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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
LayerThreshold | # 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 random
import torch
import torch.nn as nn
class LayerThreshold(nn.Module):
"""
Test for nn.layers based types
"""
def __init__(self):
super(LayerThreshold, self).__init__()
self.threshold = random.random()
self.value = self.threshold + random.random()
self.thres... | 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 random
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.gu... | dawnclaude/onnx2keras | LayerThreshold | false | 15,149 | [
"MIT"
] | 115 | 3d2a47c0a228b91fd434232274e216e491da36e3 | https://github.com/dawnclaude/onnx2keras/tree/3d2a47c0a228b91fd434232274e216e491da36e3 | import random
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Test for nn.layers based types
"""
def __init__(self):
super().__init__()
self.threshold = random.random()
self.value = self.threshold + random.random()
self.thresh = nn.Threshold(self.thresho... |
LayerReLU6 | # 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 LayerReLU6(nn.Module):
"""
Test for nn.layers based types
"""
def __init__(self):
super(LayerReLU6, self).__init__()
self.relu = nn.ReLU6()
def forward(self, x):
x = self.relu(x)
return x
def get_inputs():
return [tor... | 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... | dawnclaude/onnx2keras | LayerReLU6 | false | 15,150 | [
"MIT"
] | 115 | 3d2a47c0a228b91fd434232274e216e491da36e3 | https://github.com/dawnclaude/onnx2keras/tree/3d2a47c0a228b91fd434232274e216e491da36e3 | import torch
import torch.nn as nn
class Model(nn.Module):
"""
Test for nn.layers based types
"""
def __init__(self):
super().__init__()
self.relu = nn.ReLU6()
def forward(self, x):
x = self.relu(x)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])... |
LayerHardtanh | # 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 random
import torch
import torch.nn as nn
class LayerHardtanh(nn.Module):
"""
Test for nn.layers based types
"""
def __init__(self):
super(LayerHardtanh, self).__init__()
self.min_val = random.random()
self.max_val = self.min_val + random.random()
self.htanh = n... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import random
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_s... | dawnclaude/onnx2keras | LayerHardtanh | false | 15,151 | [
"MIT"
] | 115 | 3d2a47c0a228b91fd434232274e216e491da36e3 | https://github.com/dawnclaude/onnx2keras/tree/3d2a47c0a228b91fd434232274e216e491da36e3 | import random
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Test for nn.layers based types
"""
def __init__(self):
super().__init__()
self.min_val = random.random()
self.max_val = self.min_val + random.random()
self.htanh = nn.Hardtanh(min_val=self.min... |
ffnn | # 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.dataloader
import torch.nn
def get_shape(t):
return list(t.shape)
class ffnn(nn.Module):
def __init__(self, emb_size, num_layers, hidden_size, output_size,
dropout, output_weights_initializer=None):
super(ffnn, self).__init__()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data.dataloader
import torch.nn
assert_... | db-bionlp/CLNER | ffnn | false | 15,152 | [
"MIT"
] | 46 | 77910311acf0411252b9fea8c3e6efb7175eb21f | https://github.com/db-bionlp/CLNER/tree/77910311acf0411252b9fea8c3e6efb7175eb21f | import torch
import torch.nn as nn
import torch.utils.data.dataloader
import torch.nn
def get_shape(t):
return list(t.shape)
class Model(nn.Module):
def __init__(self, emb_size, num_layers, hidden_size, output_size,
dropout, output_weights_initializer=None):
super().__init__()
self.... |
LayerELU | # 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 random
import torch
import torch.nn as nn
class LayerELU(nn.Module):
"""
Test for nn.layers based types
"""
def __init__(self):
super(LayerELU, self).__init__()
self.alpha = random.random()
self.elu = nn.ELU(alpha=self.alpha)
def forward(self, x):
x = self.... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import random
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guard... | dawnclaude/onnx2keras | LayerELU | false | 15,153 | [
"MIT"
] | 115 | 3d2a47c0a228b91fd434232274e216e491da36e3 | https://github.com/dawnclaude/onnx2keras/tree/3d2a47c0a228b91fd434232274e216e491da36e3 | import random
import torch
import torch.nn as nn
class Model(nn.Module):
"""
Test for nn.layers based types
"""
def __init__(self):
super().__init__()
self.alpha = random.random()
self.elu = nn.ELU(alpha=self.alpha)
def forward(self, x):
x = self.elu(x)
re... |
VoxelFeatureExtractor | # 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 VoxelFeatureExtractor(nn.Module):
"""Computes mean of non-zero points within voxel."""
def forward(self, feature, occupancy):
"""
:feature FloatTensor of shape (N, K, C)
:return FloatTensor of shape (N, C)
"""
denominator = occup... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | dd-iuonac/vision3d | VoxelFeatureExtractor | false | 15,154 | [
"MIT"
] | 131 | 9ea514c80eb99d265c3247321e59bfc1c2ccd94a | https://github.com/dd-iuonac/vision3d/tree/9ea514c80eb99d265c3247321e59bfc1c2ccd94a | import torch
from torch import nn
class Model(nn.Module):
"""Computes mean of non-zero points within voxel."""
def forward(self, feature, occupancy):
"""
:feature FloatTensor of shape (N, K, C)
:return FloatTensor of shape (N, C)
"""
denominator = occupancy.type_as(fea... |
ScalarMix | # 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.dataloader
import torch.nn
class ScalarMix(nn.Module):
def __init__(self, n_layers, dropout=0):
super(ScalarMix, self).__init__()
self.n_layers = n_layers
self.dropout = dropout
self.weights = nn.Parameter(torch.zeros(n_la... | 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
import torch.utils.data.dataloader
import torch.nn
... | db-bionlp/CLNER | ScalarMix | false | 15,155 | [
"MIT"
] | 46 | 77910311acf0411252b9fea8c3e6efb7175eb21f | https://github.com/db-bionlp/CLNER/tree/77910311acf0411252b9fea8c3e6efb7175eb21f | import torch
import torch.nn as nn
import torch.utils.data.dataloader
import torch.nn
class Model(nn.Module):
def __init__(self, n_layers, dropout=0):
super().__init__()
self.n_layers = n_layers
self.dropout = dropout
self.weights = nn.Parameter(torch.zeros(n_layers))
self... |
EmissionModel | # 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
class EmissionModel(torch.nn.Module):
"""
- forward(): computes the log probability of an observation.
- sample(): given a state, sample an observation for that state.
"""
def __init__(self, N, M):
super(EmissionModel, self).__init__()
self.N = N
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.utils.dat... | dendisuhubdy/pytorch_HMM | EmissionModel | false | 15,156 | [
"Apache-2.0"
] | 88 | 3235326027328e1b0377b17f9dad8fcc56a3668c | https://github.com/dendisuhubdy/pytorch_HMM/tree/3235326027328e1b0377b17f9dad8fcc56a3668c | import torch
import torch.utils.data
class Model(torch.nn.Module):
"""
- forward(): computes the log probability of an observation.
- sample(): given a state, sample an observation for that state.
"""
def __init__(self, N, M):
super().__init__()
self.N = N
self.M = M
self.u... |
BiaffineAttention | # 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.dataloader
from torch.nn import Parameter
from torch.nn.parameter import Parameter
import torch.nn
class BiaffineAttention(nn.Module):
"""
Adopted from NeuroNLP2:
https://github.com/XuezheMax/NeuroNLP2/blob/master/neuronlp2/nn/modules/attentio... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | db-bionlp/CLNER | BiaffineAttention | false | 15,157 | [
"MIT"
] | 46 | 77910311acf0411252b9fea8c3e6efb7175eb21f | https://github.com/db-bionlp/CLNER/tree/77910311acf0411252b9fea8c3e6efb7175eb21f | import torch
import torch.nn as nn
import torch.utils.data.dataloader
from torch.nn import Parameter
from torch.nn.parameter import Parameter
import torch.nn
class Model(nn.Module):
"""
Adopted from NeuroNLP2:
https://github.com/XuezheMax/NeuroNLP2/blob/master/neuronlp2/nn/modules/attention.py
Bi... |
HDRLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
class HDRLoss(nn.Module):
"""High dynamic range loss."""
def __init__(self, eps=0.01):
"""Initializes loss with numerical stability epsilon."""
super(HDRLoss, self).__init__()
self._eps = eps
def forward(self, denoised, target):
"""Compu... | 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... | delldu/Noise2Noise | HDRLoss | false | 15,158 | [
"MIT"
] | 224 | f519f208776a60efadac208c109c9b7f432504b5 | https://github.com/delldu/Noise2Noise/tree/f519f208776a60efadac208c109c9b7f432504b5 | import torch
import torch.nn as nn
class Model(nn.Module):
"""High dynamic range loss."""
def __init__(self, eps=0.01):
"""Initializes loss with numerical stability epsilon."""
super().__init__()
self._eps = eps
def forward(self, denoised, target):
"""Computes loss by unp... |
Conv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
from torch.distributions import transforms as transform
class Flow(transform.Transform, nn.Module):
"""
Main class for a single flow.
"""
def __init__(self, amortized='none'):
""" Initialize as both transform and module """
transform.Transform.__init... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.distributions import transforms as transform
as... | dendisuhubdy/flow_synthesizer | Conv2d | false | 15,159 | [
"MIT"
] | 93 | 1561e8ce2520258acb3d228beebbb626a8abc04f | https://github.com/dendisuhubdy/flow_synthesizer/tree/1561e8ce2520258acb3d228beebbb626a8abc04f | import torch
import torch.nn as nn
from torch.distributions import transforms as transform
class Flow(transform.Transform, nn.Module):
"""
Main class for a single flow.
"""
def __init__(self, amortized='none'):
""" Initialize as both transform and module """
transform.Transform.__init... |
cnn_layer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.utils.data.dataloader
import torch.nn
class cnn_layer(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, bias=True):
super(cnn_layer, self).__init__()
self.conv = torch.nn.Conv1d(in_channels=in_channe... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | db-bionlp/CLNER | cnn_layer | false | 15,160 | [
"MIT"
] | 46 | 77910311acf0411252b9fea8c3e6efb7175eb21f | https://github.com/db-bionlp/CLNER/tree/77910311acf0411252b9fea8c3e6efb7175eb21f | import torch
import torch.nn as nn
import torch.utils.data.dataloader
import torch.nn
class Model(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, bias=True):
super().__init__()
self.conv = torch.nn.Conv1d(in_channels=in_channels, out_channels=
... |
bilinear_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.nn as nn
import torch.utils.data.dataloader
import torch.nn
class Sparse_dropout(nn.Module):
def __init__(self, p):
super(Sparse_dropout, self).__init__()
self.dropout_rate = p
def forward(self, input, noise_shape):
if not self.training:
return i... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data.dataloader
import torch.nn
assert_... | db-bionlp/CLNER | bilinear_classifier | false | 15,161 | [
"MIT"
] | 46 | 77910311acf0411252b9fea8c3e6efb7175eb21f | https://github.com/db-bionlp/CLNER/tree/77910311acf0411252b9fea8c3e6efb7175eb21f | import torch
import torch.nn as nn
import torch.utils.data.dataloader
import torch.nn
class Sparse_dropout(nn.Module):
def __init__(self, p):
super().__init__()
self.dropout_rate = p
def forward(self, input, noise_shape):
if not self.training:
return input
shapes ... |
LSID | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
import torch.nn as nn
def pixel_shuffle(input, upscale_factor, depth_first=False):
"""Rearranges elements in a tensor of shape :math:`[*, C*r^2, H, W]` to a
tensor of shape :math:`[C, H*r, W*r]`.
See :class:`~torch.nn.PixelShuffle` for details.
Args:
input (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
from torch._inductor.runtime import triton_helpers
import math
import torch.nn a... | cydonia999/Learning_to_See_in_the_Dark_PyTorch | LSID | false | 15,162 | [
"MIT"
] | 77 | 470a6a8e9c6367d8fa88ee6d1dea211dd9fb1f81 | https://github.com/cydonia999/Learning_to_See_in_the_Dark_PyTorch/tree/470a6a8e9c6367d8fa88ee6d1dea211dd9fb1f81 | import math
import torch
import torch.nn as nn
def pixel_shuffle(input, upscale_factor, depth_first=False):
"""Rearranges elements in a tensor of shape :math:`[*, C*r^2, H, W]` to a
tensor of shape :math:`[C, H*r, W*r]`.
See :class:`~torch.nn.PixelShuffle` for details.
Args:
input (Tensor): ... |
HexaLinearScore | # 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.dataloader
import torch.nn
class HexaLinearScore(nn.Module):
"""
Outer product version of hexalinear function for sequence labeling.
"""
def __init__(self, wemb_size, tagset_size, temb_size=20, rank=396, std=
0.1545, norma... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.nn as nn
import torch.utils.data.dataloader
import torc... | db-bionlp/CLNER | HexaLinearScore | false | 15,163 | [
"MIT"
] | 46 | 77910311acf0411252b9fea8c3e6efb7175eb21f | https://github.com/db-bionlp/CLNER/tree/77910311acf0411252b9fea8c3e6efb7175eb21f | import math
import torch
import torch.nn as nn
import torch.utils.data.dataloader
import torch.nn
class Model(nn.Module):
"""
Outer product version of hexalinear function for sequence labeling.
"""
def __init__(self, wemb_size, tagset_size, temb_size=20, rank=396, std=
0.1545, normalization=T... |
GraphAttentionLayer | # 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
from torch.autograd import Variable
import torch.nn.functional as F
class GraphAttentionLayer(nn.Module):
def __init__(self, requires_grad=True):
super(GraphAttentionLayer, self).__init__()
if requires_grad:
s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | dawnranger/pytorch-AGNN | GraphAttentionLayer | false | 15,164 | [
"MIT"
] | 137 | 461f71b45e5eaddb50cff31a537b06cb1a50ba8f | https://github.com/dawnranger/pytorch-AGNN/tree/461f71b45e5eaddb50cff31a537b06cb1a50ba8f | import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
from torch.autograd import Variable
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, requires_grad=True):
super().__init__()
if requires_grad:
self.beta = Parameter(torch.Tensor(1).un... |
QuadriLinearScore | # 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.dataloader
import torch.nn
class QuadriLinearScore(nn.Module):
"""
Outer product version of quadrilinear function for sequence labeling.
"""
def __init__(self, wemb_size, tagset_size, temb_size=20, rank=396, std=
0.1545, w... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.nn as nn
import torch.utils.data.dataloader
import torc... | db-bionlp/CLNER | QuadriLinearScore | false | 15,165 | [
"MIT"
] | 46 | 77910311acf0411252b9fea8c3e6efb7175eb21f | https://github.com/db-bionlp/CLNER/tree/77910311acf0411252b9fea8c3e6efb7175eb21f | import math
import torch
import torch.nn as nn
import torch.utils.data.dataloader
import torch.nn
class Model(nn.Module):
"""
Outer product version of quadrilinear function for sequence labeling.
"""
def __init__(self, wemb_size, tagset_size, temb_size=20, rank=396, std=
0.1545, window_size=1... |
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):
"""
:param q: queries, B x N_HEADS x seq_len x d_k
:param k: keys, same dim as q
:param v: values, same dim as q
:param d_k: d_model/n_heads = 128/8 = 16
:param ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | davide-belli/generative-graph-transformer | DecoderLayer | false | 15,166 | [
"MIT"
] | 51 | 949aacf57246e8c28df7dfa38e5c59bf8b2b0ee8 | https://github.com/davide-belli/generative-graph-transformer/tree/949aacf57246e8c28df7dfa38e5c59bf8b2b0ee8 | 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):
"""
:param q: queries, B x N_HEADS x seq_len x d_k
:param k: keys, same dim as q
:param v: values, same dim as q
:param d_k: d_model/n_heads = 128/8 = 16
:param ... |
LayerNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
import torch as th
import torch.nn as nn
from torch.nn import Parameter
class LayerNorm(nn.Module):
"""
Layer Normalization based on Ba & al.:
'Layer Normalization'
https://arxiv.org/pdf/1607.06450.pdf
"""
def __init__(self, input_size: 'int', learnable: 'bool'=True, ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import torch as th
import torch.nn as nn
from torch.nn import Param... | denizetkar/lstms.pth | LayerNorm | false | 15,167 | [
"Apache-2.0"
] | 130 | c1d6af1e106e17c51604ae8acdb5114828adff19 | https://github.com/denizetkar/lstms.pth/tree/c1d6af1e106e17c51604ae8acdb5114828adff19 | import math
import torch
import torch as th
import torch.nn as nn
from torch.nn import Parameter
class Model(nn.Module):
"""
Layer Normalization based on Ba & al.:
'Layer Normalization'
https://arxiv.org/pdf/1607.06450.pdf
"""
def __init__(self, input_size: 'int', learnable: 'bool'=True, epsi... |
BaLayerNorm | # 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 as th
import torch.nn as nn
from torch.nn import Parameter
class BaLayerNorm(nn.Module):
"""
Layer Normalization based on Ba & al.:
'Layer Normalization'
https://arxiv.org/pdf/1607.06450.pdf
This implementation mimicks the original torch implementation at:
https://gi... | 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 as th
import torch.nn as nn
from torch.nn import Parameter
assert_... | denizetkar/lstms.pth | BaLayerNorm | false | 15,168 | [
"Apache-2.0"
] | 130 | c1d6af1e106e17c51604ae8acdb5114828adff19 | https://github.com/denizetkar/lstms.pth/tree/c1d6af1e106e17c51604ae8acdb5114828adff19 | import torch
import torch as th
import torch.nn as nn
from torch.nn import Parameter
class Model(nn.Module):
"""
Layer Normalization based on Ba & al.:
'Layer Normalization'
https://arxiv.org/pdf/1607.06450.pdf
This implementation mimicks the original torch implementation at:
https://github.c... |
GatedConv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class GatedConv2d(nn.Module):
def __init__(self, in_c, out_c, kernel, stride, pad, dilation=1, act=
torch.relu):
super(GatedConv2d, self).__init__()
self.activation = act
self.sigmoid = nn.Sigmoid()
self.h = nn.Conv2d(in_c, out_c, kernel,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | dendisuhubdy/flow_synthesizer | GatedConv2d | false | 15,169 | [
"MIT"
] | 93 | 1561e8ce2520258acb3d228beebbb626a8abc04f | https://github.com/dendisuhubdy/flow_synthesizer/tree/1561e8ce2520258acb3d228beebbb626a8abc04f | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, in_c, out_c, kernel, stride, pad, dilation=1, act=
torch.relu):
super().__init__()
self.activation = act
self.sigmoid = nn.Sigmoid()
self.h = nn.Conv2d(in_c, out_c, kernel, stride, pad, dilation)... |
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
from torch import nn
def Tstdeps(val):
return torch.sqrt(((val - val.mean()) ** 2).mean() + 1e-08)
class MinibatchStddev(nn.Module):
def __init__(self):
super(MinibatchStddev, self).__init__()
self.eps = 1.0
def forward(self, x):
stddev_mean = Tstdeps(x)
ne... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_... | deepsound-project/pggan-pytorch | MinibatchStddev | false | 15,170 | [
"MIT"
] | 115 | dab2ec79229c3800253a209304dbb1e7ac1d1219 | https://github.com/deepsound-project/pggan-pytorch/tree/dab2ec79229c3800253a209304dbb1e7ac1d1219 | import torch
from torch import nn
def Tstdeps(val):
return torch.sqrt(((val - val.mean()) ** 2).mean() + 1e-08)
class Model(nn.Module):
def __init__(self):
super().__init__()
self.eps = 1.0
def forward(self, x):
stddev_mean = Tstdeps(x)
new_channel = stddev_mean.expand(... |
ChanNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
class ChanNorm(nn.Module):
def __init__(self, dim, eps=1e-05):
super().__init__()
self.eps = eps
self.g = nn.Parameter(torch.ones(1, dim, 1, 1))
self.b = nn.Parameter(torch.zeros(1, dim, 1, 1))
def forward(self, x):
std = torch.var(x,... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | destefani/lightweight-gan | ChanNorm | false | 15,171 | [
"MIT"
] | 1,187 | 5ba61c21c8c9c8d4574a4a3ddd4759f86debf9bf | https://github.com/destefani/lightweight-gan/tree/5ba61c21c8c9c8d4574a4a3ddd4759f86debf9bf | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, dim, eps=1e-05):
super().__init__()
self.eps = eps
self.g = nn.Parameter(torch.ones(1, dim, 1, 1))
self.b = nn.Parameter(torch.zeros(1, dim, 1, 1))
def forward(self, x):
std = torch.var(x, di... |
GatedDense | # 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 GatedDense(nn.Module):
def __init__(self, input_size, output_size, activation=torch.relu):
super(GatedDense, self).__init__()
self.activation = activation
self.sigmoid = nn.Sigmoid()
self.h = nn.Linear(input_size, output_size)
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_... | dendisuhubdy/flow_synthesizer | GatedDense | false | 15,172 | [
"MIT"
] | 93 | 1561e8ce2520258acb3d228beebbb626a8abc04f | https://github.com/dendisuhubdy/flow_synthesizer/tree/1561e8ce2520258acb3d228beebbb626a8abc04f | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, input_size, output_size, activation=torch.relu):
super().__init__()
self.activation = activation
self.sigmoid = nn.Sigmoid()
self.h = nn.Linear(input_size, output_size)
self.g = nn.Linear(input_s... |
LinearBlock | # 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 functools import partial
import torch.nn as nn
def dispatcher(dispatch_fn):
def decorated(key, *args):
if callable(key):
return key
if key is None:
key = 'none'
return dispatch_fn(key, *args)
return decorated
@dispatcher
def activ_dispatch(a... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from functools import partial... | derwind/dmfont | LinearBlock | false | 15,173 | [
"MIT"
] | 95 | 17a91a9cc1917d2485eaa8e92b68245578920c76 | https://github.com/derwind/dmfont/tree/17a91a9cc1917d2485eaa8e92b68245578920c76 | import torch
from functools import partial
import torch.nn as nn
def dispatcher(dispatch_fn):
def decorated(key, *args):
if callable(key):
return key
if key is None:
key = 'none'
return dispatch_fn(key, *args)
return decorated
@dispatcher
def activ_dispatch(a... |
PopulationColourRGBTransforms | # 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 numpy as np
class PopulationColourRGBTransforms(torch.nn.Module):
"""RGB color transforms and ordering of patches."""
def __init__(self, config, device, num_patches=1, pop_size=1,
requires_grad=True):
super(PopulationColourRGBT... | 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 numpy as np
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_... | deepmind/arnheim | PopulationColourRGBTransforms | false | 15,174 | [
"Apache-2.0"
] | 186 | cc9d2dd12391faa460b58bff1cc5be82145a5965 | https://github.com/deepmind/arnheim/tree/cc9d2dd12391faa460b58bff1cc5be82145a5965 | from _paritybench_helpers import _mock_config
import torch
import numpy as np
class Model(torch.nn.Module):
"""RGB color transforms and ordering of patches."""
def __init__(self, config, device, num_patches=1, pop_size=1,
requires_grad=True):
super().__init__()
self.config = config
... |
ConvBlock | # 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 functools import partial
import torch.nn as nn
def dispatcher(dispatch_fn):
def decorated(key, *args):
if callable(key):
return key
if key is None:
key = 'none'
return dispatch_fn(key, *args)
return decorated
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 functools import partial... | derwind/dmfont | ConvBlock | false | 15,175 | [
"MIT"
] | 95 | 17a91a9cc1917d2485eaa8e92b68245578920c76 | https://github.com/derwind/dmfont/tree/17a91a9cc1917d2485eaa8e92b68245578920c76 | import torch
import torch.nn.functional as F
from functools import partial
import torch.nn as nn
def dispatcher(dispatch_fn):
def decorated(key, *args):
if callable(key):
return key
if key is None:
key = 'none'
return dispatch_fn(key, *args)
return decorated
... |
Attention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn.functional as F
import torch.nn as nn
def dispatcher(dispatch_fn):
def decorated(key, *args):
if callable(key):
return key
if key is None:
key = 'none'
return dispatch_fn(key, *args)
return decorated
def spectral_norm(module):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | derwind/dmfont | Attention | false | 15,176 | [
"MIT"
] | 95 | 17a91a9cc1917d2485eaa8e92b68245578920c76 | https://github.com/derwind/dmfont/tree/17a91a9cc1917d2485eaa8e92b68245578920c76 | import torch
import torch.nn.functional as F
import torch.nn as nn
def dispatcher(dispatch_fn):
def decorated(key, *args):
if callable(key):
return key
if key is None:
key = 'none'
return dispatch_fn(key, *args)
return decorated
def spectral_norm(module):
... |
EncoderImageWeightNormPrecomp | # 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 collections import OrderedDict
import torch.nn as nn
import torch.nn.init
from torch.nn.utils.weight_norm import weight_norm
def l2norm(X, dim, eps=1e-08):
"""L2-normalize columns of X
"""
norm = torch.pow(X, 2).sum(dim=dim, keepdim=True).sqrt() + eps
X = torch.div(X, norm)
retur... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 collections im... | devilslot/SCAN | EncoderImageWeightNormPrecomp | false | 15,177 | [
"Apache-2.0"
] | 428 | 01812aa98e2ebe39695c8906589b6fe66b2a0d6e | https://github.com/devilslot/SCAN/tree/01812aa98e2ebe39695c8906589b6fe66b2a0d6e | import torch
from collections import OrderedDict
import torch.nn as nn
import torch.nn.init
from torch.nn.utils.weight_norm import weight_norm
def l2norm(X, dim, eps=1e-08):
"""L2-normalize columns of X
"""
norm = torch.pow(X, 2).sum(dim=dim, keepdim=True).sqrt() + eps
X = torch.div(X, norm)
retur... |
CopyChannels | # 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 CopyChannels(torch.nn.Module):
def __init__(self, multiple=3, dim=1):
super(CopyChannels, self).__init__()
self.multiple = multiple
self.dim = dim
def forward(self, x):
return torch.cat([x for _ in range(self.multiple)], dim=self.dim)
def get_inputs():
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret... | dianjixz/AutoDL | CopyChannels | false | 15,178 | [
"Apache-2.0"
] | 1,044 | 48db4eb04d55ce69e93d4a3bdc24592bdb34a868 | https://github.com/dianjixz/AutoDL/tree/48db4eb04d55ce69e93d4a3bdc24592bdb34a868 | import torch
class Model(torch.nn.Module):
def __init__(self, multiple=3, dim=1):
super().__init__()
self.multiple = multiple
self.dim = dim
def forward(self, x):
return torch.cat([x for _ in range(self.multiple)], dim=self.dim)
def get_inputs():
return [torch.rand([4, ... |
CReLU | # 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 CReLU(nn.Module):
def __init__(self):
super(CReLU, self).__init__()
def forward(self, x):
return torch.cat((F.leaky_relu(x, 0.01, inplace=True), F.leaky_relu
(-x, 0.01, inplace=True)), 1)
def get_inputs():... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | dipikakhullar/ocr | CReLU | false | 15,179 | [
"MIT"
] | 284 | a55e70d82f42803be5ed63f8f59e4fa597fcf8d6 | https://github.com/dipikakhullar/ocr/tree/a55e70d82f42803be5ed63f8f59e4fa597fcf8d6 | import torch
import torch.nn.functional as F
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
return torch.cat((F.leaky_relu(x, 0.01, inplace=True), F.leaky_relu
(-x, 0.01, inplace=True)), 1)
def get_inputs():
return... |
ResBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn.functional as F
from functools import partial
import torch.nn as nn
def dispatcher(dispatch_fn):
def decorated(key, *args):
if callable(key):
return key
if key is None:
key = 'none'
return dispatch_fn(key, *args)
return decorated
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.functional as... | derwind/dmfont | ResBlock | false | 15,180 | [
"MIT"
] | 95 | 17a91a9cc1917d2485eaa8e92b68245578920c76 | https://github.com/derwind/dmfont/tree/17a91a9cc1917d2485eaa8e92b68245578920c76 | import torch
import torch.nn.functional as F
from functools import partial
import torch.nn as nn
def dispatcher(dispatch_fn):
def decorated(key, *args):
if callable(key):
return key
if key is None:
key = 'none'
return dispatch_fn(key, *args)
return decorated
... |
IdentityPadding | # 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 IdentityPadding(nn.Module):
def __init__(self, in_channels, out_channels, stride):
super(IdentityPadding, self).__init__()
self.pooling = nn.MaxPool2d(1, stride=stride)
self.add_channels = out_channels - in_channels
... | 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... | dnddnjs/pytorch-vision | IdentityPadding | false | 15,181 | [
"MIT"
] | 48 | d432b467774f838bef37372d6cff3576c6559803 | https://github.com/dnddnjs/pytorch-vision/tree/d432b467774f838bef37372d6cff3576c6559803 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, in_channels, out_channels, stride):
super().__init__()
self.pooling = nn.MaxPool2d(1, stride=stride)
self.add_channels = out_channels - in_channels
def forward(self, x):
... |
BertSelfOutput | # 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 BertLayerNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-12):
"""Construct a layernorm module in the TF style (epsilon inside the square root).
"""
super(BertLayerNorm, self).__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
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | dfhby0/CBLUE | BertSelfOutput | false | 15,182 | [
"Apache-2.0"
] | 293 | 36bdb52f17c4379d4a5f8b407890ba294017b5e2 | https://github.com/dfhby0/CBLUE/tree/36bdb52f17c4379d4a5f8b407890ba294017b5e2 | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class BertLayerNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-12):
"""Construct a layernorm module in the TF style (epsilon inside the square root).
"""
super().__init__()
self.weight = nn.Pa... |
TwoLayerNet | # 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
class TwoLayerNet(torch.nn.Module):
def __init__(self, D_in, H, D_out):
super(TwoLayerNet, self).__init__()
self.linear1 = torch.nn.Linear(D_in, H)
self.linear2 = torch.nn.Linear(H, D_out)
def forward(self, x):
h_relu = self.linear1(x).clamp(min=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
import torch.nn
assert_size_s... | dionhaefner/delve | TwoLayerNet | false | 15,183 | [
"MIT"
] | 69 | 811756520cbfd8dce4427c53203ac193f61a94d1 | https://github.com/dionhaefner/delve/tree/811756520cbfd8dce4427c53203ac193f61a94d1 | import torch
import torch.nn
class Model(torch.nn.Module):
def __init__(self, D_in, H, D_out):
super().__init__()
self.linear1 = torch.nn.Linear(D_in, H)
self.linear2 = torch.nn.Linear(H, D_out)
def forward(self, x):
h_relu = self.linear1(x).clamp(min=0)
y_pred = self... |
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 torch
import numpy as np
def scaled_dot_product_attention(q, k, v, mask):
matmul_qk = torch.matmul(q, k.permute(0, 1, 3, 2))
dk = k.shape[-1]
scaled_attention_logits = matmul_qk / np.sqrt(dk)
if mask is not None:
scaled_attention_logits += mask * -1000000000.0
attention_weights = to... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | depengchen123/ctrl | MultiHeadAttention | false | 15,184 | [
"BSD-3-Clause"
] | 1,559 | 8673e9ec1bf6441ad8d793a626cdfd8c1fd9c4e4 | https://github.com/depengchen123/ctrl/tree/8673e9ec1bf6441ad8d793a626cdfd8c1fd9c4e4 | import torch
import numpy as np
def scaled_dot_product_attention(q, k, v, mask):
matmul_qk = torch.matmul(q, k.permute(0, 1, 3, 2))
dk = k.shape[-1]
scaled_attention_logits = matmul_qk / np.sqrt(dk)
if mask is not None:
scaled_attention_logits += mask * -1000000000.0
attention_weights = to... |
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 abc import abstractmethod
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
@abstractmethod
def forward(... | 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 abc import abstractmethod
from torch i... | diana-hep/madminer | BatchNorm | false | 15,185 | [
"MIT"
] | 46 | 3a585d2887a31886cdeadddb0a284f0472146fce | https://github.com/diana-hep/madminer/tree/3a585d2887a31886cdeadddb0a284f0472146fce | import torch
import numpy as np
from abc import abstractmethod
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
@abstractmethod
def forward(... |
LayerCake | # 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
class LayerCake(torch.nn.Module):
def __init__(self, D_in, H1, H2, H3, H4, H5, D_out):
"""
In the constructor we instantiate two nn.Linear modules and assign them as
member variables.
"""
super(LayerCake, self).__init__()
self.linear1 =... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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
assert_size_s... | dionhaefner/delve | LayerCake | false | 15,186 | [
"MIT"
] | 69 | 811756520cbfd8dce4427c53203ac193f61a94d1 | https://github.com/dionhaefner/delve/tree/811756520cbfd8dce4427c53203ac193f61a94d1 | import torch
import torch.nn
class Model(torch.nn.Module):
def __init__(self, D_in, H1, H2, H3, H4, H5, D_out):
"""
In the constructor we instantiate two nn.Linear modules and assign them as
member variables.
"""
super().__init__()
self.linear1 = torch.nn.Linear(D_... |
DWT | # 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.fft
class DWT(nn.Module):
"""
2D Discrete Wavelet Transform as implemented in [1]_.
References
----------
.. [1] Liu, Pengju, et al. “Multi-Level Wavelet-CNN for Image Restoration.” ArXiv:1805.07071 [Cs], May 2018.
arXiv.org, http://arxiv.org/a... | 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.fft
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo... | directgroup/direct | DWT | false | 15,187 | [
"Apache-2.0"
] | 55 | 78cdd530b3c93e31c11d8963880e6329f0989243 | https://github.com/directgroup/direct/tree/78cdd530b3c93e31c11d8963880e6329f0989243 | import torch
import torch.nn as nn
import torch.fft
class Model(nn.Module):
"""
2D Discrete Wavelet Transform as implemented in [1]_.
References
----------
.. [1] Liu, Pengju, et al. “Multi-Level Wavelet-CNN for Image Restoration.” ArXiv:1805.07071 [Cs], May 2018.
arXiv.org, http://arxiv.org... |
CReLU_IN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn.functional as F
import torch.nn as nn
class CReLU_IN(nn.Module):
def __init__(self, channels):
super(CReLU_IN, self).__init__()
self.bn = nn.InstanceNorm2d(channels * 2, eps=1e-05, momentum=0.1,
affine=True)
def forward(self, x):
cat = torch.c... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | dipikakhullar/ocr | CReLU_IN | false | 15,188 | [
"MIT"
] | 284 | a55e70d82f42803be5ed63f8f59e4fa597fcf8d6 | https://github.com/dipikakhullar/ocr/tree/a55e70d82f42803be5ed63f8f59e4fa597fcf8d6 | import torch
import torch.nn.functional as F
import torch.nn as nn
class Model(nn.Module):
def __init__(self, channels):
super().__init__()
self.bn = nn.InstanceNorm2d(channels * 2, eps=1e-05, momentum=0.1,
affine=True)
def forward(self, x):
cat = torch.cat((x, -x), 1)
... |
BinaryCrossEntropyLabelSmooth | # 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 BinaryCrossEntropyLabelSmooth(torch.nn.BCEWithLogitsLoss):
def __init__(self, num_classes, epsilon=0.1, weight=None, size_average=
None, reduce=None, reduction='mean', pos_weight=None):
super(BinaryCrossEntropyLabelSmooth, self).__init__(weight,
size_average, reduce... | 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
assert_size... | dianjixz/AutoDL | BinaryCrossEntropyLabelSmooth | false | 15,189 | [
"Apache-2.0"
] | 1,044 | 48db4eb04d55ce69e93d4a3bdc24592bdb34a868 | https://github.com/dianjixz/AutoDL/tree/48db4eb04d55ce69e93d4a3bdc24592bdb34a868 | import torch
class Model(torch.nn.BCEWithLogitsLoss):
def __init__(self, num_classes, epsilon=0.1, weight=None, size_average=
None, reduce=None, reduction='mean', pos_weight=None):
super().__init__(weight,
size_average, reduce, reduction, pos_weight)
self.num_classes = num_cla... |
ProteinResNetPooler | # 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
class ProteinResNetPooler(nn.Module):
def __init__(self, config):
super().__init__()
self.attention_weights = nn.Linear(config.hidden_size, 1)
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
fr... | IC-hub/ProteinLM | ProteinResNetPooler | false | 15,190 | [
"Apache-2.0"
] | 59 | 58fbf1f674569cf814becf32f71dd0d8f0c592fa | https://github.com/IC-hub/ProteinLM/tree/58fbf1f674569cf814becf32f71dd0d8f0c592fa | from _paritybench_helpers import _mock_config
import torch
from torch import nn
class Model(nn.Module):
def __init__(self, config):
super().__init__()
self.attention_weights = nn.Linear(config.hidden_size, 1)
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.acti... |
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.nn as nn
class DiceLoss(nn.Module):
def __init__(self, loss_weight=1.0):
super(DiceLoss, self).__init__()
self.loss_weight = loss_weight
def forward(self, input, target, mask, reduce=True):
batch_size = input.size(0)
input = torch.sigmoid(input)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | doem97/PSENet | DiceLoss | false | 15,192 | [
"Apache-2.0"
] | 1,213 | 4d95395658662f2223805c36dcd573d9e190ce26 | https://github.com/doem97/PSENet/tree/4d95395658662f2223805c36dcd573d9e190ce26 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, loss_weight=1.0):
super().__init__()
self.loss_weight = loss_weight
def forward(self, input, target, mask, reduce=True):
batch_size = input.size(0)
input = torch.sigmoid(input)
input = input... |
Net | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.fc1 = nn.Linear(4, 64)
self.fc2 = nn.Linear(64, 64)
self.fc3 = nn.Linear(64, 2)
def forward(self, x):
x = torch.tanh(self.fc1(x))
x = torch.tanh(self.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | dongminlee94/Samsung-DRL-Code | Net | false | 15,193 | [
"MIT"
] | 116 | c96f8739a09cfd708c265954ee8ecf0ea3b67395 | https://github.com/dongminlee94/Samsung-DRL-Code/tree/c96f8739a09cfd708c265954ee8ecf0ea3b67395 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(4, 64)
self.fc2 = nn.Linear(64, 64)
self.fc3 = nn.Linear(64, 2)
def forward(self, x):
x = torch.tanh(self.fc1(x))
x = torch.tanh(self.fc2(x))... |
MNISTClassifier | # 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 torchvision
import torchvision.ops
from torch import nn
class DeformableConv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1,
padding=1, bias=False):
super(DeformableConv2d, self).__init__()
assert type(kernel_size) == tuple or type(... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 torchvision
import tor... | developer0hye/PyTorch-Deformable-Convolution-v2 | MNISTClassifier | false | 15,194 | [
"MIT"
] | 70 | 3ed601fa70ee111278b95b134caf29e085642bc2 | https://github.com/developer0hye/PyTorch-Deformable-Convolution-v2/tree/3ed601fa70ee111278b95b134caf29e085642bc2 | import torch
import torchvision
import torchvision.ops
from torch import nn
class DeformableConv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1,
padding=1, bias=False):
super().__init__()
assert type(kernel_size) == tuple or type(kernel_size) == int
... |
Net | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.nn import Parameter
class Conv1dExt(nn.Conv1d):
def __init__(self, *args, **kwargs):
super(Conv1dExt, self).__init__(*args, **kwargs)
self.init_ncc()
self.input_tied_modules = []
self.output_tied_mod... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from to... | dhpollack/fast-wavenet.pytorch | Net | false | 15,195 | [
"MIT"
] | 98 | 853f6ecb1e8d23a5c01fc2455640c6637d30f2f9 | https://github.com/dhpollack/fast-wavenet.pytorch/tree/853f6ecb1e8d23a5c01fc2455640c6637d30f2f9 | import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.nn import Parameter
class Conv1dExt(nn.Conv1d):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.init_ncc()
self.input_tied_modules = []
self.output_tied_modules = []
... |
ReduceBranch | # 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 ReduceBranch(nn.Module):
def __init__(self, planes, stride=2):
super(ReduceBranch, self).__init__()
self.conv1 = nn.Conv2d(planes, planes, kernel_size=1, stride=1,
padding=0, bias=False)
self.conv2 = nn.C... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | dnddnjs/pytorch-vision | ReduceBranch | false | 15,196 | [
"MIT"
] | 48 | d432b467774f838bef37372d6cff3576c6559803 | https://github.com/dnddnjs/pytorch-vision/tree/d432b467774f838bef37372d6cff3576c6559803 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, planes, stride=2):
super().__init__()
self.conv1 = nn.Conv2d(planes, planes, kernel_size=1, stride=1,
padding=0, bias=False)
self.conv2 = nn.Conv2d(planes, planes, ker... |
InstanceNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.utils.data
import torch.nn as nn
from torch.nn.parameter import Parameter
class InstanceNorm(nn.Module):
def __init__(self, num_features, affine=True, eps=1e-05):
"""`num_features` number of feature channels
"""
super(InstanceNorm, self).__init__()
self.n... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
import torch.nn as nn
from torch.nn.parameter import Pa... | doantientai/augmented_cyclegan | InstanceNorm | false | 15,197 | [
"MIT"
] | 133 | 821274577e71c412198356ad6302c982554d558c | https://github.com/doantientai/augmented_cyclegan/tree/821274577e71c412198356ad6302c982554d558c | import torch
import torch.utils.data
import torch.nn as nn
from torch.nn.parameter import Parameter
class Model(nn.Module):
def __init__(self, num_features, affine=True, eps=1e-05):
"""`num_features` number of feature channels
"""
super().__init__()
self.num_features = num_feature... |
Actor | # 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 Actor(nn.Module):
def __init__(self, state_size, action_size, args, log_std_min=-20,
log_std_max=2):
super(Actor, self).__init__()
self.log_std_min = log_std_min
self.log_std_max = log_std_max
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | dongminlee94/Samsung-DRL-Code | Actor | false | 15,198 | [
"MIT"
] | 116 | c96f8739a09cfd708c265954ee8ecf0ea3b67395 | https://github.com/dongminlee94/Samsung-DRL-Code/tree/c96f8739a09cfd708c265954ee8ecf0ea3b67395 | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, state_size, action_size, args, log_std_min=-20,
log_std_max=2):
super().__init__()
self.log_std_min = log_std_min
self.log_std_max = log_std_max
self... |
MultiHeadedAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
from torch.nn import functional as F
def same_tensor(tensor, *args):
""" Do the input tensors all point to the same underlying data """
for other in args:
if not torch.is_tensor(other):
return False
if tensor.device != other.device:
ret... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | dojoteef/synst | MultiHeadedAttention | false | 15,199 | [
"BSD-3-Clause"
] | 81 | a1842682cf757e8a501cd9cee16f20e1a14158f1 | https://github.com/dojoteef/synst/tree/a1842682cf757e8a501cd9cee16f20e1a14158f1 | import torch
from torch import nn
from torch.nn import functional as F
def same_tensor(tensor, *args):
""" Do the input tensors all point to the same underlying data """
for other in args:
if not torch.is_tensor(other):
return False
if tensor.device != other.device:
ret... |
GeneralizedMeanPooling | # 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... | from torch.nn import Module
import torch
import torch.nn.functional as F
from torch.nn.modules import Module
class GeneralizedMeanPooling(Module):
"""Applies a 2D power-average adaptive pooling over an input signal composed of several input planes.
The function computed is: :math:`f(X) = pow(sum(pow(X, p)), ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch.nn import Module
... | dongan-beta/deep-image-retrieval | GeneralizedMeanPooling | false | 15,200 | [
"BSD-3-Clause"
] | 253 | 3e0885f88da328aefb7abb2fa350f8860a4bd52d | https://github.com/dongan-beta/deep-image-retrieval/tree/3e0885f88da328aefb7abb2fa350f8860a4bd52d | from torch.nn import Module
import torch
import torch.nn.functional as F
from torch.nn.modules import Module
class Model(Module):
"""Applies a 2D power-average adaptive pooling over an input signal composed of several input planes.
The function computed is: :math:`f(X) = pow(sum(pow(X, p)), 1/p)`
- ... |
TripletLogExpLoss | # 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
import torch.nn.functional as F
import torch.nn as nn
class TripletLogExpLoss(nn.Module):
"""Creates a criterion that measures the triplet loss given an input
tensors x1, x2, x3.
This is used for measuring a relative similarity between samples. A triplet
is composed by ... | 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
import torch.nn as nn
assert_size_stride = ... | dongan-beta/deep-image-retrieval | TripletLogExpLoss | false | 15,201 | [
"BSD-3-Clause"
] | 253 | 3e0885f88da328aefb7abb2fa350f8860a4bd52d | https://github.com/dongan-beta/deep-image-retrieval/tree/3e0885f88da328aefb7abb2fa350f8860a4bd52d | import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
class Model(nn.Module):
"""Creates a criterion that measures the triplet loss given an input
tensors x1, x2, x3.
This is used for measuring a relative similarity between samples. A triplet
is composed by `a`, `p` and... |
APLoss_dist | # 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
import torch.nn as nn
def sim_to_dist(scores):
return 1 - torch.sqrt(2.001 - 2 * scores)
class APLoss(nn.Module):
""" Differentiable AP loss, through quantization. From the paper:
Learning with Average Precision: Training Image Retrieval with a Listwise Loss
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | dongan-beta/deep-image-retrieval | APLoss_dist | false | 15,202 | [
"BSD-3-Clause"
] | 253 | 3e0885f88da328aefb7abb2fa350f8860a4bd52d | https://github.com/dongan-beta/deep-image-retrieval/tree/3e0885f88da328aefb7abb2fa350f8860a4bd52d | import torch
import numpy as np
import torch.nn as nn
def sim_to_dist(scores):
return 1 - torch.sqrt(2.001 - 2 * scores)
class APLoss(nn.Module):
""" Differentiable AP loss, through quantization. From the paper:
Learning with Average Precision: Training Image Retrieval with a Listwise Loss
... |
Conv | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
from torch.nn.functional import interpolate
from typing import cast
class Interpolate(nn.Module):
def __init__(self, scale_factor: 'float'=1.0, mode: 'str'='nearest'
) ->None:
super().__init__()
self.scale_factor = scale_factor
self.mode = mode
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | dooglewoogle/pystiche | Conv | false | 15,203 | [
"BSD-3-Clause"
] | 129 | 14b61123ede2abdb00daaa5b4981de6d7edaf034 | https://github.com/dooglewoogle/pystiche/tree/14b61123ede2abdb00daaa5b4981de6d7edaf034 | import torch
from torch import nn
from torch.nn.functional import interpolate
from typing import cast
class Interpolate(nn.Module):
def __init__(self, scale_factor: 'float'=1.0, mode: 'str'='nearest'
) ->None:
super().__init__()
self.scale_factor = scale_factor
self.mode = mode
... |
_nms | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.utils.data
import torch
import torch.nn as nn
class _nms(nn.Module):
def __init__(self):
super(_nms, self).__init__()
kernel = 3
pad = (kernel - 1) // 2
self.maxpool = nn.MaxPool2d(kernel_size=kernel, stride=1, padding=pad)
def forward(self, heat):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
import torch
import torch.nn as nn
assert_size_stride = torch._C.... | donnyyou/centerX | _nms | false | 15,204 | [
"Apache-2.0"
] | 350 | 6e381cb669a6014d02e31a43915271237690531c | https://github.com/donnyyou/centerX/tree/6e381cb669a6014d02e31a43915271237690531c | import torch
import torch.utils.data
import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
kernel = 3
pad = (kernel - 1) // 2
self.maxpool = nn.MaxPool2d(kernel_size=kernel, stride=1, padding=pad)
def forward(self, heat):
hm... |
UpConv | # 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 UpConv(nn.Module):
def __init__(self, input_nc, output_nc, kernel_size):
super(UpConv, self).__init__()
self.deconv = nn.ConvTranspose2d(in_channels=input_nc, out_channels
=output_nc, kernel_size=2, bias=True, stride=2, padding=0)
self.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | dong1015323606/LKVOLearner | UpConv | false | 15,205 | [
"BSD-3-Clause"
] | 237 | 6ac9fb5d3c22d6a81529063f8c52d6aa34166b2a | https://github.com/dong1015323606/LKVOLearner/tree/6ac9fb5d3c22d6a81529063f8c52d6aa34166b2a | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, input_nc, output_nc, kernel_size):
super().__init__()
self.deconv = nn.ConvTranspose2d(in_channels=input_nc, out_channels
=output_nc, kernel_size=2, bias=True, stride=2, padding=0)
self.activation_fn... |
DetLoss | # 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 DetLoss(nn.Module):
def __init__(self):
super().__init__()
self.hm_criterion = nn.BCEWithLogitsLoss(reduction='none')
self.ori_criterion = nn.SmoothL1Loss(reduction='none')
self.box_criterion = nn.SmoothL1Loss(reduction='none')
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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch ... | dotchen/LAV | DetLoss | false | 15,206 | [
"Apache-2.0"
] | 122 | dc9b4cfca39abd50c7438e8749d49f6ac0fe5e4e | https://github.com/dotchen/LAV/tree/dc9b4cfca39abd50c7438e8749d49f6ac0fe5e4e | import torch
from torch import nn
class Model(nn.Module):
def __init__(self):
super().__init__()
self.hm_criterion = nn.BCEWithLogitsLoss(reduction='none')
self.ori_criterion = nn.SmoothL1Loss(reduction='none')
self.box_criterion = nn.SmoothL1Loss(reduction='none')
def forwar... |
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
class Critic(nn.Module):
def __init__(self, state_size, action_size, args):
super(Critic, self).__init__()
self.fc1 = nn.Linear(state_size + action_size, args.hidden_size)
self.fc2 = nn.Linear(args.hidden_siz... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | dongminlee94/Samsung-DRL-Code | Critic | false | 15,207 | [
"MIT"
] | 116 | c96f8739a09cfd708c265954ee8ecf0ea3b67395 | https://github.com/dongminlee94/Samsung-DRL-Code/tree/c96f8739a09cfd708c265954ee8ecf0ea3b67395 | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, state_size, action_size, args):
super().__init__()
self.fc1 = nn.Linear(state_size + action_size, args.hidden_size)
self.fc2 = nn.Linear(args.hidden_size, args.hidde... |
AngleSimpleLinear | # 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
from torchvision import models as models
from torch.nn import Parameter
from torch.nn.parameter import Parameter
import torch.onnx
import torch.nn
class AngleSimpleLinear(nn.Module):
"""Computes cos of angles between input vectors and weights ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | dqawami/openvino_training_extensions | AngleSimpleLinear | false | 15,208 | [
"Apache-2.0"
] | 256 | dddda1dfd651eaae2d59cecda84275b1b03bd0ad | https://github.com/dqawami/openvino_training_extensions/tree/dddda1dfd651eaae2d59cecda84275b1b03bd0ad | import torch
from torch.nn import functional as F
from torch import nn
from torchvision import models as models
from torch.nn import Parameter
from torch.nn.parameter import Parameter
import torch.onnx
import torch.nn
class Model(nn.Module):
"""Computes cos of angles between input vectors and weights vectors"""
... |
LogitKLDivLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch.nn import functional as F
from torch import nn
from torchvision import models as models
import torch.onnx
import torch.nn
class LogitKLDivLoss(nn.Module):
"""Kullback–Leibler divergence loss. Inputs predicted and ground truth logits.
Args:
T (float): Softmax temperature.
"... | 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 ... | dqawami/openvino_training_extensions | LogitKLDivLoss | false | 15,209 | [
"Apache-2.0"
] | 256 | dddda1dfd651eaae2d59cecda84275b1b03bd0ad | https://github.com/dqawami/openvino_training_extensions/tree/dddda1dfd651eaae2d59cecda84275b1b03bd0ad | import torch
from torch.nn import functional as F
from torch import nn
from torchvision import models as models
import torch.onnx
import torch.nn
class Model(nn.Module):
"""Kullback–Leibler divergence loss. Inputs predicted and ground truth logits.
Args:
T (float): Softmax temperature.
"""
d... |
LengthPredictor | # 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
from torchvision import models as models
import torch.onnx
import torch.nn
class LengthPredictionLoss(nn.Module):
def __init__(self, max_delta=50):
super().__init__()
self.max_delta = max_delta
def forward(self, logits, s... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn import function... | dqawami/openvino_training_extensions | LengthPredictor | false | 15,210 | [
"Apache-2.0"
] | 256 | dddda1dfd651eaae2d59cecda84275b1b03bd0ad | https://github.com/dqawami/openvino_training_extensions/tree/dddda1dfd651eaae2d59cecda84275b1b03bd0ad | import torch
from torch.nn import functional as F
from torch import nn
from torchvision import models as models
import torch.onnx
import torch.nn
class LengthPredictionLoss(nn.Module):
def __init__(self, max_delta=50):
super().__init__()
self.max_delta = max_delta
def forward(self, logits, s... |
ResNet_conv1 | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
import torch.utils.data
import torch.nn as nn
class ResNet_conv1(nn.Module):
def __init__(self, block, layers, num_classes=1000):
self.inplanes = 64
super(ResNet_conv1, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=1, padding=3,
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.utils.data
import torch.nn as nn
assert_size_stride = t... | donegaci/memc-net | ResNet_conv1 | false | 15,211 | [
"MIT"
] | 145 | 9bdb0ab6ce99af22a165db2cedacd148dd6083c0 | https://github.com/donegaci/memc-net/tree/9bdb0ab6ce99af22a165db2cedacd148dd6083c0 | import math
import torch
import torch.utils.data
import torch.nn as nn
class Model(nn.Module):
def __init__(self, block, layers, num_classes=1000):
self.inplanes = 64
super().__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=1, padding=3,
bias=False)
for m... |
Norm | # 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.parallel
import torch.utils.data
class Norm(nn.Module):
def __init__(self, dims):
super(Norm, self).__init__()
self.dims = dims
def forward(self, x):
z2 = torch.norm(x, p=2)
out = z2 - self.dims
out = out * out
... | 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... | doudoulaile/RL-GAN-Net | Norm | false | 15,212 | [
"MIT"
] | 112 | 9c221223d1878bc24f0f39ad34928c1bb2974ae3 | https://github.com/doudoulaile/RL-GAN-Net/tree/9c221223d1878bc24f0f39ad34928c1bb2974ae3 | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
class Model(nn.Module):
def __init__(self, dims):
super().__init__()
self.dims = dims
def forward(self, x):
z2 = torch.norm(x, p=2)
out = z2 - self.dims
out = out * out
return ... |
StateInitZero | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch import nn
from torchvision import models as models
import torch.onnx
import torch.nn
class StateInitZero(nn.Module):
def __init__(self, hidden_size, num_layers=1, batch_first=False):
super(StateInitZero, self).__init__()
self.hidden_size = hidden_size
self.num_laye... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
from torchvision import models as models
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards... | dqawami/openvino_training_extensions | StateInitZero | false | 15,213 | [
"Apache-2.0"
] | 256 | dddda1dfd651eaae2d59cecda84275b1b03bd0ad | https://github.com/dqawami/openvino_training_extensions/tree/dddda1dfd651eaae2d59cecda84275b1b03bd0ad | import torch
from torch import nn
from torchvision import models as models
import torch.onnx
import torch.nn
class Model(nn.Module):
def __init__(self, hidden_size, num_layers=1, batch_first=False):
super().__init__()
self.hidden_size = hidden_size
self.num_layers = num_layers
sel... |
ScaledDotProductAttention | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch import nn
from torchvision import models as models
import torch.onnx
import torch.nn
class ScaledDotProductAttention(nn.Module):
def __init__(self, dropout=0, scale=True):
super().__init__()
self.dropout = nn.Dropout(p=dropout)
self.softmax = nn.Softmax(dim=2)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | dqawami/openvino_training_extensions | ScaledDotProductAttention | false | 15,214 | [
"Apache-2.0"
] | 256 | dddda1dfd651eaae2d59cecda84275b1b03bd0ad | https://github.com/dqawami/openvino_training_extensions/tree/dddda1dfd651eaae2d59cecda84275b1b03bd0ad | import torch
from torch import nn
from torchvision import models as models
import torch.onnx
import torch.nn
class Model(nn.Module):
def __init__(self, dropout=0, scale=True):
super().__init__()
self.dropout = nn.Dropout(p=dropout)
self.softmax = nn.Softmax(dim=2)
self.scale = sca... |
GateAddNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
from torchvision import models as models
import torch.onnx
import torch.nn
class GatedLinearUnit(nn.Module):
def __init__(self, input_size, output_size, dropout=0):
super().__init__()
self.dropout = nn.Dropout(dropout)
self.w4 = nn.Linear(input_size, outp... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | dqawami/openvino_training_extensions | GateAddNorm | false | 15,215 | [
"Apache-2.0"
] | 256 | dddda1dfd651eaae2d59cecda84275b1b03bd0ad | https://github.com/dqawami/openvino_training_extensions/tree/dddda1dfd651eaae2d59cecda84275b1b03bd0ad | import torch
from torch import nn
from torchvision import models as models
import torch.onnx
import torch.nn
class GatedLinearUnit(nn.Module):
def __init__(self, input_size, output_size, dropout=0):
super().__init__()
self.dropout = nn.Dropout(dropout)
self.w4 = nn.Linear(input_size, outp... |
_MCLSTMCell | # 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 typing import Tuple
import torch.nn as nn
class _Gate(nn.Module):
"""Utility class to implement a standard sigmoid gate"""
def __init__(self, in_features: 'int', out_features: 'int'):
super(_Gate, self).__init__()
self.fc = nn.Li... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | DavidChoi76/neuralhydrology | _MCLSTMCell | false | 15,216 | [
"BSD-3-Clause"
] | 144 | a4c284b92934ee973c8b3fedf8a60df60c8feae1 | https://github.com/DavidChoi76/neuralhydrology/tree/a4c284b92934ee973c8b3fedf8a60df60c8feae1 | from _paritybench_helpers import _mock_config
import torch
from typing import Tuple
import torch.nn as nn
class _Gate(nn.Module):
"""Utility class to implement a standard sigmoid gate"""
def __init__(self, in_features: 'int', out_features: 'int'):
super().__init__()
self.fc = nn.Linear(in_fea... |
GatedResidualNetwork | # 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
from torchvision import models as models
import torch.onnx
import torch.nn
class GatedLinearUnit(nn.Module):
def __init__(self, input_size, output_size, dropout=0):
super().__init__()
self.dropout = nn.Dropout(dropout)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | dqawami/openvino_training_extensions | GatedResidualNetwork | false | 15,217 | [
"Apache-2.0"
] | 256 | dddda1dfd651eaae2d59cecda84275b1b03bd0ad | https://github.com/dqawami/openvino_training_extensions/tree/dddda1dfd651eaae2d59cecda84275b1b03bd0ad | import torch
from torch.nn import functional as F
from torch import nn
from torchvision import models as models
import torch.onnx
import torch.nn
class GatedLinearUnit(nn.Module):
def __init__(self, input_size, output_size, dropout=0):
super().__init__()
self.dropout = nn.Dropout(dropout)
... |
SpatialAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
from torchvision import models as models
import torch.onnx
import torch.nn
class SpatialAttention(nn.Module):
def __init__(self, in_channels):
super().__init__()
self.activation = nn.Sigmoid()
self.maxpool = nn.MaxPool2d((1, in_channels))
self.avg... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
from tor... | dqawami/openvino_training_extensions | SpatialAttention | false | 15,218 | [
"Apache-2.0"
] | 256 | dddda1dfd651eaae2d59cecda84275b1b03bd0ad | https://github.com/dqawami/openvino_training_extensions/tree/dddda1dfd651eaae2d59cecda84275b1b03bd0ad | import torch
from torch import nn
from torchvision import models as models
import torch.onnx
import torch.nn
class Model(nn.Module):
def __init__(self, in_channels):
super().__init__()
self.activation = nn.Sigmoid()
self.maxpool = nn.MaxPool2d((1, in_channels))
self.avgpool = nn.A... |
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... | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
import torch.nn.functional as F
class Critic(nn.Module):
def __init__(self, state_dim, action_dim):
super(Critic, self).__init__()
self.l1 = nn.Linear(state_dim + action_dim, 400)
self.l2 = nn.Linear(400, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 ... | doudoulaile/RL-GAN-Net | Critic | false | 15,219 | [
"MIT"
] | 112 | 9c221223d1878bc24f0f39ad34928c1bb2974ae3 | https://github.com/doudoulaile/RL-GAN-Net/tree/9c221223d1878bc24f0f39ad34928c1bb2974ae3 | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, state_dim, action_dim):
super().__init__()
self.l1 = nn.Linear(state_dim + action_dim, 400)
self.l2 = nn.Linear(400, 300)
... |
SmallBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
from torchvision import models as models
import torch.onnx
import torch.nn
class SmallBlock(nn.Module):
def __init__(self, channels):
super(SmallBlock, self).__init__()
self.conv1 = nn.Conv2d(in_channels=channels, out_channels=channels,
kernel_size=3,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
from tor... | dqawami/openvino_training_extensions | SmallBlock | false | 15,220 | [
"Apache-2.0"
] | 256 | dddda1dfd651eaae2d59cecda84275b1b03bd0ad | https://github.com/dqawami/openvino_training_extensions/tree/dddda1dfd651eaae2d59cecda84275b1b03bd0ad | import torch
from torch import nn
from torchvision import models as models
import torch.onnx
import torch.nn
class Model(nn.Module):
def __init__(self, channels):
super().__init__()
self.conv1 = nn.Conv2d(in_channels=channels, out_channels=channels,
kernel_size=3, stride=1, padding=1,... |
ResBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
from torchvision import models as models
import torch.onnx
import torch.nn
class ResBlock(nn.Module):
def __init__(self, num_of_channels):
super(ResBlock, self).__init__()
self.conv1 = nn.Conv2d(in_channels=num_of_channels, out_channels=
num_of_channe... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | dqawami/openvino_training_extensions | ResBlock | false | 15,221 | [
"Apache-2.0"
] | 256 | dddda1dfd651eaae2d59cecda84275b1b03bd0ad | https://github.com/dqawami/openvino_training_extensions/tree/dddda1dfd651eaae2d59cecda84275b1b03bd0ad | import torch
from torch import nn
from torchvision import models as models
import torch.onnx
import torch.nn
class Model(nn.Module):
def __init__(self, num_of_channels):
super().__init__()
self.conv1 = nn.Conv2d(in_channels=num_of_channels, out_channels=
num_of_channels, kernel_size=3... |
EntmaxBisect | # 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... | from torch.autograd import Function
import torch
import torch.nn as nn
def entmax_bisect(X, alpha=1.5, dim=-1, n_iter=50, ensure_sum_one=True):
"""alpha-entmax: normalizing sparse transform (a la softmax).
Solves the optimization problem:
max_p <x, p> - H_a(p) s.t. p >= 0, sum(p) == 1.
wh... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch.autograd import F... | cifkao/entmax | EntmaxBisect | false | 15,222 | [
"MIT"
] | 298 | f18bab9318f9d2471a36545ee0b4c97be6d48a87 | https://github.com/cifkao/entmax/tree/f18bab9318f9d2471a36545ee0b4c97be6d48a87 | from torch.autograd import Function
import torch
import torch.nn as nn
def entmax_bisect(X, alpha=1.5, dim=-1, n_iter=50, ensure_sum_one=True):
"""alpha-entmax: normalizing sparse transform (a la softmax).
Solves the optimization problem:
max_p <x, p> - H_a(p) s.t. p >= 0, sum(p) == 1.
wh... |
Net | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch.nn import functional as F
from torch import nn
from torchvision import models as models
import torch.onnx
import torch.nn
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 10, kernel_size=3)
self.conv2 = nn.Conv2d(10, 2... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | dqawami/openvino_training_extensions | Net | false | 15,223 | [
"Apache-2.0"
] | 256 | dddda1dfd651eaae2d59cecda84275b1b03bd0ad | https://github.com/dqawami/openvino_training_extensions/tree/dddda1dfd651eaae2d59cecda84275b1b03bd0ad | import torch
from torch.nn import functional as F
from torch import nn
from torchvision import models as models
import torch.onnx
import torch.nn
class Model(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(3, 10, kernel_size=3)
self.conv2 = nn.Conv2d(10, 20, kern... |
EquivariantLayer | # 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.parallel
import torch.utils.data
import torch.nn.functional as F
from torch.nn.modules.batchnorm import _BatchNorm
class MyBatchNorm1d(_BatchNorm):
"""Applies Batch Normalization over a 2d or 3d input that is seen as a
mini-batch.
.. math::
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import math
import torch.nn a... | doudoulaile/RL-GAN-Net | EquivariantLayer | false | 15,224 | [
"MIT"
] | 112 | 9c221223d1878bc24f0f39ad34928c1bb2974ae3 | https://github.com/doudoulaile/RL-GAN-Net/tree/9c221223d1878bc24f0f39ad34928c1bb2974ae3 | import math
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
import torch.nn.functional as F
from torch.nn.modules.batchnorm import _BatchNorm
class MyBatchNorm1d(_BatchNorm):
"""Applies Batch Normalization over a 2d or 3d input that is seen as a
mini-batch.
.. math::
... |
FAdd | # 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
import torch.nn as nn
class FAdd(nn.Module):
def __init__(self):
super(FAdd, self).__init__()
def forward(self, x, y):
x = x + y + np.float32(0.1)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_in... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | dawnclaude/onnx2keras | FAdd | false | 15,225 | [
"MIT"
] | 115 | 3d2a47c0a228b91fd434232274e216e491da36e3 | https://github.com/dawnclaude/onnx2keras/tree/3d2a47c0a228b91fd434232274e216e491da36e3 | import torch
import numpy as np
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x, y):
x = x + y + np.float32(0.1)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs... |
Embedding_Net | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.nn.functional as F
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Linear') != -1:
m.weight.data.normal_(0.0, 0.02)
m.bias.data.fill_(0)
elif classname.find('BatchNorm') !... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Huihui-z/CE-GZSL | Embedding_Net | false | 15,226 | [
"MIT"
] | 58 | 7bf5358ac4727ea1dc2dc9dec2f453b014500bd8 | https://github.com/Huihui-z/CE-GZSL/tree/7bf5358ac4727ea1dc2dc9dec2f453b014500bd8 | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.nn.functional as F
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Linear') != -1:
m.weight.data.normal_(0.0, 0.02)
m.bias.data.fill_(0)
elif classname.find('BatchNorm') !... |
GatedLinearUnit | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
from torchvision import models as models
import torch.onnx
import torch.nn
class GatedLinearUnit(nn.Module):
def __init__(self, input_size, output_size, dropout=0):
super().__init__()
self.dropout = nn.Dropout(dropout)
self.w4 = nn.Linear(input_size, outp... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
from torchvision import models as models
import torch.onnx
... | dqawami/openvino_training_extensions | GatedLinearUnit | false | 15,227 | [
"Apache-2.0"
] | 256 | dddda1dfd651eaae2d59cecda84275b1b03bd0ad | https://github.com/dqawami/openvino_training_extensions/tree/dddda1dfd651eaae2d59cecda84275b1b03bd0ad | import torch
from torch import nn
from torchvision import models as models
import torch.onnx
import torch.nn
class Model(nn.Module):
def __init__(self, input_size, output_size, dropout=0):
super().__init__()
self.dropout = nn.Dropout(dropout)
self.w4 = nn.Linear(input_size, output_size)
... |
Swish | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
class Swish(nn.Module):
def __init__(self):
super(Swish, self).__init__()
def forward(self, x):
return 1.78718727865 * (x * torch.sigmoid(x) - 0.20662096414)
def get_inputs():
return [torch.rand([4, 4, ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty... | doudoulaile/RL-GAN-Net | Swish | false | 15,228 | [
"MIT"
] | 112 | 9c221223d1878bc24f0f39ad34928c1bb2974ae3 | https://github.com/doudoulaile/RL-GAN-Net/tree/9c221223d1878bc24f0f39ad34928c1bb2974ae3 | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
return 1.78718727865 * (x * torch.sigmoid(x) - 0.20662096414)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
d... |
GNNLayer | # 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
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
class GNNLayer(Module):
def __init__(self, in_features, out_features):
super(GNNLayer, self).__init__()
self.in_features = in_features
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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... | drzhang3/SDCN | GNNLayer | false | 15,229 | [
"Apache-2.0"
] | 146 | 3d11365bcb4af2cbe9625362737f1224aeea3b72 | https://github.com/drzhang3/SDCN/tree/3d11365bcb4af2cbe9625362737f1224aeea3b72 | from torch.nn import Module
import torch
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
class Model(Module):
def __init__(self, in_features, out_features):
super().__init__()
self.in_features = in_features
self.out_featu... |
RGBDiff | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch import nn
from torchvision import models as models
import torch.onnx
import torch.nn
class RGBDiff(nn.Module):
def __init__(self, dim=1):
super().__init__()
self.dim = dim
def forward(self, image):
"""
Args:
image (torch.Tensor): (N x T 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 import nn
from torchvision import models as models
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards... | dqawami/openvino_training_extensions | RGBDiff | false | 15,230 | [
"Apache-2.0"
] | 256 | dddda1dfd651eaae2d59cecda84275b1b03bd0ad | https://github.com/dqawami/openvino_training_extensions/tree/dddda1dfd651eaae2d59cecda84275b1b03bd0ad | import torch
from torch import nn
from torchvision import models as models
import torch.onnx
import torch.nn
class Model(nn.Module):
def __init__(self, dim=1):
super().__init__()
self.dim = dim
def forward(self, image):
"""
Args:
image (torch.Tensor): (N x T x C ... |
StddevLayer | # 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 StddevLayer(nn.Module):
def __init__(self, group_size=4, num_new_features=1):
super().__init__()
self.group_size = 4
self.num_new_features = 1
def forward(self, x):
b, c, h, w = x.shape
group_size = min(self.group_size, b)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | dubtor/EditGAN-Robert | StddevLayer | false | 15,231 | [
"BSD-2-Clause"
] | 110 | 8e6d80e7647c3536827f11cf0a9abf51c42794b2 | https://github.com/dubtor/EditGAN-Robert/tree/8e6d80e7647c3536827f11cf0a9abf51c42794b2 | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, group_size=4, num_new_features=1):
super().__init__()
self.group_size = 4
self.num_new_features = 1
def forward(self, x):
b, c, h, w = x.shape
group_size = min(self.group_size, b)
y =... |
Actor | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions import Normal
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class Actor(nn.Module):
"""Actor (Policy) Model."""
def __init__... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
import tor... | drib861204/Soft-Actor-Critic-and-Extensions | Actor | false | 15,232 | [
"MIT"
] | 143 | 3075df7430c1c49177b3798d753a9e3f6226672e | https://github.com/drib861204/Soft-Actor-Critic-and-Extensions/tree/3075df7430c1c49177b3798d753a9e3f6226672e | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions import Normal
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class Model(nn.Module):
"""Actor (Policy) Model."""
def __init__... |
PositionWiseFeedForward | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch.nn import functional as F
from torch import nn
from torchvision import models as models
import torch.onnx
import torch.nn
class GatedLinearUnit(nn.Module):
def __init__(self, input_size, output_size, dropout=0):
super().__init__()
self.dropout = nn.Dropout(dropout)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch.nn impor... | dqawami/openvino_training_extensions | PositionWiseFeedForward | false | 15,233 | [
"Apache-2.0"
] | 256 | dddda1dfd651eaae2d59cecda84275b1b03bd0ad | https://github.com/dqawami/openvino_training_extensions/tree/dddda1dfd651eaae2d59cecda84275b1b03bd0ad | import torch
from torch.nn import functional as F
from torch import nn
from torchvision import models as models
import torch.onnx
import torch.nn
class GatedLinearUnit(nn.Module):
def __init__(self, input_size, output_size, dropout=0):
super().__init__()
self.dropout = nn.Dropout(dropout)
... |
PositionwiseFeedForward | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
from torchvision import models as models
import torch.onnx
import torch.nn
class Identity(nn.Module):
def forward(self, input_):
return input_
class LayerNormalization(nn.Module):
""" Layer normalization module """
def __init__(self, d_hid, eps=0.001):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | dqawami/openvino_training_extensions | PositionwiseFeedForward | false | 15,234 | [
"Apache-2.0"
] | 256 | dddda1dfd651eaae2d59cecda84275b1b03bd0ad | https://github.com/dqawami/openvino_training_extensions/tree/dddda1dfd651eaae2d59cecda84275b1b03bd0ad | import torch
from torch import nn
from torchvision import models as models
import torch.onnx
import torch.nn
class Identity(nn.Module):
def forward(self, input_):
return input_
class LayerNormalization(nn.Module):
""" Layer normalization module """
def __init__(self, d_hid, eps=0.001):
... |
C3D | # 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 random
import torch
import torchvision
import torch.nn.parallel
import torch.optim
from torch import nn
class GroupMultiScaleCrop(object):
def __init__(self, input_size, scales=None, max_distort=1, fix_crop=
True, more_fix_crop=True):
self.scales = scales if scales is not None else [1, 875... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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 random
import torchvis... | coderSkyChen/Action_Recognition_Zoo | C3D | false | 15,235 | [
"MIT"
] | 240 | 92ec5ec3efeee852aec5c057798298cd3a8e58ae | https://github.com/coderSkyChen/Action_Recognition_Zoo/tree/92ec5ec3efeee852aec5c057798298cd3a8e58ae | import random
import torch
import torchvision
import torch.nn.parallel
import torch.optim
from torch import nn
class GroupMultiScaleCrop(object):
def __init__(self, input_size, scales=None, max_distort=1, fix_crop=
True, more_fix_crop=True):
self.scales = scales if scales is not None else [1, 875... |
DeepCritic | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class DeepCritic(nn.Module):
"""Critic (Value) Model."""
def __init__(self, state_size, action_size, se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
import tor... | drib861204/Soft-Actor-Critic-and-Extensions | DeepCritic | false | 15,236 | [
"MIT"
] | 143 | 3075df7430c1c49177b3798d753a9e3f6226672e | https://github.com/drib861204/Soft-Actor-Critic-and-Extensions/tree/3075df7430c1c49177b3798d753a9e3f6226672e | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class Model(nn.Module):
"""Critic (Value) Model."""
def __init__(self, state_size, action_size, seed, d... |
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... | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class Critic(nn.Module):
"""Critic (Value) Model."""
def __init__(self, state_size, action_size, seed, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
import tor... | drib861204/Soft-Actor-Critic-and-Extensions | Critic | false | 15,237 | [
"MIT"
] | 143 | 3075df7430c1c49177b3798d753a9e3f6226672e | https://github.com/drib861204/Soft-Actor-Critic-and-Extensions/tree/3075df7430c1c49177b3798d753a9e3f6226672e | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class Model(nn.Module):
"""Critic (Value) Model."""
def __init__(self, state_size, action_size, seed, d... |
SubNet | # 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 SubNet(nn.Module):
"""
The subnetwork that is used in TFN for video and audio in the pre-fusion stage
"""
def __init__(self, in_size, hidden_size, n_class, dropout, modal_name=
'text'):
"""
Args:
in_size: input dimension
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | dumpmemory/Multimodal-Infomax | SubNet | false | 15,238 | [
"MIT"
] | 57 | 9a6dc8f2bfa861cd447ba65c6a037cd7dd24f473 | https://github.com/dumpmemory/Multimodal-Infomax/tree/9a6dc8f2bfa861cd447ba65c6a037cd7dd24f473 | import torch
import torch.nn as nn
class Model(nn.Module):
"""
The subnetwork that is used in TFN for video and audio in the pre-fusion stage
"""
def __init__(self, in_size, hidden_size, n_class, dropout, modal_name=
'text'):
"""
Args:
in_size: input dimension
... |
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
from torch import nn
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 = image.shape
noise = im... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | dubtor/EditGAN-Robert | CondInjection | false | 15,239 | [
"BSD-2-Clause"
] | 110 | 8e6d80e7647c3536827f11cf0a9abf51c42794b2 | https://github.com/dubtor/EditGAN-Robert/tree/8e6d80e7647c3536827f11cf0a9abf51c42794b2 | import torch
from torch import nn
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.shape
noise = image.new_... |
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
from torch import nn
class DiceLoss(nn.Module):
def __init__(self, epsilon=1e-09):
"""Dice-Loss, 切块损失, 用于不均衡数据, 但是收敛困难, 不太稳定
paper: Dice Loss for Data-imbalanced NLP Tasks
url: https://arxiv.org/pdf/1911.02855.pdf
args:
reduction: str, Specifies the reduct... | 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... | dumpmemory/Pytorch-NLU | DiceLoss | false | 15,240 | [
"Apache-2.0"
] | 115 | 864fb9acc7751fc51abd3d05d24b5a9a7eab7110 | https://github.com/dumpmemory/Pytorch-NLU/tree/864fb9acc7751fc51abd3d05d24b5a9a7eab7110 | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, epsilon=1e-09):
"""Dice-Loss, 切块损失, 用于不均衡数据, 但是收敛困难, 不太稳定
paper: Dice Loss for Data-imbalanced NLP Tasks
url: https://arxiv.org/pdf/1911.02855.pdf
args:
reduction: str, Specifies the reduction... |
LayerNormLSTMCell | # 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.nn.parallel
import torch.utils.data
class LayerNormLSTMCell(nn.LSTMCell):
def __init__(self, input_size, hidden_size, bias=True):
super().__init__(input_size, hidden_size, bias)
self.ln_ih = nn.LayerNorm(4 * hidden_si... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | drgripa1/deepvecfont | LayerNormLSTMCell | false | 15,241 | [
"MIT"
] | 68 | a44d81ba19a22e43b4e576cd8ebc5c2fd961a621 | https://github.com/drgripa1/deepvecfont/tree/a44d81ba19a22e43b4e576cd8ebc5c2fd961a621 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.utils.data
class Model(nn.LSTMCell):
def __init__(self, input_size, hidden_size, bias=True):
super().__init__(input_size, hidden_size, bias)
self.ln_ih = nn.LayerNorm(4 * hidden_size)
... |
FocalLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch import nn
class FocalLoss(nn.Module):
def __init__(self, alpha=0.5, gamma=2, reduction='mean'):
"""FocalLoss
聚焦损失, 不确定的情况下alpha==0.5效果可能会好一点
url: https://github.com/CoinCheung/pytorch-loss
Usage is same as nn.BCEWithLogits:
>>> loss = criteria(log... | 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 ... | dumpmemory/Pytorch-NLU | FocalLoss | false | 15,242 | [
"Apache-2.0"
] | 115 | 864fb9acc7751fc51abd3d05d24b5a9a7eab7110 | https://github.com/dumpmemory/Pytorch-NLU/tree/864fb9acc7751fc51abd3d05d24b5a9a7eab7110 | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, alpha=0.5, gamma=2, reduction='mean'):
"""FocalLoss
聚焦损失, 不确定的情况下alpha==0.5效果可能会好一点
url: https://github.com/CoinCheung/pytorch-loss
Usage is same as nn.BCEWithLogits:
>>> loss = criteria(logits,... |
CecaModule | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
import torch.utils.data
import torchvision.transforms.functional as F
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
from torch import optim as optim
class CecaModule(nn.Module):
"""Constructs a circular ECA module.
ECA module where the conv uses circu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.utils.data
import torch.nn as nn
import torch.nn.parall... | dumpmemory/NonDeepNetworks | CecaModule | false | 15,243 | [
"BSD-3-Clause"
] | 307 | 5513bf588f4e64c99583440507232675c2e21e34 | https://github.com/dumpmemory/NonDeepNetworks/tree/5513bf588f4e64c99583440507232675c2e21e34 | import math
import torch
import torch.utils.data
import torchvision.transforms.functional as F
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
from torch import optim as optim
class Model(nn.Module):
"""Constructs a circular ECA module.
ECA module where the conv uses circular p... |
AE | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.nn import Linear
class AE(nn.Module):
def __init__(self, n_enc_1, n_enc_2, n_enc_3, n_dec_1, n_dec_2, n_dec_3,
n_input, n_z):
super(AE, self).__init__()
self.enc_1 = Linear(n_input, n_enc_1)
self.enc_2 =... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from to... | drzhang3/SDCN | AE | false | 15,244 | [
"Apache-2.0"
] | 146 | 3d11365bcb4af2cbe9625362737f1224aeea3b72 | https://github.com/drzhang3/SDCN/tree/3d11365bcb4af2cbe9625362737f1224aeea3b72 | import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.nn import Linear
class Model(nn.Module):
def __init__(self, n_enc_1, n_enc_2, n_enc_3, n_dec_1, n_dec_2, n_dec_3,
n_input, n_z):
super().__init__()
self.enc_1 = Linear(n_input, n_enc_1)
self.enc_2 = Line... |
ConvSqu | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.parallel
from torch import optim as optim
def autopad(k, p=None):
if p is None:
p = k // 2 if isinstance(k, int) else [(x // 2) for x in k]
return p
class ConvSqu(nn.Module):
def __init__(self, c1, c2, k=1, s=1, p=None, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch.nn as nn
import torch.nn.parallel
from torc... | dumpmemory/NonDeepNetworks | ConvSqu | false | 15,245 | [
"BSD-3-Clause"
] | 307 | 5513bf588f4e64c99583440507232675c2e21e34 | https://github.com/dumpmemory/NonDeepNetworks/tree/5513bf588f4e64c99583440507232675c2e21e34 | import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.parallel
from torch import optim as optim
def autopad(k, p=None):
if p is None:
p = k // 2 if isinstance(k, int) else [(x // 2) for x in k]
return p
class Model(nn.Module):
def __init__(self, c1, c2, k=1, s=1, p=None, g=... |
DeepActor | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions import Normal
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class DeepActor(nn.Module):
"""Actor (Policy) Model."""
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
import numpy as np
import tor... | drib861204/Soft-Actor-Critic-and-Extensions | DeepActor | false | 15,246 | [
"MIT"
] | 143 | 3075df7430c1c49177b3798d753a9e3f6226672e | https://github.com/drib861204/Soft-Actor-Critic-and-Extensions/tree/3075df7430c1c49177b3798d753a9e3f6226672e | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions import Normal
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class Model(nn.Module):
"""Actor (Policy) Model."""
def __init__... |
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
import torch.utils.cpp_extension
class AdaILN(nn.Module):
def __init__(self, channels, resl, eps=1e-08):
super().__init__()
self.rho = nn.Parameter(torch.Tensor(1, channels, 1, 1))
self.rho.data.fill_(1.0)
self.instance_norm = nn.InstanceNorm2d(c... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.utils.cpp_extension
assert_size_stride = tor... | STomoya/animeface | AdaILN | false | 15,247 | [
"MIT"
] | 61 | 37b3cd26097d7874559d4c152e41e5712b7a1a42 | https://github.com/STomoya/animeface/tree/37b3cd26097d7874559d4c152e41e5712b7a1a42 | import torch
import torch.nn as nn
import torch.utils.cpp_extension
class Model(nn.Module):
def __init__(self, channels, resl, eps=1e-08):
super().__init__()
self.rho = nn.Parameter(torch.Tensor(1, channels, 1, 1))
self.rho.data.fill_(1.0)
self.instance_norm = nn.InstanceNorm2d(ch... |
DiceLossV1 | # 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 DiceLossV1(nn.Module):
def __init__(self, reduction='mean', epsilon=1e-09):
"""【ERROR, 不收敛-原因未知】Dice-Loss, 切块损失, 用于不均衡数据, 但是收敛困难
paper: Dice Loss for Data-imbalanced NLP Tasks
url: https://arxiv.org/pdf/1911.02855.pdf
args:
reduc... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | dumpmemory/Pytorch-NLU | DiceLossV1 | false | 15,248 | [
"Apache-2.0"
] | 115 | 864fb9acc7751fc51abd3d05d24b5a9a7eab7110 | https://github.com/dumpmemory/Pytorch-NLU/tree/864fb9acc7751fc51abd3d05d24b5a9a7eab7110 | import torch
from torch import nn
class Model(nn.Module):
def __init__(self, reduction='mean', epsilon=1e-09):
"""【ERROR, 不收敛-原因未知】Dice-Loss, 切块损失, 用于不均衡数据, 但是收敛困难
paper: Dice Loss for Data-imbalanced NLP Tasks
url: https://arxiv.org/pdf/1911.02855.pdf
args:
reduction:... |
HighwayLayer | # 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.utils.data
import torch.utils.data.distributed
import torch.utils.checkpoint
import torch.utils.tensorboard
def my_xavier_init(m, gain=1):
"""Xavier initialization: weights initialization that tries to make variance of outputs
of a layer equal to variance of its ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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... | ali-senguel/fairo | HighwayLayer | false | 15,249 | [
"MIT"
] | 669 | 1ec5d8ecbdfc782de63a92aad9bf8534110ce762 | https://github.com/ali-senguel/fairo/tree/1ec5d8ecbdfc782de63a92aad9bf8534110ce762 | import torch
from torch import nn
import torch.utils.data
import torch.utils.data.distributed
import torch.utils.checkpoint
import torch.utils.tensorboard
def my_xavier_init(m, gain=1):
"""Xavier initialization: weights initialization that tries to make variance of outputs
of a layer equal to variance of its ... |
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