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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
EPE | # 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 EPE(nn.Module):
def __init__(self):
super(EPE, self).__init__()
def forward(self, flow, gt, loss_mask):
loss_map = (flow - gt.detach()) ** 2
loss_map = (loss_map.sum(1, True) + 1e-06) ** 0.5
return loss_map * loss_mask
def get_inputs... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | Entangled-Others-Studio/arXiv2020-RIFE | EPE | false | 9,075 | [
"MIT"
] | 0 | 4cd37527876b19f2eb357385eb5e9167545450af | https://github.com/Entangled-Others-Studio/arXiv2020-RIFE/tree/4cd37527876b19f2eb357385eb5e9167545450af | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, flow, gt, loss_mask):
loss_map = (flow - gt.detach()) ** 2
loss_map = (loss_map.sum(1, True) + 1e-06) ** 0.5
return loss_map * loss_mask
def get_inputs():
... |
Encoder | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
class Encoder(nn.Module):
def __init__(self, out_dim=64):
super(Encoder, self).__init__()
self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | GuohongLi/simclr-pytorch | Encoder | false | 9,076 | [
"BSD-3-Clause"
] | 0 | 7e08b2433a623fdbc1c097402fded4cc69d1b54e | https://github.com/GuohongLi/simclr-pytorch/tree/7e08b2433a623fdbc1c097402fded4cc69d1b54e | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, out_dim=64):
super().__init__()
self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)
self.c... |
TransitionUp | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn
import torch.nn.functional as F
import torch.nn as nn
class TransitionUp(nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
def forward(self, x, skip, concat=True):
out = F.interpolate(x, size=(skip.size(2), skip.size(3)), mode=
... | 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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | FUTUREEEEEE/FCHarDNet | TransitionUp | false | 9,077 | [
"MIT"
] | 0 | fc4b854b5cfa01a449bcfaece6bb3c32d84d9e2b | https://github.com/FUTUREEEEEE/FCHarDNet/tree/fc4b854b5cfa01a449bcfaece6bb3c32d84d9e2b | import torch
import torch.nn
import torch.nn.functional as F
import torch.nn as nn
class Model(nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
def forward(self, x, skip, concat=True):
out = F.interpolate(x, size=(skip.size(2), skip.size(3)), mode=
'b... |
SCRM | # 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 SCRM(nn.Module):
"""
spatial & channel wise relation loss
"""
def __init__(self, gamma=0.1):
super(SCRM, self).__init__()
self.softmax = nn.Softmax(dim=-1)
self.gamma = gamma
def spatial_wise(self, x... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | DemoAuguste/ZAQ-code | SCRM | false | 9,078 | [
"MIT"
] | 0 | 9986a2d217ab5cb284e08c062f8726cabacb311e | https://github.com/DemoAuguste/ZAQ-code/tree/9986a2d217ab5cb284e08c062f8726cabacb311e | import torch
import torch.nn.functional as F
import torch.nn as nn
class Model(nn.Module):
"""
spatial & channel wise relation loss
"""
def __init__(self, gamma=0.1):
super().__init__()
self.softmax = nn.Softmax(dim=-1)
self.gamma = gamma
def spatial_wise(self, x):
... |
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
class Critic(nn.Module):
def __init__(self, hidden_size, action, num_inputs, spp_num_outputs,
data_width=8):
super(Critic, self).__init__()
self.action = action
self.num_outputs = self.action.shape[0]
self.num_inputs = num_inputs
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | GraceYYJ/cbx-k | Critic | false | 9,079 | [
"MIT"
] | 0 | 1a955bc8d1675b8024763218482372dca982cc6c | https://github.com/GraceYYJ/cbx-k/tree/1a955bc8d1675b8024763218482372dca982cc6c | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, hidden_size, action, num_inputs, spp_num_outputs,
data_width=8):
super().__init__()
self.action = action
self.num_outputs = self.action.shape[0]
self.num_inputs = num_inputs
self.spp_data... |
Decoder | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
from torch import nn
import torch.hub
def overlap_and_add(signal, frame_step):
outer_dimensions = signal.size()[:-2]
frames, frame_length = signal.size()[-2:]
subframe_length = math.gcd(frame_length, frame_step)
subframe_step = frame_step // subframe_length
subframes_per_f... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
from torch import nn
import torch.hub
assert_size_stride = torch._C.... | FindingBen/demucs-copy | Decoder | false | 9,080 | [
"MIT"
] | 0 | b607e9c91b776eb03bf95a2aa9c4900c92fc7c3f | https://github.com/FindingBen/demucs-copy/tree/b607e9c91b776eb03bf95a2aa9c4900c92fc7c3f | import math
import torch
from torch import nn
import torch.hub
def overlap_and_add(signal, frame_step):
outer_dimensions = signal.size()[:-2]
frames, frame_length = signal.size()[-2:]
subframe_length = math.gcd(frame_length, frame_step)
subframe_step = frame_step // subframe_length
subframes_per_f... |
TLU | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch import nn
from torch.nn import Parameter
from torch.nn.parameter import Parameter
class TLU(nn.Module):
def __init__(self, num_features):
"""max(y, tau) = max(y - tau, 0) + tau = ReLU(y - tau) + tau"""
super(TLU, self).__init__()
self.num_features = num_features
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
from torch.nn import Parameter
from torch.nn.parameter import Parame... | DengpanFu/fast-reid-v0 | TLU | false | 9,081 | [
"Apache-2.0"
] | 0 | e444c0187ccb6ef3b8348f8c5f0c5a0814b3683e | https://github.com/DengpanFu/fast-reid-v0/tree/e444c0187ccb6ef3b8348f8c5f0c5a0814b3683e | import torch
from torch import nn
from torch.nn import Parameter
from torch.nn.parameter import Parameter
class Model(nn.Module):
def __init__(self, num_features):
"""max(y, tau) = max(y - tau, 0) + tau = ReLU(y - tau) + tau"""
super().__init__()
self.num_features = num_features
s... |
PixelUnshuffle | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch import nn
import torch.utils.data
class PixelUnshuffle(nn.Module):
"""
Initialize: inplanes, planes, upscale_factor
OUTPUT: (planes // upscale_factor^2) * ht * wd
"""
def __init__(self, downscale_factor=2):
super(PixelUnshuffle, self).__init__()
self._r = d... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._... | HwangToeMat/tmp | PixelUnshuffle | false | 9,082 | [
"Apache-2.0"
] | 0 | a4f48443b16b5e07a9cf95f54651ade8c7669134 | https://github.com/HwangToeMat/tmp/tree/a4f48443b16b5e07a9cf95f54651ade8c7669134 | import torch
from torch import nn
import torch.utils.data
class Model(nn.Module):
"""
Initialize: inplanes, planes, upscale_factor
OUTPUT: (planes // upscale_factor^2) * ht * wd
"""
def __init__(self, downscale_factor=2):
super().__init__()
self._r = downscale_factor
def forw... |
AnyHead | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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 AnyHead(nn.Module):
"""AnyNet head."""
def __init__(self, w_in, nc):
super(AnyHead, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(w_in, nc, bias=True)
def forward(self, x):
x = self.avg_pool(x)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | DengpanFu/fast-reid-v0 | AnyHead | false | 9,083 | [
"Apache-2.0"
] | 0 | e444c0187ccb6ef3b8348f8c5f0c5a0814b3683e | https://github.com/DengpanFu/fast-reid-v0/tree/e444c0187ccb6ef3b8348f8c5f0c5a0814b3683e | import torch
from torch import nn
class Model(nn.Module):
"""AnyNet head."""
def __init__(self, w_in, nc):
super().__init__()
self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(w_in, nc, bias=True)
def forward(self, x):
x = self.avg_pool(x)
x = x.vie... |
BellMembFunc | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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
def _mk_param(val):
"""Make a torch parameter from a scalar value"""
if isinstance(val, torch.Tensor):
val = val.item()
return torch.nn.Parameter(torch.tensor(val, dtype=torch.float))
class BellMembFunc(torch.nn.Module):
"""
Generalised Bell membership function; defined ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | GradyKurpasi/anfis-pytorch | BellMembFunc | false | 9,084 | [
"MIT"
] | 0 | 4cce596193a8bc65e632405ca66d116c771033d7 | https://github.com/GradyKurpasi/anfis-pytorch/tree/4cce596193a8bc65e632405ca66d116c771033d7 | import torch
def _mk_param(val):
"""Make a torch parameter from a scalar value"""
if isinstance(val, torch.Tensor):
val = val.item()
return torch.nn.Parameter(torch.tensor(val, dtype=torch.float))
class Model(torch.nn.Module):
"""
Generalised Bell membership function; defined by thre... |
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
class TwoLayerNet(torch.nn.Module):
"""
From the pytorch examples, a simjple 2-layer neural net.
https://pytorch.org/tutorials/beginner/pytorch_with_examples.html
"""
def __init__(self, d_in, hidden_size, d_out):
super(TwoLayerNet, self).__init__()
self.linear... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C... | GradyKurpasi/anfis-pytorch | TwoLayerNet | false | 9,085 | [
"MIT"
] | 0 | 4cce596193a8bc65e632405ca66d116c771033d7 | https://github.com/GradyKurpasi/anfis-pytorch/tree/4cce596193a8bc65e632405ca66d116c771033d7 | import torch
class Model(torch.nn.Module):
"""
From the pytorch examples, a simjple 2-layer neural net.
https://pytorch.org/tutorials/beginner/pytorch_with_examples.html
"""
def __init__(self, d_in, hidden_size, d_out):
super().__init__()
self.linear1 = torch.nn.Linear(d_i... |
ModelBasic | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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 ModelBasic(nn.Module):
"""parallel passing of data, categorical output with one unit per number of clusters"""
def __init__(self, n_obs, n_units=100, n_timesteps=10, max_K=10):
super(ModelBasic, self).__init__()
self.fc_input = nn.Linear(2 * n_obs, n_u... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | HeikoSchuett/hard-inference | ModelBasic | false | 9,086 | [
"MIT"
] | 0 | eb850d97458dbbf8a5c434df71c802065c8e348f | https://github.com/HeikoSchuett/hard-inference/tree/eb850d97458dbbf8a5c434df71c802065c8e348f | import torch
import torch.nn as nn
class Model(nn.Module):
"""parallel passing of data, categorical output with one unit per number of clusters"""
def __init__(self, n_obs, n_units=100, n_timesteps=10, max_K=10):
super().__init__()
self.fc_input = nn.Linear(2 * n_obs, n_units)
self.fc... |
MNIST_CNN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class SqueezeLastTwo(nn.Module):
"""
A module which squeezes the last two dimensions,
ordinary squeeze can be a problem for batch size 1
"""
def __init__(self):
super(SqueezeLastTwo, 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 import triton_helpers
from torch._inductor.runtime.... | FrancescoCappio/swad | MNIST_CNN | false | 9,087 | [
"MIT"
] | 0 | b1da3eacb7dc3711360e6621ca16f2d75c4f411c | https://github.com/FrancescoCappio/swad/tree/b1da3eacb7dc3711360e6621ca16f2d75c4f411c | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class SqueezeLastTwo(nn.Module):
"""
A module which squeezes the last two dimensions,
ordinary squeeze can be a problem for batch size 1
"""
def __init__(self):
super().__init__()
def forward(s... |
CharbonnierLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.utils.data
import torch.nn as nn
class CharbonnierLoss(nn.Module):
"""Charbonnier Loss (L1)"""
def __init__(self, eps=1e-06):
super(CharbonnierLoss, self).__init__()
self.eps = eps
def forward(self, x, y):
diff = x - y
loss = torch.sum(torch.sqrt... | 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.utils.data
impo... | AbnerVictor/HCFlow | CharbonnierLoss | false | 9,088 | [
"Apache-2.0"
] | 0 | e55938ac9f58c117898e3d161ddc73b14d15289b | https://github.com/AbnerVictor/HCFlow/tree/e55938ac9f58c117898e3d161ddc73b14d15289b | import torch
import torch.utils.data
import torch.nn as nn
class Model(nn.Module):
"""Charbonnier Loss (L1)"""
def __init__(self, eps=1e-06):
super().__init__()
self.eps = eps
def forward(self, x, y):
diff = x - y
loss = torch.sum(torch.sqrt(diff * diff + self.eps))
... |
GaussMembFunc | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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
def _mk_param(val):
"""Make a torch parameter from a scalar value"""
if isinstance(val, torch.Tensor):
val = val.item()
return torch.nn.Parameter(torch.tensor(val, dtype=torch.float))
class GaussMembFunc(torch.nn.Module):
"""
Gaussian membership functions, defined by two... | 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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_str... | GradyKurpasi/anfis-pytorch | GaussMembFunc | false | 9,089 | [
"MIT"
] | 0 | 4cce596193a8bc65e632405ca66d116c771033d7 | https://github.com/GradyKurpasi/anfis-pytorch/tree/4cce596193a8bc65e632405ca66d116c771033d7 | import torch
def _mk_param(val):
"""Make a torch parameter from a scalar value"""
if isinstance(val, torch.Tensor):
val = val.item()
return torch.nn.Parameter(torch.tensor(val, dtype=torch.float))
class Model(torch.nn.Module):
"""
Gaussian membership functions, defined by two paramet... |
rSoftMax | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn.functional as F
from torch import nn
class rSoftMax(nn.Module):
def __init__(self, radix, cardinality):
super().__init__()
self.radix = radix
self.cardinality = cardinality
def forward(self, x):
batch = x.size(0)
if self.radix > 1:
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
a... | DengpanFu/fast-reid-v0 | rSoftMax | false | 9,090 | [
"Apache-2.0"
] | 0 | e444c0187ccb6ef3b8348f8c5f0c5a0814b3683e | https://github.com/DengpanFu/fast-reid-v0/tree/e444c0187ccb6ef3b8348f8c5f0c5a0814b3683e | import torch
import torch.nn.functional as F
from torch import nn
class Model(nn.Module):
def __init__(self, radix, cardinality):
super().__init__()
self.radix = radix
self.cardinality = cardinality
def forward(self, x):
batch = x.size(0)
if self.radix > 1:
... |
ConvModule | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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.distributed
from torch import nn
import torch.utils.data
class ConvModule(nn.Module):
def __init__(self, input_dim, kernel_size, dropout_rate, causal=False):
super(ConvModule, self).__init__()
self.layer_norm = nn.LayerNorm(input_dim)
self.pw_conv_1 = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Five-Hundred-Years-Ago/StreamingTransformer | ConvModule | false | 9,091 | [
"Apache-2.0"
] | 0 | fdaace64ed786bbdaeea2b9f44e96f9403ef98fe | https://github.com/Five-Hundred-Years-Ago/StreamingTransformer/tree/fdaace64ed786bbdaeea2b9f44e96f9403ef98fe | import torch
import torch.utils.data.distributed
from torch import nn
import torch.utils.data
class Model(nn.Module):
def __init__(self, input_dim, kernel_size, dropout_rate, causal=False):
super().__init__()
self.layer_norm = nn.LayerNorm(input_dim)
self.pw_conv_1 = nn.Conv2d(1, 2, 1, 1,... |
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 torch.nn as nn
class Actor(nn.Module):
def __init__(self, hidden_size, action, num_inputs, num_output,
spp_num_outputs=[1, 2, 4], data_width=8):
super(Actor, self).__init__()
self.action = action
self.num_inputs = num_inputs
self.num_outputs = num_outpu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 ... | GraceYYJ/cbx-k | Actor | false | 9,092 | [
"MIT"
] | 0 | 1a955bc8d1675b8024763218482372dca982cc6c | https://github.com/GraceYYJ/cbx-k/tree/1a955bc8d1675b8024763218482372dca982cc6c | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, hidden_size, action, num_inputs, num_output,
spp_num_outputs=[1, 2, 4], data_width=8):
super().__init__()
self.action = action
self.num_inputs = num_inputs
self.num_outputs = num_output
s... |
Quantization | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.utils.data
import torch.nn as nn
class Quant(torch.autograd.Function):
@staticmethod
def forward(ctx, input):
input = torch.clamp(input, 0, 1)
output = (input * 255.0).round() / 255.0
return output
@staticmethod
def backward(ctx, grad_output):
... | 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.utils.data
impo... | AbnerVictor/HCFlow | Quantization | false | 9,093 | [
"Apache-2.0"
] | 0 | e55938ac9f58c117898e3d161ddc73b14d15289b | https://github.com/AbnerVictor/HCFlow/tree/e55938ac9f58c117898e3d161ddc73b14d15289b | import torch
import torch.utils.data
import torch.nn as nn
class Quant(torch.autograd.Function):
@staticmethod
def forward(ctx, input):
input = torch.clamp(input, 0, 1)
output = (input * 255.0).round() / 255.0
return output
@staticmethod
def backward(ctx, grad_output):
... |
MultiLayeredConv1d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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.distributed
import torch.utils.data
class MultiLayeredConv1d(torch.nn.Module):
"""Multi-layered conv1d for Transformer block.
This is a module of multi-leyered conv1d designed
to replace positionwise feed-forward network
in Transforner block, which is introduced 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
from torch._inductor.runtime import triton_helpers
import torch.utils.data.distr... | Five-Hundred-Years-Ago/StreamingTransformer | MultiLayeredConv1d | false | 9,094 | [
"Apache-2.0"
] | 0 | fdaace64ed786bbdaeea2b9f44e96f9403ef98fe | https://github.com/Five-Hundred-Years-Ago/StreamingTransformer/tree/fdaace64ed786bbdaeea2b9f44e96f9403ef98fe | import torch
import torch.utils.data.distributed
import torch.utils.data
class Model(torch.nn.Module):
"""Multi-layered conv1d for Transformer block.
This is a module of multi-leyered conv1d designed
to replace positionwise feed-forward network
in Transforner block, which is introduced in
`FastSp... |
SplAtConv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import logging
import torch
import torch.nn.functional as F
from torch import nn
from torch.nn import ReLU
from torch.nn import Conv2d
from torch.nn.modules.utils import _pair
def get_norm(norm, out_channels, num_splits=1, **kwargs):
"""
Args:
norm (str or callable):
Returns:
nn.Module or ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | DengpanFu/fast-reid-v0 | SplAtConv2d | false | 9,095 | [
"Apache-2.0"
] | 0 | e444c0187ccb6ef3b8348f8c5f0c5a0814b3683e | https://github.com/DengpanFu/fast-reid-v0/tree/e444c0187ccb6ef3b8348f8c5f0c5a0814b3683e | import logging
import torch
import torch.nn.functional as F
from torch import nn
from torch.nn import ReLU
from torch.nn import Conv2d
from torch.nn.modules.utils import _pair
def get_norm(norm, out_channels, num_splits=1, **kwargs):
"""
Args:
norm (str or callable):
Returns:
nn.Module or ... |
TwoLayerFCBodyWithAction | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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.optim
import torch.nn as nn
import torch.nn.functional as F
def layer_init(layer, w_scale=1.0):
nn.init.orthogonal_(layer.weight.data)
layer.weight.data.mul_(w_scale)
nn.init.constant_(layer.bias.data, 0)
return layer
class TwoLayerFCBodyWithAction(nn.Module):
def __in... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.optim
import tor... | DMIU-ShELL/deeprl-shell | TwoLayerFCBodyWithAction | false | 9,096 | [
"Apache-2.0"
] | 0 | a7845ab1c4967ba2af9486625086c3d0b176d293 | https://github.com/DMIU-ShELL/deeprl-shell/tree/a7845ab1c4967ba2af9486625086c3d0b176d293 | import torch
import torch.optim
import torch.nn as nn
import torch.nn.functional as F
def layer_init(layer, w_scale=1.0):
nn.init.orthogonal_(layer.weight.data)
layer.weight.data.mul_(w_scale)
nn.init.constant_(layer.bias.data, 0)
return layer
class Model(nn.Module):
def __init__(self, state_di... |
cls_pos | # 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 cls_pos(nn.Module):
def __init__(self):
super(cls_pos, self).__init__()
self.bce = nn.BCEWithLogitsLoss(reduction='none')
def forward(self, pos_pred, pos_label):
log_loss = self.bce(pos_pred[:, 0, :, :], pos_label[:, 2, :, :])
pos_pred... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | FrancesC0de/Pedestron | cls_pos | false | 9,097 | [
"Apache-2.0"
] | 0 | 9ef6a408f97f8c8af98096b7945df18c9d3656ca | https://github.com/FrancesC0de/Pedestron/tree/9ef6a408f97f8c8af98096b7945df18c9d3656ca | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
self.bce = nn.BCEWithLogitsLoss(reduction='none')
def forward(self, pos_pred, pos_label):
log_loss = self.bce(pos_pred[:, 0, :, :], pos_label[:, 2, :, :])
pos_pred = pos_pred.sig... |
Conv_Blocks | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class Conv_Blocks(nn.Module):
def __init__(self, input_dim, output_dim, filter_size=3, batch_norm=
False, non_lin='tanh', dropout=0.0, first_block=False, last_block=
False, skip_connection=False):
super(Conv_Blocks, self).__init__()
self.skip_con... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | HRHLALALA/GoalGAN | Conv_Blocks | false | 9,098 | [
"MIT"
] | 0 | 01443f2a578333a0d5ab3a449bc7da69f5023190 | https://github.com/HRHLALALA/GoalGAN/tree/01443f2a578333a0d5ab3a449bc7da69f5023190 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, input_dim, output_dim, filter_size=3, batch_norm=
False, non_lin='tanh', dropout=0.0, first_block=False, last_block=
False, skip_connection=False):
super().__init__()
self.skip_connection = skip_connecti... |
MySimpleNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn.functional as F
from torch import nn
class MySimpleNet(nn.Module):
"""
Very simple 2-layer net, slightly adapted from the docs:
https://skorch.readthedocs.io/en/stable/user/quickstart.html
"""
def __init__(self, num_in, num_feat, num_hidden=10, nonlin=F.re... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | GradyKurpasi/anfis-pytorch | MySimpleNet | false | 9,099 | [
"MIT"
] | 0 | 4cce596193a8bc65e632405ca66d116c771033d7 | https://github.com/GradyKurpasi/anfis-pytorch/tree/4cce596193a8bc65e632405ca66d116c771033d7 | import torch
import torch.nn.functional as F
from torch import nn
class Model(nn.Module):
"""
Very simple 2-layer net, slightly adapted from the docs:
https://skorch.readthedocs.io/en/stable/user/quickstart.html
"""
def __init__(self, num_in, num_feat, num_hidden=10, nonlin=F.relu):
... |
offset_pos | # 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 offset_pos(nn.Module):
def __init__(self):
super(offset_pos, self).__init__()
self.smoothl1 = nn.SmoothL1Loss(reduction='none')
def forward(self, offset_pred, offset_label):
l1_loss = offset_label[:, 2, :, :].unsqueeze(dim=1) * self.smoothl1(
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | FrancesC0de/Pedestron | offset_pos | false | 9,100 | [
"Apache-2.0"
] | 0 | 9ef6a408f97f8c8af98096b7945df18c9d3656ca | https://github.com/FrancesC0de/Pedestron/tree/9ef6a408f97f8c8af98096b7945df18c9d3656ca | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
self.smoothl1 = nn.SmoothL1Loss(reduction='none')
def forward(self, offset_pred, offset_label):
l1_loss = offset_label[:, 2, :, :].unsqueeze(dim=1) * self.smoothl1(
offset_pr... |
reg_hw_pos | # 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 reg_hw_pos(nn.Module):
def __init__(self):
super(reg_hw_pos, self).__init__()
self.smoothl1 = nn.SmoothL1Loss(reduction='none')
def forward(self, h_pred, h_label):
l1_loss = h_label[:, 2, :, :] * self.smoothl1(h_pred[:, 0, :, :] /
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | FrancesC0de/Pedestron | reg_hw_pos | false | 9,101 | [
"Apache-2.0"
] | 0 | 9ef6a408f97f8c8af98096b7945df18c9d3656ca | https://github.com/FrancesC0de/Pedestron/tree/9ef6a408f97f8c8af98096b7945df18c9d3656ca | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
self.smoothl1 = nn.SmoothL1Loss(reduction='none')
def forward(self, h_pred, h_label):
l1_loss = h_label[:, 2, :, :] * self.smoothl1(h_pred[:, 0, :, :] /
(h_label[:, 0, :, :] ... |
reg_pos | # 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 reg_pos(nn.Module):
def __init__(self):
super(reg_pos, self).__init__()
self.smoothl1 = nn.SmoothL1Loss(reduction='none')
def forward(self, h_pred, h_label):
l1_loss = h_label[:, 1, :, :] * self.smoothl1(h_pred[:, 0, :, :] /
(h_lab... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | FrancesC0de/Pedestron | reg_pos | false | 9,102 | [
"Apache-2.0"
] | 0 | 9ef6a408f97f8c8af98096b7945df18c9d3656ca | https://github.com/FrancesC0de/Pedestron/tree/9ef6a408f97f8c8af98096b7945df18c9d3656ca | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
self.smoothl1 = nn.SmoothL1Loss(reduction='none')
def forward(self, h_pred, h_label):
l1_loss = h_label[:, 1, :, :] * self.smoothl1(h_pred[:, 0, :, :] /
(h_label[:, 0, :, :] ... |
UpConv_Blocks | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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_Blocks(nn.Module):
def __init__(self, input_dim, output_dim, filter=4, padding=1,
first_block=False, last_block=False, batch_norm=False, non_lin=
'relu', dropout=0, skip_connection=False):
super(UpConv_Blocks, self).__init__()
self.B... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | HRHLALALA/GoalGAN | UpConv_Blocks | false | 9,103 | [
"MIT"
] | 0 | 01443f2a578333a0d5ab3a449bc7da69f5023190 | https://github.com/HRHLALALA/GoalGAN/tree/01443f2a578333a0d5ab3a449bc7da69f5023190 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, input_dim, output_dim, filter=4, padding=1,
first_block=False, last_block=False, batch_norm=False, non_lin=
'relu', dropout=0, skip_connection=False):
super().__init__()
self.Block = nn.Sequential()
... |
Conv2dZeros | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.utils.data
import torch.nn as nn
class _ActNorm(nn.Module):
"""
Activation Normalization
Initialize the bias and scale with a given minibatch,
so that the output per-channel have zero mean and unit variance for that.
After initialization, `bias` and `logs` will be traine... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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.... | AbnerVictor/HCFlow | Conv2dZeros | false | 9,104 | [
"Apache-2.0"
] | 0 | e55938ac9f58c117898e3d161ddc73b14d15289b | https://github.com/AbnerVictor/HCFlow/tree/e55938ac9f58c117898e3d161ddc73b14d15289b | import torch
import torch.utils.data
import torch.nn as nn
class _ActNorm(nn.Module):
"""
Activation Normalization
Initialize the bias and scale with a given minibatch,
so that the output per-channel have zero mean and unit variance for that.
After initialization, `bias` and `logs` will be traine... |
SelfAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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 SelfAttention(nn.Module):
def __init__(self, hidden):
super(SelfAttention, self).__init__()
self.W = nn.Linear(hidden, 1)
def forward(self, x):
hidden = self.W(x)
scores = hidden.bmm(hidden.transpose(1, 2))
alpha = nn.functiona... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | IAMZn1018/ccks2021-entity-linking | SelfAttention | false | 9,105 | [
"Apache-2.0"
] | 0 | 6596b0b16d8c1fc4400c736b30ff46158d1575e4 | https://github.com/IAMZn1018/ccks2021-entity-linking/tree/6596b0b16d8c1fc4400c736b30ff46158d1575e4 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, hidden):
super().__init__()
self.W = nn.Linear(hidden, 1)
def forward(self, x):
hidden = self.W(x)
scores = hidden.bmm(hidden.transpose(1, 2))
alpha = nn.functional.softmax(scores, dim=-1)
... |
ResidualBlock_noBN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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.init as init
import torch.nn.functional as F
def initialize_weights(net_l, scale=1):
if not isinstance(net_l, list):
net_l = [net_l]
for net in net_l:
for m in net.modules():
if isinstance(m, nn.Conv2d):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
impor... | AbnerVictor/HCFlow | ResidualBlock_noBN | false | 9,106 | [
"Apache-2.0"
] | 0 | e55938ac9f58c117898e3d161ddc73b14d15289b | https://github.com/AbnerVictor/HCFlow/tree/e55938ac9f58c117898e3d161ddc73b14d15289b | import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.init as init
import torch.nn.functional as F
def initialize_weights(net_l, scale=1):
if not isinstance(net_l, list):
net_l = [net_l]
for net in net_l:
for m in net.modules():
if isinstance(m, nn.Conv2d):
... |
AverageAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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.cuda
import torch.distributed
class PositionwiseFeedForward(nn.Module):
""" A two-layer Feed-Forward-Network with residual layer norm.
Args:
d_model (int): the size of input for the first-layer of the FFN.
d_ff (int): the hidden layer size of th... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.cuda
import torch.distributed
assert_size_str... | GarrettNicolai/OpenNMT-py | AverageAttention | false | 9,108 | [
"MIT"
] | 0 | 9491d900ac1b50fe39da417bacc0b9d610331888 | https://github.com/GarrettNicolai/OpenNMT-py/tree/9491d900ac1b50fe39da417bacc0b9d610331888 | import torch
import torch.nn as nn
import torch.cuda
import torch.distributed
class PositionwiseFeedForward(nn.Module):
""" A two-layer Feed-Forward-Network with residual layer norm.
Args:
d_model (int): the size of input for the first-layer of the FFN.
d_ff (int): the hidden layer size of th... |
L2Norm | # 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 L2Norm(nn.Module):
def __init__(self, dim=1):
super().__init__()
self.dim = dim
def forward(self, x):
return F.normalize(x, p=2, dim=self.dim)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | Guido27/project_vg | L2Norm | false | 9,109 | [
"MIT"
] | 0 | 3322fc355742929f43f3d97204398035645d968c | https://github.com/Guido27/project_vg/tree/3322fc355742929f43f3d97204398035645d968c | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, dim=1):
super().__init__()
self.dim = dim
def forward(self, x):
return F.normalize(x, p=2, dim=self.dim)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_i... |
Attention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
class Attention(nn.Module):
def __init__(self, hidden):
super(Attention, self).__init__()
self.linear = nn.Linear(hidden, 1, bias=False)
def forward(self, x, mask=None):
weights = self.linear(x)
if mask is not None:
weights = wei... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | IAMZn1018/ccks2021-entity-linking | Attention | false | 9,110 | [
"Apache-2.0"
] | 0 | 6596b0b16d8c1fc4400c736b30ff46158d1575e4 | https://github.com/IAMZn1018/ccks2021-entity-linking/tree/6596b0b16d8c1fc4400c736b30ff46158d1575e4 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, hidden):
super().__init__()
self.linear = nn.Linear(hidden, 1, bias=False)
def forward(self, x, mask=None):
weights = self.linear(x)
if mask is not None:
weights = weights.mask_fill(mask... |
SparseConv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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 numpy as np
import torch.nn as nn
import torch.utils.data
import torch.nn.functional as F
import scipy.sparse as sparse
from torch.nn.modules.utils import _pair
class SparseConv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, k, rho_init,
rho_maxim... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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 numpy as np
import torch.nn as nn
import torch.utils.data
imp... | FaithfulZhening/CNN-FCF-CVPR-2019 | SparseConv2d | false | 9,111 | [
"Apache-2.0"
] | 0 | f65f6577feb4a2cdaed3fb60cb14b8840e25e19c | https://github.com/FaithfulZhening/CNN-FCF-CVPR-2019/tree/f65f6577feb4a2cdaed3fb60cb14b8840e25e19c | import math
import torch
import numpy as np
import torch.nn as nn
import torch.utils.data
import torch.nn.functional as F
import scipy.sparse as sparse
from torch.nn.modules.utils import _pair
class Model(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, k, rho_init,
rho_maximum, mu,... |
BasicBlock1 | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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 BasicBlock1(nn.Module):
def __init__(self, input_dim, output_dim):
super(BasicBlock1, self).__init__()
self.ID = input_dim
self.conv = nn.Conv2d(in_channels=input_dim, out_channels=
output_dim, kernel_size=1, padding=0, stride=1)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Houseqin/PytorchToCaffe | BasicBlock1 | false | 9,112 | [
"MIT"
] | 0 | e94224ba6414e76369f191e7e3d9731c12ce2bd7 | https://github.com/Houseqin/PytorchToCaffe/tree/e94224ba6414e76369f191e7e3d9731c12ce2bd7 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, input_dim, output_dim):
super().__init__()
self.ID = input_dim
self.conv = nn.Conv2d(in_channels=input_dim, out_channels=
output_dim, kernel_size=1, padding=0, stride=1)
self.relu = nn.ReLU()... |
FakeReLUM | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
class FakeReLU(torch.autograd.Function):
@staticmethod
def forward(ctx, input):
return input.clamp(min=0)
@staticmethod
def backward(ctx, grad_output):
return grad_output
class FakeReLUM(nn.Module):
def forward(self, x):
return FakeRe... | 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... | Jay-Roberts/FW-Perturbations | FakeReLUM | false | 9,113 | [
"MIT"
] | 0 | 0960f6116125307cc986f9f19b3c5ab4c15ed535 | https://github.com/Jay-Roberts/FW-Perturbations/tree/0960f6116125307cc986f9f19b3c5ab4c15ed535 | import torch
import torch.nn as nn
class FakeReLU(torch.autograd.Function):
@staticmethod
def forward(ctx, input):
return input.clamp(min=0)
@staticmethod
def backward(ctx, grad_output):
return grad_output
class Model(nn.Module):
def forward(self, x):
return FakeReLU.a... |
h_sigmoid | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.utils.data
import torch.nn as nn
class h_sigmoid(nn.Module):
def __init__(self, inplace=True):
super(h_sigmoid, self).__init__()
self.relu = nn.ReLU6(inplace=inplace)
def forward(self, x):
return self.relu(x + 3) / 6
def get_inputs():
return [torch.ran... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guard... | Ghaust/SSD | h_sigmoid | false | 9,114 | [
"MIT"
] | 0 | 2bf14a48795d20ad2177f622e84d62b3ff81183f | https://github.com/Ghaust/SSD/tree/2bf14a48795d20ad2177f622e84d62b3ff81183f | import torch
import torch.utils.data
import torch.nn as nn
class Model(nn.Module):
def __init__(self, inplace=True):
super().__init__()
self.relu = nn.ReLU6(inplace=inplace)
def forward(self, x):
return self.relu(x + 3) / 6
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
... |
RegressionModel | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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 RegressionModel(nn.Module):
def __init__(self, num_features_in, num_anchors=21, feature_size=256):
super(RegressionModel, self).__init__()
self.conv1 = nn.Conv2d(num_features_in, feature_size, kernel_size=(
3, 3), padding=1)
self.act1 =... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | HenryOsborne/Rotation | RegressionModel | false | 9,115 | [
"Apache-2.0"
] | 0 | 417fa90bcbb2a144f0c1d2ce5d9fc110f6617bf2 | https://github.com/HenryOsborne/Rotation/tree/417fa90bcbb2a144f0c1d2ce5d9fc110f6617bf2 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, num_features_in, num_anchors=21, feature_size=256):
super().__init__()
self.conv1 = nn.Conv2d(num_features_in, feature_size, kernel_size=(
3, 3), padding=1)
self.act1 = nn.ReLU()
self.conv2 =... |
GlobalAttentionGeneral | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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
def conv1x1(in_planes, out_planes, bias=False):
"""1x1 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=1,
padding=0, bias=bias)
class GlobalAttentionGeneral(nn.Module):
def __init__(self, idf, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Huy2122k/Project3-AttnGANwCLIP | GlobalAttentionGeneral | false | 9,116 | [
"MIT"
] | 0 | 3fb8c643bf71599e1606ec468e86373ccde1ed20 | https://github.com/Huy2122k/Project3-AttnGANwCLIP/tree/3fb8c643bf71599e1606ec468e86373ccde1ed20 | import torch
import torch.nn as nn
import torch.nn.parallel
def conv1x1(in_planes, out_planes, bias=False):
"""1x1 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=1,
padding=0, bias=bias)
class Model(nn.Module):
def __init__(self, idf, cdf):
sup... |
BothContextGate | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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.cuda
import torch.distributed
class ContextGate(nn.Module):
"""
Context gate is a decoder module that takes as input the previous word
embedding, the current decoder state and the attention state, and
produces a gate.
The gate can be used to select t... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | ESCM-summarization/ESCM-summary-evaluation | BothContextGate | false | 9,117 | [
"MIT"
] | 0 | 3780b51f0ed44cbbea3f163a871d875f1e5e9393 | https://github.com/ESCM-summarization/ESCM-summary-evaluation/tree/3780b51f0ed44cbbea3f163a871d875f1e5e9393 | import torch
import torch.nn as nn
import torch.cuda
import torch.distributed
class ContextGate(nn.Module):
"""
Context gate is a decoder module that takes as input the previous word
embedding, the current decoder state and the attention state, and
produces a gate.
The gate can be used to select t... |
SourceContextGate | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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.cuda
import torch.distributed
class ContextGate(nn.Module):
"""
Context gate is a decoder module that takes as input the previous word
embedding, the current decoder state and the attention state, and
produces a gate.
The gate can be used to select t... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | ESCM-summarization/ESCM-summary-evaluation | SourceContextGate | false | 9,118 | [
"MIT"
] | 0 | 3780b51f0ed44cbbea3f163a871d875f1e5e9393 | https://github.com/ESCM-summarization/ESCM-summary-evaluation/tree/3780b51f0ed44cbbea3f163a871d875f1e5e9393 | import torch
import torch.nn as nn
import torch.cuda
import torch.distributed
class ContextGate(nn.Module):
"""
Context gate is a decoder module that takes as input the previous word
embedding, the current decoder state and the attention state, and
produces a gate.
The gate can be used to select t... |
TargetContextGate | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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.cuda
import torch.distributed
class ContextGate(nn.Module):
"""
Context gate is a decoder module that takes as input the previous word
embedding, the current decoder state and the attention state, and
produces a gate.
The gate can be used to select t... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | ESCM-summarization/ESCM-summary-evaluation | TargetContextGate | false | 9,119 | [
"MIT"
] | 0 | 3780b51f0ed44cbbea3f163a871d875f1e5e9393 | https://github.com/ESCM-summarization/ESCM-summary-evaluation/tree/3780b51f0ed44cbbea3f163a871d875f1e5e9393 | import torch
import torch.nn as nn
import torch.cuda
import torch.distributed
class ContextGate(nn.Module):
"""
Context gate is a decoder module that takes as input the previous word
embedding, the current decoder state and the attention state, and
produces a gate.
The gate can be used to select t... |
GlobalAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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.cuda
import torch.distributed
def aeq(*args):
"""
Assert all arguments have the same value
"""
arguments = (arg for arg in args)
first = next(arguments)
assert all(arg == first for arg in arguments
), 'Not ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | ESCM-summarization/ESCM-summary-evaluation | GlobalAttention | false | 9,120 | [
"MIT"
] | 0 | 3780b51f0ed44cbbea3f163a871d875f1e5e9393 | https://github.com/ESCM-summarization/ESCM-summary-evaluation/tree/3780b51f0ed44cbbea3f163a871d875f1e5e9393 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.cuda
import torch.distributed
def aeq(*args):
"""
Assert all arguments have the same value
"""
arguments = (arg for arg in args)
first = next(arguments)
assert all(arg == first for arg in arguments
), 'Not ... |
ContextGate | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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.cuda
import torch.distributed
class ContextGate(nn.Module):
"""
Context gate is a decoder module that takes as input the previous word
embedding, the current decoder state and the attention state, and
produces a gate.
The gate can be used to select t... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.cuda
import torch.distributed
assert_size_str... | ESCM-summarization/ESCM-summary-evaluation | ContextGate | false | 9,121 | [
"MIT"
] | 0 | 3780b51f0ed44cbbea3f163a871d875f1e5e9393 | https://github.com/ESCM-summarization/ESCM-summary-evaluation/tree/3780b51f0ed44cbbea3f163a871d875f1e5e9393 | import torch
import torch.nn as nn
import torch.cuda
import torch.distributed
class Model(nn.Module):
"""
Context gate is a decoder module that takes as input the previous word
embedding, the current decoder state and the attention state, and
produces a gate.
The gate can be used to select the inp... |
MaxPoolBlock | # 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 Block(nn.Module):
def __init__(self):
"""Initialisation for a lower-level DeepLPF conv block
:returns: N/A
:rtype: N/A
"""
super(Block, self).__init__()
def conv3x3(self, in_channels, out_channels, stride=1):
"""Repre... | import torch
import triton
import triton.language as tl
from 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... | DevilMayNotCry/My_curl | MaxPoolBlock | false | 9,122 | [
"BSD-3-Clause"
] | 0 | a8f65a3e58cbdeefb4679aa2f0c3d9d800b67381 | https://github.com/DevilMayNotCry/My_curl/tree/a8f65a3e58cbdeefb4679aa2f0c3d9d800b67381 | import torch
import torch.nn as nn
class Block(nn.Module):
def __init__(self):
"""Initialisation for a lower-level DeepLPF conv block
:returns: N/A
:rtype: N/A
"""
super().__init__()
def conv3x3(self, in_channels, out_channels, stride=1):
"""Represents a con... |
GlobalPoolingBlock | # 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 Block(nn.Module):
def __init__(self):
"""Initialisation for a lower-level DeepLPF conv block
:returns: N/A
:rtype: N/A
"""
super(Block, self).__init__()
def conv3x3(self, in_channels, out_channels, stride=1):
"""Repre... | import torch
import 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... | DevilMayNotCry/My_curl | GlobalPoolingBlock | false | 9,123 | [
"BSD-3-Clause"
] | 0 | a8f65a3e58cbdeefb4679aa2f0c3d9d800b67381 | https://github.com/DevilMayNotCry/My_curl/tree/a8f65a3e58cbdeefb4679aa2f0c3d9d800b67381 | import torch
import torch.nn as nn
class Block(nn.Module):
def __init__(self):
"""Initialisation for a lower-level DeepLPF conv block
:returns: N/A
:rtype: N/A
"""
super().__init__()
def conv3x3(self, in_channels, out_channels, stride=1):
"""Represents a con... |
AddNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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 TimeDistributedInterpolation(nn.Module):
def __init__(self, output_size: 'int', batch_first: 'bool'=False,
trainable: 'bool'=False):
super().__init__()
self.output_size = output_size
self.batch_first = batch_... | 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.functional as F
import torch.nn as nn
assert_size_stride = torc... | JakeForsey/pytorch-forecasting | AddNorm | false | 9,124 | [
"MIT"
] | 0 | e5291df3dd8f8d72ecd2b21869f69cebf9456028 | https://github.com/JakeForsey/pytorch-forecasting/tree/e5291df3dd8f8d72ecd2b21869f69cebf9456028 | import torch
import torch.nn.functional as F
import torch.nn as nn
class TimeDistributedInterpolation(nn.Module):
def __init__(self, output_size: 'int', batch_first: 'bool'=False,
trainable: 'bool'=False):
super().__init__()
self.output_size = output_size
self.batch_first = batch_... |
h_swish | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.utils.data
import torch.nn as nn
class h_sigmoid(nn.Module):
def __init__(self, inplace=True):
super(h_sigmoid, self).__init__()
self.relu = nn.ReLU6(inplace=inplace)
def forward(self, x):
return self.relu(x + 3) / 6
class h_swish(nn.Module):
def __in... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guard... | Ghaust/SSD | h_swish | false | 9,125 | [
"MIT"
] | 0 | 2bf14a48795d20ad2177f622e84d62b3ff81183f | https://github.com/Ghaust/SSD/tree/2bf14a48795d20ad2177f622e84d62b3ff81183f | import torch
import torch.utils.data
import torch.nn as nn
class h_sigmoid(nn.Module):
def __init__(self, inplace=True):
super().__init__()
self.relu = nn.ReLU6(inplace=inplace)
def forward(self, x):
return self.relu(x + 3) / 6
class Model(nn.Module):
def __init__(self, inplac... |
ClassificationModel | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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 ClassificationModel(nn.Module):
def __init__(self, num_features_in, num_anchors=21, num_classes=15,
prior=0.01, feature_size=256):
super(ClassificationModel, self).__init__()
self.num_classes = num_classes
self.num_anchors = num_anchors
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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_... | HenryOsborne/Rotation | ClassificationModel | false | 9,126 | [
"Apache-2.0"
] | 0 | 417fa90bcbb2a144f0c1d2ce5d9fc110f6617bf2 | https://github.com/HenryOsborne/Rotation/tree/417fa90bcbb2a144f0c1d2ce5d9fc110f6617bf2 | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, num_features_in, num_anchors=21, num_classes=15,
prior=0.01, feature_size=256):
super().__init__()
self.num_classes = num_classes
self.num_anchors = num_anchors
self.conv1 = nn.Conv2d(num_feature... |
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 as nn
class Block(nn.Module):
def __init__(self):
"""Initialisation for a lower-level DeepLPF conv block
:returns: N/A
:rtype: N/A
"""
super(Block, self).__init__()
def conv3x3(self, in_channels, out_channels, stride=1):
"""Repre... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | DevilMayNotCry/My_curl | ConvBlock | false | 9,127 | [
"BSD-3-Clause"
] | 0 | a8f65a3e58cbdeefb4679aa2f0c3d9d800b67381 | https://github.com/DevilMayNotCry/My_curl/tree/a8f65a3e58cbdeefb4679aa2f0c3d9d800b67381 | import torch
import torch.nn as nn
class Block(nn.Module):
def __init__(self):
"""Initialisation for a lower-level DeepLPF conv block
:returns: N/A
:rtype: N/A
"""
super().__init__()
def conv3x3(self, in_channels, out_channels, stride=1):
"""Represents a con... |
MidNet2 | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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 MidNet2(nn.Module):
def forward(self, x_in):
"""Network with dilation rate 2
:param x_in: input convolutional features
:returns: processed convolutional features
:rtype: Tensor
"""
x = self.lrelu(self.conv1... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | DevilMayNotCry/My_curl | MidNet2 | false | 9,128 | [
"BSD-3-Clause"
] | 0 | a8f65a3e58cbdeefb4679aa2f0c3d9d800b67381 | https://github.com/DevilMayNotCry/My_curl/tree/a8f65a3e58cbdeefb4679aa2f0c3d9d800b67381 | import torch
import torch.nn as nn
class Model(nn.Module):
def forward(self, x_in):
"""Network with dilation rate 2
:param x_in: input convolutional features
:returns: processed convolutional features
:rtype: Tensor
"""
x = self.lrelu(self.conv1(x... |
AffineLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.utils.data
import torch
import torch.nn as nn
class AffineLayer(nn.Module):
def __init__(self, num_channels, bias=False):
super(AffineLayer, self).__init__()
weight = torch.FloatTensor(1, num_channels, 1, 1).fill_(1)
self.weight = nn.Parameter(weight, requires_gr... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cud... | JeyesHan/DeFRCN_Custom | AffineLayer | false | 9,129 | [
"MIT"
] | 0 | 6a536408a61bb10a5ef84ce6683b6278e6e01f43 | https://github.com/JeyesHan/DeFRCN_Custom/tree/6a536408a61bb10a5ef84ce6683b6278e6e01f43 | import torch
import torch.utils.data
import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, num_channels, bias=False):
super().__init__()
weight = torch.FloatTensor(1, num_channels, 1, 1).fill_(1)
self.weight = nn.Parameter(weight, requires_grad=True)
self.b... |
LR | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.utils.data
class LR(nn.Module):
def __init__(self, feature_nums, output_dim=1):
super(LR, self).__init__()
self.linear = nn.Linear(feature_nums, output_dim)
self.bias = nn.Parameter(torch.zeros((output_dim,)))
def forward(self, x):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | JiaXingBinggan/LSTM_Project | LR | false | 9,130 | [
"Apache-2.0"
] | 0 | 9d84fb96951f2f6036cb58e9c839bb879a09cbcc | https://github.com/JiaXingBinggan/LSTM_Project/tree/9d84fb96951f2f6036cb58e9c839bb879a09cbcc | import torch
import torch.nn as nn
import torch.utils.data
class Model(nn.Module):
def __init__(self, feature_nums, output_dim=1):
super().__init__()
self.linear = nn.Linear(feature_nums, output_dim)
self.bias = nn.Parameter(torch.zeros((output_dim,)))
def forward(self, x):
"... |
MidNet4 | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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 MidNet4(nn.Module):
def forward(self, x_in):
"""Network with dilation rate 4
:param x_in: input convolutional features
:returns: processed convolutional features
:rtype: Tensor
"""
x = self.lrelu(self.conv1(x_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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | DevilMayNotCry/My_curl | MidNet4 | false | 9,131 | [
"BSD-3-Clause"
] | 0 | a8f65a3e58cbdeefb4679aa2f0c3d9d800b67381 | https://github.com/DevilMayNotCry/My_curl/tree/a8f65a3e58cbdeefb4679aa2f0c3d9d800b67381 | import torch
import torch.nn as nn
class Model(nn.Module):
def forward(self, x_in):
"""Network with dilation rate 4
:param x_in: input convolutional features
:returns: processed convolutional features
:rtype: Tensor
"""
x = self.lrelu(self.conv1(x_in))
x ... |
NetVLAD | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
class NetVLAD(nn.Module):
"""NetVLAD layer implementation"""
def __init__(self, num_clusters, dim, alpha=1.0):
"""
Args:
num_clusters : int
The number of clusters
dim : int
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | Guido27/project_vg | NetVLAD | false | 9,132 | [
"MIT"
] | 0 | 3322fc355742929f43f3d97204398035645d968c | https://github.com/Guido27/project_vg/tree/3322fc355742929f43f3d97204398035645d968c | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
"""NetVLAD layer implementation"""
def __init__(self, num_clusters, dim, alpha=1.0):
"""
Args:
num_clusters : int
The number of clusters
dim : int
... |
LocalNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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 LocalNet(nn.Module):
def forward(self, x_in):
"""Defines a double convolution
:param x_in: input convolutional features
:returns: convolutional features
:rtype: Tensor
"""
x = self.lrelu(self.conv1(self.refpad(x_in)))
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.... | DevilMayNotCry/My_curl | LocalNet | false | 9,133 | [
"BSD-3-Clause"
] | 0 | a8f65a3e58cbdeefb4679aa2f0c3d9d800b67381 | https://github.com/DevilMayNotCry/My_curl/tree/a8f65a3e58cbdeefb4679aa2f0c3d9d800b67381 | import torch
import torch.nn as nn
class Model(nn.Module):
def forward(self, x_in):
"""Defines a double convolution
:param x_in: input convolutional features
:returns: convolutional features
:rtype: Tensor
"""
x = self.lrelu(self.conv1(self.refpad(x_in)))
... |
TReLU | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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 TReLU(nn.Module):
def __init__(self):
super(TReLU, self).__init__()
self.alpha = nn.Parameter(torch.FloatTensor(1), requires_grad=True)
self.alpha.data.fill_(0)
def forward(self, x):
x = F.relu(x - self.... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | HenryOsborne/LearningToPaint | TReLU | false | 9,134 | [
"MIT"
] | 0 | d8fdf41c8d193b91c78f73b7a092897e846e19eb | https://github.com/HenryOsborne/LearningToPaint/tree/d8fdf41c8d193b91c78f73b7a092897e846e19eb | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self):
super().__init__()
self.alpha = nn.Parameter(torch.FloatTensor(1), requires_grad=True)
self.alpha.data.fill_(0)
def forward(self, x):
x = F.relu(x - self.alpha) + se... |
DCGANGenerator_mnist | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import functools
import torch
import torch.utils.data
import torch
import torch.nn as nn
class DCGANGenerator_mnist(nn.Module):
def __init__(self, z_dim, ngf=64, output_nc=1, norm_layer=nn.BatchNorm2d):
super(DCGANGenerator_mnist, self).__init__()
self.z_dim = z_dim
self.ngf = ngf
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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 functools
im... | Gabriele91/EvolutionaryGAN-pytorch | DCGANGenerator_mnist | false | 9,135 | [
"MIT"
] | 0 | 993cb13551908727e52aef738f8954072b5b398a | https://github.com/Gabriele91/EvolutionaryGAN-pytorch/tree/993cb13551908727e52aef738f8954072b5b398a | import functools
import torch
import torch.utils.data
import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, z_dim, ngf=64, output_nc=1, norm_layer=nn.BatchNorm2d):
super().__init__()
self.z_dim = z_dim
self.ngf = ngf
self.img_size = 28 * 28 * output_nc
... |
LinearAttentionLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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 LinearAttentionLayer(nn.Module):
def __init__(self, input_dim):
super().__init__()
self.linear = nn.Linear(input_dim, 1)
def forward(self, question, question_mask):
qtn = question.view(-1, question.shape[-1])
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | HuyTu7/dl_optimizers | LinearAttentionLayer | false | 9,136 | [
"MIT"
] | 0 | 245242718324cebcabe657bdbc704aa54ad0b8d2 | https://github.com/HuyTu7/dl_optimizers/tree/245242718324cebcabe657bdbc704aa54ad0b8d2 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, input_dim):
super().__init__()
self.linear = nn.Linear(input_dim, 1)
def forward(self, question, question_mask):
qtn = question.view(-1, question.shape[-1])
attn_scor... |
AlignQuestionEmbedding | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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 AlignQuestionEmbedding(nn.Module):
def __init__(self, input_dim):
super().__init__()
self.linear = nn.Linear(input_dim, input_dim)
self.relu = nn.ReLU()
def forward(self, context, question, question_mask):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | HuyTu7/dl_optimizers | AlignQuestionEmbedding | false | 9,137 | [
"MIT"
] | 0 | 245242718324cebcabe657bdbc704aa54ad0b8d2 | https://github.com/HuyTu7/dl_optimizers/tree/245242718324cebcabe657bdbc704aa54ad0b8d2 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, input_dim):
super().__init__()
self.linear = nn.Linear(input_dim, input_dim)
self.relu = nn.ReLU()
def forward(self, context, question, question_mask):
ctx_ = self.li... |
MergeLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
class MergeLayer(torch.nn.Module):
def __init__(self, dim1, dim2, dim3, dim4):
super().__init__()
self.fc1 = torch.nn.Linear(dim1 + dim2, dim3)
self.fc2 = torch.nn.Linear(dim3, dim4)
self.act = torch.nn.ReLU()
torch.nn.init.xavier_normal_(self.fc1.weight)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C... | IDSC-io/vre-tgn | MergeLayer | false | 9,138 | [
"Apache-2.0"
] | 0 | 46e8327e3befe67003874fa70b384a511523f8f7 | https://github.com/IDSC-io/vre-tgn/tree/46e8327e3befe67003874fa70b384a511523f8f7 | import torch
class Model(torch.nn.Module):
def __init__(self, dim1, dim2, dim3, dim4):
super().__init__()
self.fc1 = torch.nn.Linear(dim1 + dim2, dim3)
self.fc2 = torch.nn.Linear(dim3, dim4)
self.act = torch.nn.ReLU()
torch.nn.init.xavier_normal_(self.fc1.weight)
t... |
EqualConvTranspose2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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 math import sqrt
import torch.utils.data
def equal_lr(module, name='weight'):
EqualLR.apply(module, name)
return module
class EqualLR:
def __init__(self, name):
self.name = name
def compute_weight(self, module):
weight = getattr(module, 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
import torch.nn as nn
from math import sqrt
import torch.utils.data
assert_size_... | GuiCamargoX/gans_pytorch | EqualConvTranspose2d | false | 9,139 | [
"MIT"
] | 0 | 3103184e54ea0d2922fc664a994a912bf61db426 | https://github.com/GuiCamargoX/gans_pytorch/tree/3103184e54ea0d2922fc664a994a912bf61db426 | import torch
import torch.nn as nn
from math import sqrt
import torch.utils.data
def equal_lr(module, name='weight'):
EqualLR.apply(module, name)
return module
class EqualLR:
def __init__(self, name):
self.name = name
def compute_weight(self, module):
weight = getattr(module, self.... |
MatrixTree | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
import torch.cuda
import torch.distributed
class MatrixTree(nn.Module):
"""Implementation of the matrix-tree theorem for computing marginals
of non-projective dependency parsing. This attention layer is used
in the paper "Learning Structured Text Representations"
:ci... | 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.cuda
import torch.distributed
assert_s... | GarrettNicolai/OpenNMT-py | MatrixTree | false | 9,140 | [
"MIT"
] | 0 | 9491d900ac1b50fe39da417bacc0b9d610331888 | https://github.com/GarrettNicolai/OpenNMT-py/tree/9491d900ac1b50fe39da417bacc0b9d610331888 | import torch
import torch.nn as nn
import torch.cuda
import torch.distributed
class Model(nn.Module):
"""Implementation of the matrix-tree theorem for computing marginals
of non-projective dependency parsing. This attention layer is used
in the paper "Learning Structured Text Representations"
:cite:`D... |
EqualConv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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 math import sqrt
import torch.utils.data
def equal_lr(module, name='weight'):
EqualLR.apply(module, name)
return module
class EqualLR:
def __init__(self, name):
self.name = name
def compute_weight(self, module):
weight = getattr(module, 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
import torch.nn as nn
from math import sqrt
import torch.utils.data
assert_size_... | GuiCamargoX/gans_pytorch | EqualConv2d | false | 9,141 | [
"MIT"
] | 0 | 3103184e54ea0d2922fc664a994a912bf61db426 | https://github.com/GuiCamargoX/gans_pytorch/tree/3103184e54ea0d2922fc664a994a912bf61db426 | import torch
import torch.nn as nn
from math import sqrt
import torch.utils.data
def equal_lr(module, name='weight'):
EqualLR.apply(module, name)
return module
class EqualLR:
def __init__(self, name):
self.name = name
def compute_weight(self, module):
weight = getattr(module, self.... |
EqualLinear | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class EqualLinear(nn.Module):
def __init__(self, in_dim, out_dim, lr_mul=1, bias=True):
super().__init__()
self.weight = nn.Parameter(torch.randn(out_dim, in_dim))
if bias:
self.bias = n... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | GuiCamargoX/gans_pytorch | EqualLinear | false | 9,142 | [
"MIT"
] | 0 | 3103184e54ea0d2922fc664a994a912bf61db426 | https://github.com/GuiCamargoX/gans_pytorch/tree/3103184e54ea0d2922fc664a994a912bf61db426 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
class Model(nn.Module):
def __init__(self, in_dim, out_dim, lr_mul=1, bias=True):
super().__init__()
self.weight = nn.Parameter(torch.randn(out_dim, in_dim))
if bias:
self.bias = nn.Para... |
TimeEncode | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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
class TimeEncode(torch.nn.Module):
def __init__(self, dimension):
super(TimeEncode, self).__init__()
self.dimension = dimension
self.w = torch.nn.Linear(1, dimension)
self.w.weight = torch.nn.Parameter(torch.from_numpy(1 / 10 ** np.
lins... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import numpy ... | IDSC-io/vre-tgn | TimeEncode | false | 9,143 | [
"Apache-2.0"
] | 0 | 46e8327e3befe67003874fa70b384a511523f8f7 | https://github.com/IDSC-io/vre-tgn/tree/46e8327e3befe67003874fa70b384a511523f8f7 | import torch
import numpy as np
class Model(torch.nn.Module):
def __init__(self, dimension):
super().__init__()
self.dimension = dimension
self.w = torch.nn.Linear(1, dimension)
self.w.weight = torch.nn.Parameter(torch.from_numpy(1 / 10 ** np.
linspace(0, 9, dimension)... |
MLP | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
class MLP(torch.nn.Module):
def __init__(self, dim, drop=0.3):
super().__init__()
self.fc_1 = torch.nn.Linear(dim, 80)
self.fc_2 = torch.nn.Linear(80, 10)
self.fc_3 = torch.nn.Linear(10, 1)
self.act = torch.nn.ReLU()
self.dropout = torch.nn.Dropout(p=d... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C... | IDSC-io/vre-tgn | MLP | false | 9,144 | [
"Apache-2.0"
] | 0 | 46e8327e3befe67003874fa70b384a511523f8f7 | https://github.com/IDSC-io/vre-tgn/tree/46e8327e3befe67003874fa70b384a511523f8f7 | import torch
class Model(torch.nn.Module):
def __init__(self, dim, drop=0.3):
super().__init__()
self.fc_1 = torch.nn.Linear(dim, 80)
self.fc_2 = torch.nn.Linear(80, 10)
self.fc_3 = torch.nn.Linear(10, 1)
self.act = torch.nn.ReLU()
self.dropout = torch.nn.Dropout(p... |
L1Loss | # 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 functools
import torch
import torch.nn.functional as F
import torch.nn as nn
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tensor: Reduced loss ten... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import functools
impor... | ChHanXiao/mmdetection | L1Loss | false | 9,145 | [
"Apache-2.0"
] | 0 | 324aa5a042857a9b57abe37385e1210709a20d02 | https://github.com/ChHanXiao/mmdetection/tree/324aa5a042857a9b57abe37385e1210709a20d02 | import functools
import torch
import torch.nn.functional as F
import torch.nn as nn
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tensor: Reduced loss ten... |
BalancedL1Loss | # 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 functools
import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tenso... | 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 functools
impor... | ChHanXiao/mmdetection | BalancedL1Loss | false | 9,146 | [
"Apache-2.0"
] | 0 | 324aa5a042857a9b57abe37385e1210709a20d02 | https://github.com/ChHanXiao/mmdetection/tree/324aa5a042857a9b57abe37385e1210709a20d02 | import functools
import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tenso... |
GaussianFocalLoss | # 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 functools
import torch
import torch.nn.functional as F
import torch.nn as nn
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tensor: Reduced loss ten... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import functools
impor... | ChHanXiao/mmdetection | GaussianFocalLoss | false | 9,147 | [
"Apache-2.0"
] | 0 | 324aa5a042857a9b57abe37385e1210709a20d02 | https://github.com/ChHanXiao/mmdetection/tree/324aa5a042857a9b57abe37385e1210709a20d02 | import functools
import torch
import torch.nn.functional as F
import torch.nn as nn
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tensor: Reduced loss ten... |
SimpleModel | # 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 SimpleModel(nn.Module):
def __init__(self):
super(SimpleModel, self).__init__()
def forward(self, x):
return x * 2
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | JimmyCai91/tensorboardX | SimpleModel | false | 9,148 | [
"MIT"
] | 0 | 9bff602008d71f4bbf6e83e99125033629f4ee6f | https://github.com/JimmyCai91/tensorboardX/tree/9bff602008d71f4bbf6e83e99125033629f4ee6f | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
return x * 2
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return []
|
BasicBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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.utils.weight_norm as weightNorm
def conv3x3(in_planes, out_planes, stride=1):
return weightNorm(nn.Conv2d(in_planes, out_planes, kernel_size=3,
stride=stride, padding=1, bias=True))
class TReLU(nn.Module):
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
from torch._inductor.runtime.... | HenryOsborne/LearningToPaint | BasicBlock | false | 9,149 | [
"MIT"
] | 0 | d8fdf41c8d193b91c78f73b7a092897e846e19eb | https://github.com/HenryOsborne/LearningToPaint/tree/d8fdf41c8d193b91c78f73b7a092897e846e19eb | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.utils.weight_norm as weightNorm
def conv3x3(in_planes, out_planes, stride=1):
return weightNorm(nn.Conv2d(in_planes, out_planes, kernel_size=3,
stride=stride, padding=1, bias=True))
class TReLU(nn.Module):
def __init... |
VocabGraphConvolution | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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.init as init
class VocabGraphConvolution(nn.Module):
"""Vocabulary GCN module.
Params:
`voc_dim`: The size of vocabulary graph
`num_adj`: The number of the adjacency matrix of Vocabulary graph
`hid_dim`: The hidden dimensi... | import torch
from torch._inductor.select_algorithm import extern_kernels
import 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.nn.init as init
assert_size_strid... | JakobVokac/VGCN-BERT | VocabGraphConvolution | false | 9,150 | [
"MIT"
] | 0 | f82f1922c0d461c12d43c45bc58b61b92534b99b | https://github.com/JakobVokac/VGCN-BERT/tree/f82f1922c0d461c12d43c45bc58b61b92534b99b | import math
import torch
import torch.nn as nn
import torch.nn.init as init
class Model(nn.Module):
"""Vocabulary GCN module.
Params:
`voc_dim`: The size of vocabulary graph
`num_adj`: The number of the adjacency matrix of Vocabulary graph
`hid_dim`: The hidden dimension after XAW
... |
GHMC | # 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
def _expand_onehot_labels(labels, label_weights, label_channels):
bin_labels = labels.new_full((labels.size(0), label_channels), 0)
inds = torch.nonzero((labels >= 0) & (labels < label_channels),
as_tuple=False).squeeze()
if inds.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.nn as nn
... | ChHanXiao/mmdetection | GHMC | false | 9,151 | [
"Apache-2.0"
] | 0 | 324aa5a042857a9b57abe37385e1210709a20d02 | https://github.com/ChHanXiao/mmdetection/tree/324aa5a042857a9b57abe37385e1210709a20d02 | import torch
import torch.nn.functional as F
import torch.nn as nn
def _expand_onehot_labels(labels, label_weights, label_channels):
bin_labels = labels.new_full((labels.size(0), label_channels), 0)
inds = torch.nonzero((labels >= 0) & (labels < label_channels),
as_tuple=False).squeeze()
if inds.n... |
DenseCrossEntropy | # 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 DenseCrossEntropy(nn.Module):
def forward(self, x, target):
x = x.float()
target = target.float()
logprobs = torch.nn.functional.log_softmax(x, dim=-1)
loss = -logprobs * target
loss = loss.sum(-1)
return loss.mean()
def g... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | Husky95/Google-Landmark-Recognition-2020-3rd-Place-Solution | DenseCrossEntropy | false | 9,152 | [
"Apache-2.0"
] | 0 | 48806b9e09beabf74e8f96575855dcfa13a4f996 | https://github.com/Husky95/Google-Landmark-Recognition-2020-3rd-Place-Solution/tree/48806b9e09beabf74e8f96575855dcfa13a4f996 | import torch
import torch.nn as nn
class Model(nn.Module):
def forward(self, x, target):
x = x.float()
target = target.float()
logprobs = torch.nn.functional.log_softmax(x, dim=-1)
loss = -logprobs * target
loss = loss.sum(-1)
return loss.mean()
def get_inputs():... |
GE2ELoss | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import torch.nn as nn
import torch.nn.functional as F
def calc_loss(sim_matrix):
same_idx = list(range(sim_matrix.size(0)))
pos = sim_matrix[same_idx, :, same_idx]
neg = (torch.exp(sim_matrix).sum(dim=2) + 1e-06).log_()
per_embedding_loss = -1 * (pos - neg)
loss = per_embedding_loss.s... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | JeffT13/SCOTUS_Speaker_Verification | GE2ELoss | false | 9,153 | [
"BSD-3-Clause"
] | 0 | 276f52c23fe40d1f55ae77889b202350f3220d1d | https://github.com/JeffT13/SCOTUS_Speaker_Verification/tree/276f52c23fe40d1f55ae77889b202350f3220d1d | import torch
import torch.nn as nn
import torch.nn.functional as F
def calc_loss(sim_matrix):
same_idx = list(range(sim_matrix.size(0)))
pos = sim_matrix[same_idx, :, same_idx]
neg = (torch.exp(sim_matrix).sum(dim=2) + 1e-06).log_()
per_embedding_loss = -1 * (pos - neg)
loss = per_embedding_loss.s... |
MSELoss | # 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 functools
import torch
import torch.nn.functional as F
import torch.nn as nn
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tensor: Reduced loss ten... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import functools
import torch.nn.functional as F
import torch.nn as nn
assert_size_stride... | ChHanXiao/mmdetection | MSELoss | false | 9,154 | [
"Apache-2.0"
] | 0 | 324aa5a042857a9b57abe37385e1210709a20d02 | https://github.com/ChHanXiao/mmdetection/tree/324aa5a042857a9b57abe37385e1210709a20d02 | import functools
import torch
import torch.nn.functional as F
import torch.nn as nn
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tensor: Reduced loss ten... |
LabelwiseLinearOutput | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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 LabelwiseLinearOutput(nn.Module):
"""Applies a linear transformation to the incoming data for each label
Args:
input_size (int): The number of expected features in the input.
num_classes (int): Total number of classes.
"""
def __init__(self, i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | JamesLYC88/LibMultiLabel | LabelwiseLinearOutput | false | 9,155 | [
"MIT"
] | 0 | 042b76b3564409d916cf735ace617319009ae118 | https://github.com/JamesLYC88/LibMultiLabel/tree/042b76b3564409d916cf735ace617319009ae118 | import torch
import torch.nn as nn
class Model(nn.Module):
"""Applies a linear transformation to the incoming data for each label
Args:
input_size (int): The number of expected features in the input.
num_classes (int): Total number of classes.
"""
def __init__(self, input_size, num_c... |
GHMR | # 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 GHMR(nn.Module):
"""GHM Regression Loss.
Details of the theorem can be viewed in the paper
`Gradient Harmonized Single-stage Detector
<https://arxiv.org/abs/1811.05181>`_.
Args:
mu (float): The parameter for the Authentic Smooth L1 loss.
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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | ChHanXiao/mmdetection | GHMR | false | 9,156 | [
"Apache-2.0"
] | 0 | 324aa5a042857a9b57abe37385e1210709a20d02 | https://github.com/ChHanXiao/mmdetection/tree/324aa5a042857a9b57abe37385e1210709a20d02 | import torch
import torch.nn as nn
class Model(nn.Module):
"""GHM Regression Loss.
Details of the theorem can be viewed in the paper
`Gradient Harmonized Single-stage Detector
<https://arxiv.org/abs/1811.05181>`_.
Args:
mu (float): The parameter for the Authentic Smooth L1 loss.
... |
ArcMarginProduct_subcenter | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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
class ArcMarginProduct_subcenter(nn.Module):
def __init__(self, in_features, out_features, k=3):
super().__init__()
self.weight = nn.Parameter(torch.FloatTensor(out_features * k,
in_features))
self.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Husky95/Google-Landmark-Recognition-2020-3rd-Place-Solution | ArcMarginProduct_subcenter | false | 9,157 | [
"Apache-2.0"
] | 0 | 48806b9e09beabf74e8f96575855dcfa13a4f996 | https://github.com/Husky95/Google-Landmark-Recognition-2020-3rd-Place-Solution/tree/48806b9e09beabf74e8f96575855dcfa13a4f996 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, in_features, out_features, k=3):
super().__init__()
self.weight = nn.Parameter(torch.FloatTensor(out_features * k,
in_features))
self.reset_parameters()
... |
maximum_absolute_error | # 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 maximum_absolute_error(nn.Module):
def forward(self, yhat, y):
return torch.max(torch.abs(torch.sub(y, yhat)))
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
a... | JonasBrusokas/ModelarDB-ext | maximum_absolute_error | false | 9,158 | [
"Apache-2.0"
] | 0 | 354678994cc5fa2d2264436f1d33f250e11d990d | https://github.com/JonasBrusokas/ModelarDB-ext/tree/354678994cc5fa2d2264436f1d33f250e11d990d | import torch
from torch import nn
class Model(nn.Module):
def forward(self, yhat, y):
return torch.max(torch.abs(torch.sub(y, yhat)))
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return []
|
SmoothL1Loss | # 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 functools
import torch
import torch.nn.functional as F
import torch.nn as nn
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tensor: Reduced loss ten... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import functools
impor... | ChHanXiao/mmdetection | SmoothL1Loss | false | 9,159 | [
"Apache-2.0"
] | 0 | 324aa5a042857a9b57abe37385e1210709a20d02 | https://github.com/ChHanXiao/mmdetection/tree/324aa5a042857a9b57abe37385e1210709a20d02 | import functools
import torch
import torch.nn.functional as F
import torch.nn as nn
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tensor: Reduced loss ten... |
VarifocalLoss | # 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
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tensor: Reduced loss tensor.
"""
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | ChHanXiao/mmdetection | VarifocalLoss | false | 9,160 | [
"Apache-2.0"
] | 0 | 324aa5a042857a9b57abe37385e1210709a20d02 | https://github.com/ChHanXiao/mmdetection/tree/324aa5a042857a9b57abe37385e1210709a20d02 | import torch
import torch.nn.functional as F
import torch.nn as nn
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tensor: Reduced loss tensor.
"""
... |
LabelwiseAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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 LabelwiseAttention(nn.Module):
"""Applies attention technique to summarize the sequence for each label
See `Explainable Prediction of Medical Codes from Clinical Text <https://aclanthology.org/N18-1100.pdf>`_
Args:
input_siz... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | JamesLYC88/LibMultiLabel | LabelwiseAttention | false | 9,161 | [
"MIT"
] | 0 | 042b76b3564409d916cf735ace617319009ae118 | https://github.com/JamesLYC88/LibMultiLabel/tree/042b76b3564409d916cf735ace617319009ae118 | import torch
import torch.nn.functional as F
import torch.nn as nn
class Model(nn.Module):
"""Applies attention technique to summarize the sequence for each label
See `Explainable Prediction of Medical Codes from Clinical Text <https://aclanthology.org/N18-1100.pdf>`_
Args:
input_size (int): The ... |
Psi2QNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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.parameter import Parameter
import torch.nn as nn
class Psi2QNet(nn.Module):
def __init__(self, output_dim, feature_dim):
super(Psi2QNet, self).__init__()
self.w = Parameter(torch.Tensor(feature_dim))
nn.init.constant_(self.w, 0)
self
def forward(sel... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn.parameter import Parameter
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided... | IanWangg/Multi-Context-RL | Psi2QNet | false | 9,162 | [
"MIT"
] | 0 | a268b16c5ad421b35339cb85de5347d4cf56b3dd | https://github.com/IanWangg/Multi-Context-RL/tree/a268b16c5ad421b35339cb85de5347d4cf56b3dd | import torch
from torch.nn.parameter import Parameter
import torch.nn as nn
class Model(nn.Module):
def __init__(self, output_dim, feature_dim):
super().__init__()
self.w = Parameter(torch.Tensor(feature_dim))
nn.init.constant_(self.w, 0)
self
def forward(self, psi):
... |
KeypointRCNNPredictor | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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
from torch import nn
class KeypointRCNNPredictor(nn.Module):
def __init__(self, in_channels, num_keypoints):
super(KeypointRCNNPredictor, self).__init__()
input_features = in_channels
deconv_kernel = 4
self.kps_score_lowres = nn.ConvTranspose2d... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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
from ... | Jack-XHP/LabPicV2-MaskRCNN | KeypointRCNNPredictor | false | 9,163 | [
"MIT"
] | 0 | b0586b2827000c7b7337d5110b2b1fd6185053a8 | https://github.com/Jack-XHP/LabPicV2-MaskRCNN/tree/b0586b2827000c7b7337d5110b2b1fd6185053a8 | import torch
import torch.utils.data
from torch import nn
class Model(nn.Module):
def __init__(self, in_channels, num_keypoints):
super().__init__()
input_features = in_channels
deconv_kernel = 4
self.kps_score_lowres = nn.ConvTranspose2d(input_features,
num_keypoints,... |
TripletLoss | # 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 TripletLoss(nn.Module):
def __init__(self, alpha=0.2):
super(TripletLoss, self).__init__()
self.alpha = alpha
def calc_euclidean(self, x1, x2):
return (x1 - x2).pow(2).sum(1)
def forward(self, anchor, positive, negative):
distance... | 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... | Jovian-Dsouza/Avenger_FaceNet | TripletLoss | false | 9,164 | [
"Apache-2.0"
] | 0 | e8bdffd017c9c27d4dc0f347f6992f760f1af5db | https://github.com/Jovian-Dsouza/Avenger_FaceNet/tree/e8bdffd017c9c27d4dc0f347f6992f760f1af5db | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, alpha=0.2):
super().__init__()
self.alpha = alpha
def calc_euclidean(self, x1, x2):
return (x1 - x2).pow(2).sum(1)
def forward(self, anchor, positive, negative):
distance_positive = self.calc_e... |
LinearExcitability | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
from torch import nn
from torch.nn.parameter import Parameter
def linearExcitability(input, weight, excitability=None, bias=None):
"""Applies a linear transformation to the incoming data: :math:`y = c(xA^T) + b`.
Shape:
- input: :math:`(N, *, in_features)`
- we... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
from torch import nn
from torch.nn.parameter import Parameter
assert... | JosephKJ/continual-learning | LinearExcitability | false | 9,165 | [
"MIT"
] | 0 | 2e526cc58ab35d76cddc1df46ee421baea89a727 | https://github.com/JosephKJ/continual-learning/tree/2e526cc58ab35d76cddc1df46ee421baea89a727 | import math
import torch
from torch import nn
from torch.nn.parameter import Parameter
def linearExcitability(input, weight, excitability=None, bias=None):
"""Applies a linear transformation to the incoming data: :math:`y = c(xA^T) + b`.
Shape:
- input: :math:`(N, *, in_features)`
- we... |
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 math
import torch
from typing import Optional
from typing import Tuple
from torch import nn
class MultiHeadedAttention(nn.Module):
"""Multi-Head Attention layer.
Args:
n_head (int): The number of heads.
n_feat (int): The number of features.
dropout_rate (float): Dropout rate.
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | JJoving/wenet | MultiHeadedAttention | false | 9,166 | [
"Apache-2.0"
] | 0 | 4a2195744dba43fe4fb9ad8d46a2b90a80dbdc4e | https://github.com/JJoving/wenet/tree/4a2195744dba43fe4fb9ad8d46a2b90a80dbdc4e | import math
import torch
from typing import Optional
from typing import Tuple
from torch import nn
class Model(nn.Module):
"""Multi-Head Attention layer.
Args:
n_head (int): The number of heads.
n_feat (int): The number of features.
dropout_rate (float): Dropout rate.
"""
de... |
BaselineTokenCNN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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 BaselineTokenCNN(nn.Module):
def __init__(self, num_classes):
super(BaselineTokenCNN, self).__init__()
self.conv1 = nn.Conv2d(in_channels=1, out_channels=4, kernel_size=7)
self.pool1 = nn.MaxPool2d(kernel_size=2, str... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Jesse-mk/10617_Project | BaselineTokenCNN | false | 9,167 | [
"MIT"
] | 0 | 2290e582fddc74f2f2f3e64e25f33a3bef6b1841 | https://github.com/Jesse-mk/10617_Project/tree/2290e582fddc74f2f2f3e64e25f33a3bef6b1841 | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, num_classes):
super().__init__()
self.conv1 = nn.Conv2d(in_channels=1, out_channels=4, kernel_size=7)
self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv2 = nn.Co... |
SelfAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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 SelfAttention(nn.Module):
def __init__(self, input_dim):
super(SelfAttention, self).__init__()
self.pre_pooling_linear = nn.Linear(input_dim, input_dim)
self.pooling_linear = nn.Linear(input_dim, 1)
def forward(self, x):
self.pre_pooli... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language 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
im... | JunKong5/WestBERT | SelfAttention | false | 9,168 | [
"MIT"
] | 0 | 8e0fc9aca290103698cd08239710193c36b06eff | https://github.com/JunKong5/WestBERT/tree/8e0fc9aca290103698cd08239710193c36b06eff | import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self, input_dim):
super().__init__()
self.pre_pooling_linear = nn.Linear(input_dim, input_dim)
self.pooling_linear = nn.Linear(input_dim, 1)
def forward(self, x):
self.pre_pooling_linear(x)
weight... |
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 torch.nn as nn
class MultiheadAttention(nn.Module):
"""A warpper for torch.nn.MultiheadAttention.
This module implements MultiheadAttention with residual connection,
and positional encoding used in DETR is also passed as input.
Args:
embed_dims (int): The embedding dimens... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from 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.... | ChHanXiao/mmdetection | MultiheadAttention | false | 9,169 | [
"Apache-2.0"
] | 0 | 324aa5a042857a9b57abe37385e1210709a20d02 | https://github.com/ChHanXiao/mmdetection/tree/324aa5a042857a9b57abe37385e1210709a20d02 | import torch
import torch.nn as nn
class Model(nn.Module):
"""A warpper for torch.nn.MultiheadAttention.
This module implements MultiheadAttention with residual connection,
and positional encoding used in DETR is also passed as input.
Args:
embed_dims (int): The embedding dimension.
... |
StyledConv | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import math
import torch
from torch import nn
import torch.utils.checkpoint
from torch.nn import functional as F
def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
rest_dim = [1] * (input.ndim - bias.ndim - 1)
input = input
if input.ndim == 3:
return F.leaky_relu(input + bias.v... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
from to... | Dokhyam/StyleCLIP | StyledConv | false | 9,170 | [
"MIT"
] | 0 | 3953c6fda14672762897d3ee16c0458dc848c21d | https://github.com/Dokhyam/StyleCLIP/tree/3953c6fda14672762897d3ee16c0458dc848c21d | import math
import torch
from torch import nn
import torch.utils.checkpoint
from torch.nn import functional as F
def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
rest_dim = [1] * (input.ndim - bias.ndim - 1)
input = input
if input.ndim == 3:
return F.leaky_relu(input + bias.v... |
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 warnings
import numpy as np
from torch.nn.modules.loss import _Loss
def one_hot(labels, num_classes):
"""
Converts label image `labels` to a one-hot vector with `num_classes` number of channels as last dimension.
"""
labels = labels % num_classes
y = np.eye(num_classes)
one... | 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 numpy as np
from torch.nn.modules.loss import _Loss
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cud... | JanSellner/MONAI | DiceLoss | false | 9,171 | [
"Apache-2.0"
] | 0 | ff8fa2bae94914030abb1bc0680417fdaa74afd8 | https://github.com/JanSellner/MONAI/tree/ff8fa2bae94914030abb1bc0680417fdaa74afd8 | import torch
import warnings
import numpy as np
from torch.nn.modules.loss import _Loss
def one_hot(labels, num_classes):
"""
Converts label image `labels` to a one-hot vector with `num_classes` number of channels as last dimension.
"""
labels = labels % num_classes
y = np.eye(num_classes)
one... |
FusedLeakyReLU | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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.checkpoint
from torch.nn import functional as F
def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
rest_dim = [1] * (input.ndim - bias.ndim - 1)
input = input
if input.ndim == 3:
return F.leaky_relu(input + bias.view(1, *rest... | 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
import torch.utils.checkpoint
from torch.nn import functional as F
assert_size_stride = torch._C._dynamo.guards.assert_... | Dokhyam/StyleCLIP | FusedLeakyReLU | false | 9,172 | [
"MIT"
] | 0 | 3953c6fda14672762897d3ee16c0458dc848c21d | https://github.com/Dokhyam/StyleCLIP/tree/3953c6fda14672762897d3ee16c0458dc848c21d | import torch
from torch import nn
import torch.utils.checkpoint
from torch.nn import functional as F
def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
rest_dim = [1] * (input.ndim - bias.ndim - 1)
input = input
if input.ndim == 3:
return F.leaky_relu(input + bias.view(1, *rest... |
PolicyNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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 PolicyNet(nn.Module):
def __init__(self):
super(PolicyNet, self).__init__()
self.fc1 = nn.Linear(64, 32)
self.fc2 = nn.Linear(32, 16)
self.fc3 = nn.Linear(16, 4)
def forward(self, x):
x = torch.f... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Jontahan/kvad | PolicyNet | false | 9,173 | [
"MIT"
] | 0 | 1b22db801048beb948b34bdd615ebe8630d13d9f | https://github.com/Jontahan/kvad/tree/1b22db801048beb948b34bdd615ebe8630d13d9f | import torch
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(64, 32)
self.fc2 = nn.Linear(32, 16)
self.fc3 = nn.Linear(16, 4)
def forward(self, x):
x = torch.flatten(x)
x... |
Res | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
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.distributions
class Res(nn.Module):
def __init__(self, H):
super().__init__()
self.u1 = nn.Linear(H, H)
self.u2 = nn.Linear(H, H)
self.v1 = nn.Linear(H, H)
self.v2 = nn.Linear(H, H)
self.w = nn.Linear(H, H)
def fo... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
import t... | JohnReid/pytorch-struct | Res | false | 9,174 | [
"MIT"
] | 0 | d9d4dd166f90a012aef6917ff7a14c708ced3477 | https://github.com/JohnReid/pytorch-struct/tree/d9d4dd166f90a012aef6917ff7a14c708ced3477 | import torch
from torch import nn
import torch.distributions
class Model(nn.Module):
def __init__(self, H):
super().__init__()
self.u1 = nn.Linear(H, H)
self.u2 = nn.Linear(H, H)
self.v1 = nn.Linear(H, H)
self.v2 = nn.Linear(H, H)
self.w = nn.Linear(H, H)
def ... |
ConvertPointsToHomogeneous | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import torch.nn as nn
def convert_points_to_homogeneous(points):
"""Function that converts points from Euclidean to homogeneous space.
See :class:`~torchgeometry.ConvertPointsToHomogeneous` for details.
Examples::
>>> input = torch.rand(2, 4, 3) # BxNx3
>>> output = tgm.co... | 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... | JudyYe/frankmocap | ConvertPointsToHomogeneous | false | 9,175 | [
"BSD-3-Clause"
] | 0 | b6e63f344e852ebdbca0095643b5bc0466370891 | https://github.com/JudyYe/frankmocap/tree/b6e63f344e852ebdbca0095643b5bc0466370891 | import torch
import torch.nn as nn
def convert_points_to_homogeneous(points):
"""Function that converts points from Euclidean to homogeneous space.
See :class:`~torchgeometry.ConvertPointsToHomogeneous` for details.
Examples::
>>> input = torch.rand(2, 4, 3) # BxNx3
>>> output = tgm.co... |
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