
from torch.nn.modules.utils import _pair
import torch.nn.functional as F
import torch.utils.data
from torch.nn.parameter import Parameter
from torch.nn.functional import pad
from torch.nn.modules import Module
def conv2d_same_padding(input, weight, bias=None, strid... |
GeneralizedDiceLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import collections
import torch
import warnings
from typing import Optional
from typing import Union
from typing import Any
from typing import Callable
from typing import Tuple
import torch.nn
from torch.nn.modules.loss import _Loss
from enum import Enum
import collections.abc
def issequenceiterable(obj: 'Any') ->boo... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import collections
from typi... | danielschulz/MONAI | GeneralizedDiceLoss | false | 1,811 | [
"Apache-2.0"
] | 0 | 54ef6e9e700f0de3d50184c0148f953be871a58e | https://github.com/danielschulz/MONAI/tree/54ef6e9e700f0de3d50184c0148f953be871a58e | import collections
import torch
import warnings
from typing import Optional
from typing import Union
from typing import Any
from typing import Callable
from typing import Tuple
import torch.nn
from torch.nn.modules.loss import _Loss
from enum import Enum
import collections.abc
def issequenceiterable(obj: 'Any') ->boo... |
AngularPenaltySMLoss | # 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 AngularPenaltySMLoss(nn.Module):
def __init__(self, in_features, out_features, loss_type='arcface', eps=
1e-07, s=None, m=None):
"""
Angular Penalty Softmax Loss
Three 'loss_types' available: ['arcface', 'sp... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | dayoungMM/Angular-Penalty-Softmax-Losses-Pytorch | AngularPenaltySMLoss | false | 1,812 | [
"MIT"
] | 0 | 5599f2e280b2af8d40e53727290eb797d18e7239 | https://github.com/dayoungMM/Angular-Penalty-Softmax-Losses-Pytorch/tree/5599f2e280b2af8d40e53727290eb797d18e7239 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, in_features, out_features, loss_type='arcface', eps=
1e-07, s=None, m=None):
"""
Angular Penalty Softmax Loss
Three 'loss_types' available: ['arcface', 'sphereface', 'cos... |
ToSEG | # 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.autograd import Function
import math
import torch
import warnings
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.cpp_extension
def fused_leaky_relu(input, bias=None, negative_slope=0.2, scale=2 ** 0.5):
if input.device.type == 'cpu':
if bias is not N... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.autograd import Function
import math
import warnings
import numpy as ... | crobbins327/semanticGAN_WSI | ToSEG | false | 1,813 | [
"BSD-2-Clause",
"MIT"
] | 0 | 4046ddc822f463e03952402247f79d540bf7be95 | https://github.com/crobbins327/semanticGAN_WSI/tree/4046ddc822f463e03952402247f79d540bf7be95 | from torch.autograd import Function
import math
import torch
import warnings
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.cpp_extension
def fused_leaky_relu(input, bias=None, negative_slope=0.2, scale=2 ** 0.5):
if input.device.type == 'cpu':
if bias is not N... |
ResidualAttentionBlock | # 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 as th
import torch.nn as nn
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.... | dashstander/glide-text2im | ResidualAttentionBlock | false | 1,814 | [
"MIT"
] | 0 | 58f03a871ee0567e27fccc40df98203e675a9b8e | https://github.com/dashstander/glide-text2im/tree/58f03a871ee0567e27fccc40df98203e675a9b8e | import math
import torch
import torch as th
import torch.nn as nn
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... |
FilterResponseNorm_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
class FilterResponseNorm_layer(nn.Module):
def __init__(self, num_filters, eps=1e-06):
super(FilterResponseNorm_layer, self).__init__()
self.eps = eps
par_shape = 1, num_filters, 1, 1
self.tau = torch.nn.Parameter(torch.zeros(par_shape))
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | deebuls/pytorch-cifar | FilterResponseNorm_layer | false | 1,815 | [
"MIT"
] | 0 | c6d9b16eeb00418d8c4f4f4c1e97f141c1f7d198 | https://github.com/deebuls/pytorch-cifar/tree/c6d9b16eeb00418d8c4f4f4c1e97f141c1f7d198 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, num_filters, eps=1e-06):
super().__init__()
self.eps = eps
par_shape = 1, num_filters, 1, 1
self.tau = torch.nn.Parameter(torch.zeros(par_shape))
self.beta = torch.nn.Parameter(torch.zeros(par_sh... |
TSA_Fusion | # 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.functional as F
import torch.nn as nn
class TSA_Fusion(nn.Module):
""" Temporal Spatial Attention fusion module
Temporal: correlation;
Spatial: 3 pyramid levels.
"""
def __init__(self, nf=64, nframes=5, center=2):
super(TSA_Fusion, self... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
impor... | creeper121386/EDVR-modified | TSA_Fusion | false | 1,816 | [
"Apache-2.0"
] | 0 | 3fa565b99811e8f84f6ea3793090614606382332 | https://github.com/creeper121386/EDVR-modified/tree/3fa565b99811e8f84f6ea3793090614606382332 | import torch
import torch.utils.data
import torch.nn.functional as F
import torch.nn as nn
class Model(nn.Module):
""" Temporal Spatial Attention fusion module
Temporal: correlation;
Spatial: 3 pyramid levels.
"""
def __init__(self, nf=64, nframes=5, center=2):
super().__init__()
... |
ps_FNNDenoiser | # 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 Linear
from torch.nn.init import xavier_normal_
from torch.nn.functional import relu
class ps_FNNDenoiser(Module):
def __init__(self, input_dim):
"""The FNN enc and FNN dec of the Denoiser.
:param input_dim: The input dimensionality.
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.nn import Module
f... | ddcas/singing-language-identification | ps_FNNDenoiser | false | 1,817 | [
"MIT"
] | 0 | d104419b196d56d4de37cff47c32e88e28c58690 | https://github.com/ddcas/singing-language-identification/tree/d104419b196d56d4de37cff47c32e88e28c58690 | from torch.nn import Module
import torch
from torch.nn import Linear
from torch.nn.init import xavier_normal_
from torch.nn.functional import relu
class Model(Module):
def __init__(self, input_dim):
"""The FNN enc and FNN dec of the Denoiser.
:param input_dim: The input dimensionality.
:... |
RegularizedLinear | # 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 RegularizedLinear(nn.Linear):
def __init__(self, *args, ar_weight=0.001, l1_weight=0.001, **kwargs):
super(RegularizedLinear, self).__init__(*args, **kwargs)
self.ar_weight = ar_weight
self.l1_weight = l1_weight
self._losses = {}
def 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.... | dearkafka/inferno | RegularizedLinear | false | 1,818 | [
"Apache-2.0"
] | 0 | e9e3b863fd1fc97cf94d08ac6b4f8df7665f996a | https://github.com/dearkafka/inferno/tree/e9e3b863fd1fc97cf94d08ac6b4f8df7665f996a | import torch
import torch.nn as nn
class Model(nn.Linear):
def __init__(self, *args, ar_weight=0.001, l1_weight=0.001, **kwargs):
super().__init__(*args, **kwargs)
self.ar_weight = ar_weight
self.l1_weight = l1_weight
self._losses = {}
def forward(self, input):
output... |
SorensenDiceLoss | # 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
from torch.autograd import Variable
def assert_(condition, message='', exception_type=AssertionError):
"""Like assert, but with arbitrary exception types."""
if not condition:
raise exception_type(message)
def flatten_samples(tensor_or_variable):
"""
Flatte... | 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... | dearkafka/inferno | SorensenDiceLoss | false | 1,819 | [
"Apache-2.0"
] | 0 | e9e3b863fd1fc97cf94d08ac6b4f8df7665f996a | https://github.com/dearkafka/inferno/tree/e9e3b863fd1fc97cf94d08ac6b4f8df7665f996a | import torch
import torch.nn as nn
from torch.autograd import Variable
def assert_(condition, message='', exception_type=AssertionError):
"""Like assert, but with arbitrary exception types."""
if not condition:
raise exception_type(message)
def flatten_samples(tensor_or_variable):
"""
Flatte... |
MTFullyConnected | # 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 time
import torch
import numpy as np
from torch import nn
from torch import optim
from torch.nn import functional as F
class Base(nn.Module):
""" This class is the base structure for all of classification/regression DNN models.
Mainly, it provides the general methods for training, evaluating model and ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 time
import numpy as n... | cthoyt/DrugEx | MTFullyConnected | false | 1,820 | [
"MIT"
] | 0 | 9e4d31adb2c65d0afc852948f502c79dcf8308a3 | https://github.com/cthoyt/DrugEx/tree/9e4d31adb2c65d0afc852948f502c79dcf8308a3 | import time
import torch
import numpy as np
from torch import nn
from torch import optim
from torch.nn import functional as F
class Base(nn.Module):
""" This class is the base structure for all of classification/regression DNN models.
Mainly, it provides the general methods for training, evaluating model and ... |
Maxout | # 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 Maxout(nn.Module):
def __init__(self, pool_size):
super().__init__()
self._pool_size = pool_size
def forward(self, x):
assert x.shape[-1
] % self._pool_size == 0, 'Wrong input last dim size ({}) for Maxout({})'.format(
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
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | demdecuong/SEGMENT | Maxout | false | 1,821 | [
"MIT"
] | 0 | 629dc55dcbc9629b35fb237e313b95ceacecdc89 | https://github.com/demdecuong/SEGMENT/tree/629dc55dcbc9629b35fb237e313b95ceacecdc89 | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, pool_size):
super().__init__()
self._pool_size = pool_size
def forward(self, x):
assert x.shape[-1
] % self._pool_size == 0, 'Wrong input last dim size ({}) for Maxout({})'.format(
x.... |
Debayer2x2 | # 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 Debayer2x2(nn.Module):
"""Demosaicing of Bayer images using 2x2 convolutions.
Requires BG-Bayer color filter array layout. That is,
the image[1,1]='B', image[1,2]='G'.
"""
def __init__(self):
super(Debayer2x2, self)... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | delldu/ImageClean | Debayer2x2 | false | 1,822 | [
"MIT"
] | 0 | ffa5b180d36afb3840c6b36c08a767c520068498 | https://github.com/delldu/ImageClean/tree/ffa5b180d36afb3840c6b36c08a767c520068498 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
"""Demosaicing of Bayer images using 2x2 convolutions.
Requires BG-Bayer color filter array layout. That is,
the image[1,1]='B', image[1,2]='G'.
"""
def __init__(self):
super().__init__()
s... |
MuSigmaEncoder | # 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 typing import Tuple
from torch import nn
class MuSigmaEncoder(nn.Module):
"""
Maps a representation r to mu and sigma which will define the normal
distribution from which we sample the latent variable z.
Parameters
----------
r_dim : int
Dimension of output representa... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | deltaskelta/neural-processes | MuSigmaEncoder | false | 1,823 | [
"MIT"
] | 0 | 34a6b98b7a9142f5e5f87f7f1644217d5aa9e1bb | https://github.com/deltaskelta/neural-processes/tree/34a6b98b7a9142f5e5f87f7f1644217d5aa9e1bb | import torch
from typing import Tuple
from torch import nn
class Model(nn.Module):
"""
Maps a representation r to mu and sigma which will define the normal
distribution from which we sample the latent variable z.
Parameters
----------
r_dim : int
Dimension of output representation r.
... |
fixed_loss | # 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 fixed_loss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, out_image, gt_image, est_noise, gt_noise, if_asym):
h_x = est_noise.size()[2]
w_x = est_noise.size()[3]
count_h = self._ten... | 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
... | delldu/ImageClean | fixed_loss | false | 1,824 | [
"MIT"
] | 0 | ffa5b180d36afb3840c6b36c08a767c520068498 | https://github.com/delldu/ImageClean/tree/ffa5b180d36afb3840c6b36c08a767c520068498 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, out_image, gt_image, est_noise, gt_noise, if_asym):
h_x = est_noise.size()[2]
w_x = est_noise.size()[3]
count_h = self._tensor_s... |
Discriminator | # 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
import torch.utils.data
def uniform(size, tensor):
stdv = 1.0 / math.sqrt(size)
if tensor is not None:
tensor.data.uniform_(-stdv, stdv)
class Discriminator(nn.Module):
def __init__(self, hidden_dim):
super(Discriminator, 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 math
import torch.nn as nn
import torch.utils.data
assert_size_stride = t... | dendisuhubdy/pytorch_geometric | Discriminator | false | 1,825 | [
"MIT"
] | 0 | a0592f61aef617c0c8ff61b3d822d04901054c22 | https://github.com/dendisuhubdy/pytorch_geometric/tree/a0592f61aef617c0c8ff61b3d822d04901054c22 | import math
import torch
import torch.nn as nn
import torch.utils.data
def uniform(size, tensor):
stdv = 1.0 / math.sqrt(size)
if tensor is not None:
tensor.data.uniform_(-stdv, stdv)
class Model(nn.Module):
def __init__(self, hidden_dim):
super().__init__()
self.weight = nn.Par... |
STFullyConnected | # 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 time
import torch
import numpy as np
from torch import nn
from torch import optim
from torch.nn import functional as F
class Base(nn.Module):
""" This class is the base structure for all of classification/regression DNN models.
Mainly, it provides the general methods for training, evaluating model and ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | cthoyt/DrugEx | STFullyConnected | false | 1,826 | [
"MIT"
] | 0 | 9e4d31adb2c65d0afc852948f502c79dcf8308a3 | https://github.com/cthoyt/DrugEx/tree/9e4d31adb2c65d0afc852948f502c79dcf8308a3 | import time
import torch
import numpy as np
from torch import nn
from torch import optim
from torch.nn import functional as F
class Base(nn.Module):
""" This class is the base structure for all of classification/regression DNN models.
Mainly, it provides the general methods for training, evaluating model and ... |
SoftDetectionModule | # 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.functional as F
import torch.nn as nn
class SoftDetectionModule(nn.Module):
def __init__(self, soft_local_max_size=3):
super(SoftDetectionModule, self).__init__()
self.soft_local_max_size = soft_local_max_size
self.pad = self.soft_local_max_size // 2
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 math as tl_math
import torch.nn as nn
... | deep-learning-20/d2-net | SoftDetectionModule | false | 1,827 | [
"BSD-3-Clause-Clear"
] | 0 | b092186353af23e9247c7f56ac2de3396b8c5a00 | https://github.com/deep-learning-20/d2-net/tree/b092186353af23e9247c7f56ac2de3396b8c5a00 | import torch
import torch.nn.functional as F
import torch.nn as nn
class Model(nn.Module):
def __init__(self, soft_local_max_size=3):
super().__init__()
self.soft_local_max_size = soft_local_max_size
self.pad = self.soft_local_max_size // 2
def forward(self, batch):
b = batch... |
AdaptiveAvgPool3dOutSize1 | # 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
from abc import abstractmethod
from typing import Tuple
import torch.nn
class EfficientBlockBase(nn.Module):
"""
PyTorchVideo/accelerator provides a set of efficient blocks
that have optimal efficiency for each target hardware device.
Each ef... | 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
from abc import abstractmethod
from typing import Tuple
import torch.nn
assert_size_stride = t... | denred0/pytorchvideo | AdaptiveAvgPool3dOutSize1 | false | 1,828 | [
"Apache-2.0"
] | 0 | d874bfc9969895d2afcedea2e12bae5e1bcfb809 | https://github.com/denred0/pytorchvideo/tree/d874bfc9969895d2afcedea2e12bae5e1bcfb809 | import torch
import torch.nn as nn
import torch.utils.data
from abc import abstractmethod
from typing import Tuple
import torch.nn
class EfficientBlockBase(nn.Module):
"""
PyTorchVideo/accelerator provides a set of efficient blocks
that have optimal efficiency for each target hardware device.
Each ef... |
SELU | # 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
from torch.autograd import Variable
import torch.nn.functional as F
def where(condition, if_true, if_false):
"""
Torch equivalent of numpy.where.
Parameters
----------
condition : torch.ByteTensor or torch.cuda.ByteTensor or torch.autograd.Variable
Condi... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.funct... | dearkafka/inferno | SELU | false | 1,829 | [
"Apache-2.0"
] | 0 | e9e3b863fd1fc97cf94d08ac6b4f8df7665f996a | https://github.com/dearkafka/inferno/tree/e9e3b863fd1fc97cf94d08ac6b4f8df7665f996a | import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
def where(condition, if_true, if_false):
"""
Torch equivalent of numpy.where.
Parameters
----------
condition : torch.ByteTensor or torch.cuda.ByteTensor or torch.autograd.Variable
Condi... |
BatchDHCN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils
import torch.utils.data
import torch.optim
class BatchDHCN(nn.Module):
"""docstring for BatchDHCN"""
def __init__(self, embed_size=512, output_size=512, num_channel=2,
conv_size=3, batch_norm=True):
super(Ba... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | deeplearning2020/self | BatchDHCN | false | 1,830 | [
"MIT"
] | 0 | cf0e6f9acdcfe17906c6327042d25ac9c8894885 | https://github.com/deeplearning2020/self/tree/cf0e6f9acdcfe17906c6327042d25ac9c8894885 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils
import torch.utils.data
import torch.optim
class Model(nn.Module):
"""docstring for BatchDHCN"""
def __init__(self, embed_size=512, output_size=512, num_channel=2,
conv_size=3, batch_norm=True):
super().__in... |
DenseSAGEConv | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
import torch.nn.functional as F
import torch.utils.data
from torch.nn import Parameter
def uniform(size, tensor):
stdv = 1.0 / math.sqrt(size)
if tensor is not None:
tensor.data.uniform_(-stdv, stdv)
class DenseSAGEConv(torch.nn.Module):
def __init__(self, in_channels, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | dendisuhubdy/pytorch_geometric | DenseSAGEConv | false | 1,831 | [
"MIT"
] | 0 | a0592f61aef617c0c8ff61b3d822d04901054c22 | https://github.com/dendisuhubdy/pytorch_geometric/tree/a0592f61aef617c0c8ff61b3d822d04901054c22 | import math
import torch
import torch.nn.functional as F
import torch.utils.data
from torch.nn import Parameter
def uniform(size, tensor):
stdv = 1.0 / math.sqrt(size)
if tensor is not None:
tensor.data.uniform_(-stdv, stdv)
class Model(torch.nn.Module):
def __init__(self, in_channels, out_chan... |
MaskedMSE | # 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 MaskedMSE(nn.Module):
def __init__(self):
super(MaskedMSE, self).__init__()
self.criterion = nn.MSELoss()
def forward(self, input, target, gamma=2.0):
mask = gamma * target / (target + 1e-07)
self.loss = self.criterion(input * mask, ta... | 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... | dhruvramani/AccentTransfer | MaskedMSE | false | 1,832 | [
"MIT"
] | 0 | 63a35b4aa37bc41c1f66dfb4bae76e2924183d7c | https://github.com/dhruvramani/AccentTransfer/tree/63a35b4aa37bc41c1f66dfb4bae76e2924183d7c | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
self.criterion = nn.MSELoss()
def forward(self, input, target, gamma=2.0):
mask = gamma * target / (target + 1e-07)
self.loss = self.criterion(input * mask, target * mask)
... |
adaILN | # 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 torch.nn.parameter import Parameter
class adaILN(nn.Module):
def __init__(self, num_features, eps=1e-05):
super(adaILN, self).__init__()
self.eps = eps
self.rho = Parameter(torch.Tensor(1, num_features, 1, 1))
self.rho.data.fill_(0.9)
d... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
from torch.nn.parameter import Parameter
assert_size_stri... | denny3388/Conditional-UGATIT | adaILN | false | 1,833 | [
"MIT"
] | 0 | 86ad35f05aaa105a814dec031d37370f44b71d5b | https://github.com/denny3388/Conditional-UGATIT/tree/86ad35f05aaa105a814dec031d37370f44b71d5b | import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
class Model(nn.Module):
def __init__(self, num_features, eps=1e-05):
super().__init__()
self.eps = eps
self.rho = Parameter(torch.Tensor(1, num_features, 1, 1))
self.rho.data.fill_(0.9)
def forward(se... |
MaskedTemporalPooling | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.utils.data
from typing import Optional
import torch.nn
class MaskedTemporalPooling(torch.nn.Module):
"""
Applies temporal pooling operations on masked inputs. For each pooling operation
all masked values are ignored.
"""
def __init__(self, method: 'str'):
"""
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
import torch.nn
assert_size_stride = torch._C._dynamo.guards.asse... | denred0/pytorchvideo | MaskedTemporalPooling | false | 1,834 | [
"Apache-2.0"
] | 0 | d874bfc9969895d2afcedea2e12bae5e1bcfb809 | https://github.com/denred0/pytorchvideo/tree/d874bfc9969895d2afcedea2e12bae5e1bcfb809 | import torch
import torch.utils.data
from typing import Optional
import torch.nn
class Model(torch.nn.Module):
"""
Applies temporal pooling operations on masked inputs. For each pooling operation
all masked values are ignored.
"""
def __init__(self, method: 'str'):
"""
method (str... |
TemporalConvNet | # 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 torch.nn.utils import weight_norm
class Chomp1d(nn.Module):
def __init__(self, chomp_size):
super(Chomp1d, self).__init__()
self.chomp_size = chomp_size
def forward(self, x, pad_right=True):
return x[:, :, :-self.chomp_size].contiguous() if pad... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | ddcas/singing-language-identification | TemporalConvNet | false | 1,835 | [
"MIT"
] | 0 | d104419b196d56d4de37cff47c32e88e28c58690 | https://github.com/ddcas/singing-language-identification/tree/d104419b196d56d4de37cff47c32e88e28c58690 | import torch
import torch.nn as nn
from torch.nn.utils import weight_norm
class Chomp1d(nn.Module):
def __init__(self, chomp_size):
super().__init__()
self.chomp_size = chomp_size
def forward(self, x, pad_right=True):
return x[:, :, :-self.chomp_size].contiguous() if pad_right else x... |
NaiveTorchNet | # 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.autograd import Variable
import torch.nn as nn
import torch.autograd
import torch.optim as optim
class NaiveTorchNet(nn.Module):
"""A reimplementation of from-scratch NaiveNet using PyTorch"""
def __init__(self, input_nodes, hidden_nodes, output_nodes, learn_rate=0.1
):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.autograd import Variable
import torch.nn as nn
import torch.autograd
... | deo1/deo1 | NaiveTorchNet | false | 1,836 | [
"MIT"
] | 0 | 36671f12269d3bd662d746e8b9f66c22255c9df7 | https://github.com/deo1/deo1/tree/36671f12269d3bd662d746e8b9f66c22255c9df7 | import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.autograd
import torch.optim as optim
class Model(nn.Module):
"""A reimplementation of from-scratch NaiveNet using PyTorch"""
def __init__(self, input_nodes, hidden_nodes, output_nodes, learn_rate=0.1
):
super(... |
Attn | # 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 Attn(nn.Module):
def __init__(self, hidden_size, batch_size=1, method='dot'):
super(Attn, self).__init__()
self.method = method
self.hidden_size = hidden_size
self.batch_size = batch_size
if self.meth... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | dhpollack/mgc | Attn | false | 1,837 | [
"MIT"
] | 0 | ed1b8fb512f0b42cb8121a2809def65f232dc154 | https://github.com/dhpollack/mgc/tree/ed1b8fb512f0b42cb8121a2809def65f232dc154 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, hidden_size, batch_size=1, method='dot'):
super().__init__()
self.method = method
self.hidden_size = hidden_size
self.batch_size = batch_size
if self.method == 'ge... |
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
import torch.nn as nn
import torch.nn.functional as F
class PositiveLinear(nn.Linear):
"""Applies a transformation to the incoming data of the following form: :math:`y_i = xlog(exp(A)+1)^T`
where log and exp are elementwise operations.
Args:
in_features: size of each inpu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | dfioravanti/copula_vae | PositiveLinear | false | 1,838 | [
"MIT"
] | 0 | 4fdadfb9ca65a75367d50df4a5848942de20741f | https://github.com/dfioravanti/copula_vae/tree/4fdadfb9ca65a75367d50df4a5848942de20741f | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Linear):
"""Applies a transformation to the incoming data of the following form: :math:`y_i = xlog(exp(A)+1)^T`
where log and exp are elementwise operations.
Args:
in_features: size of each input sample
... |
PrimaryCaps | # 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 PrimaryCaps(nn.Module):
"""
输入:(B,C,H,W)=(B,256,20,20)
输出：(B,C_N,C_L)=(B,32*6*6, 8)=(B,1152,8)
C_N:capsule_num，胶囊的个数
C_L:capsule_length，每个胶囊的长度
"""
def __init__(self, capsule_length=8, in_channels=256, out_channels=32,
capsu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | daxiongpro/pytorch-tutorial | PrimaryCaps | false | 1,839 | [
"MIT"
] | 0 | abafc32f7ee1092024085f703e4ced51ce358a1b | https://github.com/daxiongpro/pytorch-tutorial/tree/abafc32f7ee1092024085f703e4ced51ce358a1b | import torch
import torch.nn as nn
class Model(nn.Module):
"""
输入:(B,C,H,W)=(B,256,20,20)
输出：(B,C_N,C_L)=(B,32*6*6, 8)=(B,1152,8)
C_N:capsule_num，胶囊的个数
C_L:capsule_length，每个胶囊的长度
"""
def __init__(self, capsule_length=8, in_channels=256, out_channels=32,
capsule_num... |
LearnMaskedDefault | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.utils.data
import torch.nn
class LearnMaskedDefault(nn.Module):
"""
Learns default values to fill invalid entries within input tensors. The
invalid entries are represented by a mask which is passed into forward alongside
the input tensor. Note the defaul... | 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.nn
assert_size_stride = torch.... | denred0/pytorchvideo | LearnMaskedDefault | false | 1,840 | [
"Apache-2.0"
] | 0 | d874bfc9969895d2afcedea2e12bae5e1bcfb809 | https://github.com/denred0/pytorchvideo/tree/d874bfc9969895d2afcedea2e12bae5e1bcfb809 | import torch
import torch.nn as nn
import torch.utils.data
import torch.nn
class Model(nn.Module):
"""
Learns default values to fill invalid entries within input tensors. The
invalid entries are represented by a mask which is passed into forward alongside
the input tensor. Note the default value is on... |
SynthWide | # 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 SynthWide(nn.Module):
def __init__(self, num_c=10, f=1):
super(SynthWide, self).__init__()
self.pool = nn.MaxPool2d(2, 2)
self.conv1 = nn.Conv2d(3, 32 * f, 3, padding=1)
self.conv2 = nn.Conv2d(32 * f, 64 * f,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | dengliming/iotnets | SynthWide | false | 1,842 | [
"MIT"
] | 0 | db744e56769c799dbf765a27fc5aa91e3edeaaa3 | https://github.com/dengliming/iotnets/tree/db744e56769c799dbf765a27fc5aa91e3edeaaa3 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, num_c=10, f=1):
super().__init__()
self.pool = nn.MaxPool2d(2, 2)
self.conv1 = nn.Conv2d(3, 32 * f, 3, padding=1)
self.conv2 = nn.Conv2d(32 * f, 64 * f, 3, padding=1)
... |
TCN_SLID | # 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 torch.nn.utils import weight_norm
class Chomp1d(nn.Module):
def __init__(self, chomp_size):
super(Chomp1d, self).__init__()
self.chomp_size = chomp_size
def forward(self, x, pad_right=True):
return x[:, :, :-self.chomp_size].contiguous() if pad... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | ddcas/singing-language-identification | TCN_SLID | false | 1,843 | [
"MIT"
] | 0 | d104419b196d56d4de37cff47c32e88e28c58690 | https://github.com/ddcas/singing-language-identification/tree/d104419b196d56d4de37cff47c32e88e28c58690 | import torch
import torch.nn as nn
from torch.nn.utils import weight_norm
class Chomp1d(nn.Module):
def __init__(self, chomp_size):
super().__init__()
self.chomp_size = chomp_size
def forward(self, x, pad_right=True):
return x[:, :, :-self.chomp_size].contiguous() if pad_right else x... |
HearthstoneNet | # 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 HearthstoneNet(nn.Module):
def __init__(self):
super(HearthstoneNet, self).__init__()
self.conv1 = nn.Conv2d(1, 64, kernel_size=(3, 3), padding=1)
self.conv2 = nn.Conv2d(64, 64, kernel_size=(3, 3), padding=1)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | dianarvp/stone_ground_hearth_battles | HearthstoneNet | false | 1,844 | [
"Apache-2.0"
] | 0 | 450e70eaef21b543be579a6d696676fb148a99b0 | https://github.com/dianarvp/stone_ground_hearth_battles/tree/450e70eaef21b543be579a6d696676fb148a99b0 | import torch
from torch import nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(1, 64, kernel_size=(3, 3), padding=1)
self.conv2 = nn.Conv2d(64, 64, kernel_size=(3, 3), padding=1)
self.max_pool = nn.MaxPool2d(... |
AttnBertPooler | # 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 _paritybench_helpers import _mock_config
import math
import torch
from torch import nn
class AttnBertPooler(nn.Module):
def __init__(self, config):
super(AttnBertPooler, self).__init__()
self.dense = nn.Linear(config.hidden_size * 2, config.hidden_size * 2)
self.activation = nn.Tanh(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | AnonymousAuthor2013/PostRec | AttnBertPooler | false | 1,845 | [
"MIT"
] | 0 | a1461f716d177e28b96ca29d1398f96b5717c1e1 | https://github.com/AnonymousAuthor2013/PostRec/tree/a1461f716d177e28b96ca29d1398f96b5717c1e1 | from _paritybench_helpers import _mock_config
import math
import torch
from torch import nn
class Model(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size * 2, config.hidden_size * 2)
self.activation = nn.Tanh()
self.hidden_size = ... |
TestNet | # 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 ScaleLayer(nn.Module):
def __init__(self, init_value=0.001):
super().__init__()
self.scale = nn.Parameter(torch.FloatTensor([init_value]))
def forward(self, input):
return input * self.scale
class TestNet(nn.Module):
def __init__(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... | dizzyvn/torch-tcav | TestNet | false | 1,846 | [
"Apache-2.0"
] | 0 | c9795e817d1104923ef7422f5575607e6b835abc | https://github.com/dizzyvn/torch-tcav/tree/c9795e817d1104923ef7422f5575607e6b835abc | import torch
import torch.nn as nn
class ScaleLayer(nn.Module):
def __init__(self, init_value=0.001):
super().__init__()
self.scale = nn.Parameter(torch.FloatTensor([init_value]))
def forward(self, input):
return input * self.scale
class Model(nn.Module):
def __init__(self):
... |
BertPooler | # 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 _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class BertPooler(nn.Module):
def __init__(self, config):
super(BertPooler, self).__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | Andr3wis2Cool4School/AI-pro | BertPooler | false | 1,847 | [
"MIT"
] | 0 | dfe5f5959bc187d899a86f13b84158c66f64d1cc | https://github.com/Andr3wis2Cool4School/AI-pro/tree/dfe5f5959bc187d899a86f13b84158c66f64d1cc | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hidden_states):
... |
BatchNorm | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import numpy as np
from torch import tensor
import torch.nn as nn
import numpy.random as rng
class BaseFlow(nn.Module):
""" """
def __init__(self, n_inputs, **kwargs):
super(BaseFlow, self).__init__()
self.n_inputs = n_inputs
def forward(self, x, **kwargs):
raise 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._inductor.runtime.triton_helpers import libdevice, math as tl_math
import numpy as np
from torch import tensor
import torch.nn as... | dlvp/madminer | BatchNorm | false | 1,848 | [
"MIT"
] | 0 | 4ae7d9b73452848a6c9d1b81b50ef316ff7a054f | https://github.com/dlvp/madminer/tree/4ae7d9b73452848a6c9d1b81b50ef316ff7a054f | import torch
import numpy as np
from torch import tensor
import torch.nn as nn
import numpy.random as rng
class BaseFlow(nn.Module):
""" """
def __init__(self, n_inputs, **kwargs):
super().__init__()
self.n_inputs = n_inputs
def forward(self, x, **kwargs):
raise NotImplementedErr... |
Critic | # 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 _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.nn.functional as F
class Critic(nn.Module):
def __init__(self, num_inputs, args):
super(Critic, self).__init__()
self.fc1 = nn.Linear(num_inputs, args.hidden_size)
self.fc2 = nn.Linear(args.hidde... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | dlrudco/pg_travel | Critic | false | 1,849 | [
"MIT"
] | 0 | 33733b624894095096af8201f7597c3244d3480d | https://github.com/dlrudco/pg_travel/tree/33733b624894095096af8201f7597c3244d3480d | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, num_inputs, args):
super().__init__()
self.fc1 = nn.Linear(num_inputs, args.hidden_size)
self.fc2 = nn.Linear(args.hidden_size, args.... |
MetapathAggrLayer | # 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
from torch import nn
class MetapathAggrLayer(nn.Module):
"""
metapath attention layer.
"""
def __init__(self, in_features, nmeta, dropout, alpha):
super(MetapathAggrLayer, self).__init__()
self.dropout = dropout
self.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.... | dingdanhao110/HINGCN | MetapathAggrLayer | false | 1,850 | [
"MIT"
] | 0 | 281b73c03bd3b00e35bce4c5e1c27076233555e4 | https://github.com/dingdanhao110/HINGCN/tree/281b73c03bd3b00e35bce4c5e1c27076233555e4 | import torch
from torch.nn import functional as F
from torch import nn
class Model(nn.Module):
"""
metapath attention layer.
"""
def __init__(self, in_features, nmeta, dropout, alpha):
super().__init__()
self.dropout = dropout
self.in_features = in_features
self.alpha ... |
SoftDiceLossSquared | # 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._C
import torch.serialization
class SoftDiceLossSquared(nn.Module):
def __init__(self, apply_nonlin=None, batch_dice=False, do_bg=True,
smooth=1.0):
"""
squares the terms in the denominator as proposed by Milletari et al.
"""
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch._C
import torch.serialization
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strid... | dkswxd/Swin-Transformer-Semantic-Segmentation | SoftDiceLossSquared | false | 1,851 | [
"Apache-2.0"
] | 0 | 6af19736e5492a01d8952d4ee86a6d59b21c2ae1 | https://github.com/dkswxd/Swin-Transformer-Semantic-Segmentation/tree/6af19736e5492a01d8952d4ee86a6d59b21c2ae1 | import torch
import torch.nn as nn
import torch._C
import torch.serialization
class Model(nn.Module):
def __init__(self, apply_nonlin=None, batch_dice=False, do_bg=True,
smooth=1.0):
"""
squares the terms in the denominator as proposed by Milletari et al.
"""
super().__ini... |
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