index int64 0 50.7k | project_path stringclasses 279
values | completion_path stringlengths 57 199 | signature_postion stringlengths 3 11 | class_position stringlengths 3 11 | function_name stringlengths 1 62 | class_name stringlengths 0 55 | body_position stringlengths 3 11 | body_position_with_comments stringlengths 3 11 | masked_class stringlengths 14 8.32k | masked_class_with_comment stringlengths 14 8.32k | func_body stringlengths 9 6.72k | func_body_with_comment stringlengths 9 6.72k | len_func_body int64 1 643 | len_input int64 10 2.02k | len_output int64 4 1.9k | total int64 20 2.05k | file_code stringlengths 14 442k | file_code_with_comment stringlengths 14 442k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
17,890 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/models/tiny_vit.py | 79,80 | 67,92 | forward | MBConv | 80,92 | 80,92 | class MBConv(nn.Module):
def __init__(self, in_chs, out_chs, expand_ratio, act_layer, drop_path):
super().__init__()
mid_chs = int(in_chs * expand_ratio)
self.conv1 = ConvNorm(in_chs, mid_chs, ks=1)
self.act1 = act_layer()
self.conv2 = ConvNorm(mid_chs, mid_chs, ks=3, stride=... | class MBConv(nn.Module):
def __init__(self, in_chs, out_chs, expand_ratio, act_layer, drop_path):
super().__init__()
mid_chs = int(in_chs * expand_ratio)
self.conv1 = ConvNorm(in_chs, mid_chs, ks=1)
self.act1 = act_layer()
self.conv2 = ConvNorm(mid_chs, mid_chs, ks=3, stride=... | shortcut = x
x = self.conv1(x)
x = self.act1(x)
x = self.conv2(x)
x = self.act2(x)
x = self.conv3(x)
x = self.drop_path(x)
x += shortcut
x = self.act3(x)
return x
| shortcut = x
x = self.conv1(x)
x = self.act1(x)
x = self.conv2(x)
x = self.act2(x)
x = self.conv3(x)
x = self.drop_path(x)
x += shortcut
x = self.act3(x)
return x
| 29 | 252 | 106 | 358 | """ TinyViT
Paper: `TinyViT: Fast Pretraining Distillation for Small Vision Transformers`
- https://arxiv.org/abs/2207.10666
Adapted from official impl at https://github.com/microsoft/Cream/tree/main/TinyViT
"""
__all__ = ['TinyVit']
import math
import itertools
from functools import partial
from typing import ... | """ TinyViT
Paper: `TinyViT: Fast Pretraining Distillation for Small Vision Transformers`
- https://arxiv.org/abs/2207.10666
Adapted from official impl at https://github.com/microsoft/Cream/tree/main/TinyViT
"""
__all__ = ['TinyVit']
import math
import itertools
from functools import partial
from typing import ... |
17,891 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/models/tiny_vit.py | 155,156 | 135,165 | forward | NormMlp | 156,165 | 156,165 | class NormMlp(nn.Module):
def __init__(
self,
in_features,
hidden_features=None,
out_features=None,
norm_layer=nn.LayerNorm,
act_layer=nn.GELU,
drop=0.,
):
super().__init__()
out_features = out_features or in_fea... | class NormMlp(nn.Module):
def __init__(
self,
in_features,
hidden_features=None,
out_features=None,
norm_layer=nn.LayerNorm,
act_layer=nn.GELU,
drop=0.,
):
super().__init__()
out_features = out_features or in_fea... | x = self.norm(x)
x = self.fc1(x)
x = self.act(x)
x = self.drop1(x)
x = self.fc2(x)
x = self.drop2(x)
return x
| x = self.norm(x)
x = self.fc1(x)
x = self.act(x)
x = self.drop1(x)
x = self.fc2(x)
x = self.drop2(x)
return x
| 20 | 223 | 79 | 302 | """ TinyViT
Paper: `TinyViT: Fast Pretraining Distillation for Small Vision Transformers`
- https://arxiv.org/abs/2207.10666
Adapted from official impl at https://github.com/microsoft/Cream/tree/main/TinyViT
"""
__all__ = ['TinyVit']
import math
import itertools
from functools import partial
from typing import ... | """ TinyViT
Paper: `TinyViT: Fast Pretraining Distillation for Small Vision Transformers`
- https://arxiv.org/abs/2207.10666
Adapted from official impl at https://github.com/microsoft/Cream/tree/main/TinyViT
"""
__all__ = ['TinyVit']
import math
import itertools
from functools import partial
from typing import ... |
17,892 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/models/tiny_vit.py | 60,61 | 52,67 | forward | PatchEmbed | 61,67 | 61,67 | class PatchEmbed(nn.Module):
def __init__(self, in_chs, out_chs, act_layer):
super().__init__()
self.stride = 4
self.conv1 = ConvNorm(in_chs, out_chs // 2, 3, 2, 1)
self.act = act_layer()
self.conv2 = ConvNorm(out_chs // 2, out_chs, 3, 2, 1)
def forward(self, x):
FILL_FU... | class PatchEmbed(nn.Module):
def __init__(self, in_chs, out_chs, act_layer):
super().__init__()
self.stride = 4
self.conv1 = ConvNorm(in_chs, out_chs // 2, 3, 2, 1)
self.act = act_layer()
self.conv2 = ConvNorm(out_chs // 2, out_chs, 3, 2, 1)
def forward(self, x):
FILL_FU... | x = self.conv1(x)
x = self.act(x)
x = self.conv2(x)
return x
| x = self.conv1(x)
x = self.act(x)
x = self.conv2(x)
return x
| 11 | 146 | 44 | 190 | """ TinyViT
Paper: `TinyViT: Fast Pretraining Distillation for Small Vision Transformers`
- https://arxiv.org/abs/2207.10666
Adapted from official impl at https://github.com/microsoft/Cream/tree/main/TinyViT
"""
__all__ = ['TinyVit']
import math
import itertools
from functools import partial
from typing import ... | """ TinyViT
Paper: `TinyViT: Fast Pretraining Distillation for Small Vision Transformers`
- https://arxiv.org/abs/2207.10666
Adapted from official impl at https://github.com/microsoft/Cream/tree/main/TinyViT
"""
__all__ = ['TinyVit']
import math
import itertools
from functools import partial
from typing import ... |
17,893 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/models/tiny_vit.py | 101,102 | 92,110 | forward | PatchMerging | 102,110 | 102,110 | class PatchMerging(nn.Module):
def __init__(self, dim, out_dim, act_layer):
super().__init__()
self.conv1 = ConvNorm(dim, out_dim, 1, 1, 0)
self.act1 = act_layer()
self.conv2 = ConvNorm(out_dim, out_dim, 3, 2, 1, groups=out_dim)
self.act2 = act_layer()
self.conv3 = Co... | class PatchMerging(nn.Module):
def __init__(self, dim, out_dim, act_layer):
super().__init__()
self.conv1 = ConvNorm(dim, out_dim, 1, 1, 0)
self.act1 = act_layer()
self.conv2 = ConvNorm(out_dim, out_dim, 3, 2, 1, groups=out_dim)
self.act2 = act_layer()
self.conv3 = Co... | x = self.conv1(x)
x = self.act1(x)
x = self.conv2(x)
x = self.act2(x)
x = self.conv3(x)
return x
| x = self.conv1(x)
x = self.act1(x)
x = self.conv2(x)
x = self.act2(x)
x = self.conv3(x)
return x
| 17 | 168 | 69 | 237 | """ TinyViT
Paper: `TinyViT: Fast Pretraining Distillation for Small Vision Transformers`
- https://arxiv.org/abs/2207.10666
Adapted from official impl at https://github.com/microsoft/Cream/tree/main/TinyViT
"""
__all__ = ['TinyVit']
import math
import itertools
from functools import partial
from typing import ... | """ TinyViT
Paper: `TinyViT: Fast Pretraining Distillation for Small Vision Transformers`
- https://arxiv.org/abs/2207.10666
Adapted from official impl at https://github.com/microsoft/Cream/tree/main/TinyViT
"""
__all__ = ['TinyVit']
import math
import itertools
from functools import partial
from typing import ... |
17,894 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/models/tiny_vit.py | 517,519 | 419,558 | group_matcher | TinyVit | 519,528 | 519,528 | class TinyVit(nn.Module):
def __init__(
self,
in_chans=3,
num_classes=1000,
global_pool='avg',
embed_dims=(96, 192, 384, 768),
depths=(2, 2, 6, 2),
num_heads=(3, 6, 12, 24),
window_sizes=(7, 7, 14, 7),
mlp_ra... | class TinyVit(nn.Module):
def __init__(
self,
in_chans=3,
num_classes=1000,
global_pool='avg',
embed_dims=(96, 192, 384, 768),
depths=(2, 2, 6, 2),
num_heads=(3, 6, 12, 24),
window_sizes=(7, 7, 14, 7),
mlp_ra... | matcher = dict(
stem=r'^patch_embed',
blocks=r'^stages\.(\d+)' if coarse else [
(r'^stages\.(\d+).downsample', (0,)),
(r'^stages\.(\d+)\.\w+\.(\d+)', None),
]
)
return matcher
| matcher = dict(
stem=r'^patch_embed',
blocks=r'^stages\.(\d+)' if coarse else [
(r'^stages\.(\d+).downsample', (0,)),
(r'^stages\.(\d+)\.\w+\.(\d+)', None),
]
)
return matcher
| 17 | 1,382 | 105 | 1,487 | """ TinyViT
Paper: `TinyViT: Fast Pretraining Distillation for Small Vision Transformers`
- https://arxiv.org/abs/2207.10666
Adapted from official impl at https://github.com/microsoft/Cream/tree/main/TinyViT
"""
__all__ = ['TinyVit']
import math
import itertools
from functools import partial
from typing import ... | """ TinyViT
Paper: `TinyViT: Fast Pretraining Distillation for Small Vision Transformers`
- https://arxiv.org/abs/2207.10666
Adapted from official impl at https://github.com/microsoft/Cream/tree/main/TinyViT
"""
__all__ = ['TinyVit']
import math
import itertools
from functools import partial
from typing import ... |
17,895 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/models/tiny_vit.py | 299,300 | 247,344 | forward | TinyVitBlock | 300,339 | 300,339 | class TinyVitBlock(nn.Module):
""" TinyViT Block.
Args:
dim (int): Number of input channels.
num_heads (int): Number of attention heads.
window_size (int): Window size.
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
drop (float, optional): Dropout rate. Def... | class TinyVitBlock(nn.Module):
""" TinyViT Block.
Args:
dim (int): Number of input channels.
num_heads (int): Number of attention heads.
window_size (int): Window size.
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
drop (float, optional): Dropout rate. Def... | B, H, W, C = x.shape
L = H * W
shortcut = x
if H == self.window_size and W == self.window_size:
x = x.reshape(B, L, C)
x = self.attn(x)
x = x.view(B, H, W, C)
else:
pad_b = (self.window_size - H % self.window_size) % self.window_si... | B, H, W, C = x.shape
L = H * W
shortcut = x
if H == self.window_size and W == self.window_size:
x = x.reshape(B, L, C)
x = self.attn(x)
x = x.view(B, H, W, C)
else:
pad_b = (self.window_size - H % self.window_size) % self.window_si... | 171 | 666 | 553 | 1,219 | """ TinyViT
Paper: `TinyViT: Fast Pretraining Distillation for Small Vision Transformers`
- https://arxiv.org/abs/2207.10666
Adapted from official impl at https://github.com/microsoft/Cream/tree/main/TinyViT
"""
__all__ = ['TinyVit']
import math
import itertools
from functools import partial
from typing import ... | """ TinyViT
Paper: `TinyViT: Fast Pretraining Distillation for Small Vision Transformers`
- https://arxiv.org/abs/2207.10666
Adapted from official impl at https://github.com/microsoft/Cream/tree/main/TinyViT
"""
__all__ = ['TinyVit']
import math
import itertools
from functools import partial
from typing import ... |
17,896 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/models/tiny_vit.py | 408,409 | 347,419 | forward | TinyVitStage | 409,415 | 409,415 | class TinyVitStage(nn.Module):
""" A basic TinyViT layer for one stage.
Args:
dim (int): Number of input channels.
out_dim: the output dimension of the layer
depth (int): Number of blocks.
num_heads (int): Number of attention heads.
window_size (int): Local window size.
... | class TinyVitStage(nn.Module):
""" A basic TinyViT layer for one stage.
Args:
dim (int): Number of input channels.
out_dim: the output dimension of the layer
depth (int): Number of blocks.
num_heads (int): Number of attention heads.
window_size (int): Local window size.
... | x = self.downsample(x)
x = x.permute(0, 2, 3, 1) # BCHW -> BHWC
x = self.blocks(x)
x = x.permute(0, 3, 1, 2) # BHWC -> BCHW
return x
| x = self.downsample(x)
x = x.permute(0, 2, 3, 1) # BCHW -> BHWC
x = self.blocks(x)
x = x.permute(0, 3, 1, 2) # BHWC -> BCHW
return x
| 28 | 628 | 96 | 724 | """ TinyViT
Paper: `TinyViT: Fast Pretraining Distillation for Small Vision Transformers`
- https://arxiv.org/abs/2207.10666
Adapted from official impl at https://github.com/microsoft/Cream/tree/main/TinyViT
"""
__all__ = ['TinyVit']
import math
import itertools
from functools import partial
from typing import ... | """ TinyViT
Paper: `TinyViT: Fast Pretraining Distillation for Small Vision Transformers`
- https://arxiv.org/abs/2207.10666
Adapted from official impl at https://github.com/microsoft/Cream/tree/main/TinyViT
"""
__all__ = ['TinyVit']
import math
import itertools
from functools import partial
from typing import ... |
17,898 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/models/tnt.py | 63,64 | 46,79 | forward | Attention | 64,79 | 64,79 | class Attention(nn.Module):
""" Multi-Head Attention
"""
def __init__(self, dim, hidden_dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.):
super().__init__()
self.hidden_dim = hidden_dim
self.num_heads = num_heads
head_dim = hidden_dim // num_heads
self.he... | class Attention(nn.Module):
""" Multi-Head Attention
"""
def __init__(self, dim, hidden_dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.):
super().__init__()
self.hidden_dim = hidden_dim
self.num_heads = num_heads
head_dim = hidden_dim // num_heads
self.he... | B, N, C = x.shape
qk = self.qk(x).reshape(B, N, 2, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
q, k = qk.unbind(0) # make torchscript happy (cannot use tensor as tuple)
v = self.v(x).reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3)
attn = (q @ k.transpose(-2, -1... | B, N, C = x.shape
qk = self.qk(x).reshape(B, N, 2, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
q, k = qk.unbind(0) # make torchscript happy (cannot use tensor as tuple)
v = self.v(x).reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3)
attn = (q @ k.transpose(-2, -1... | 68 | 274 | 260 | 534 | """ Transformer in Transformer (TNT) in PyTorch
A PyTorch implement of TNT as described in
'Transformer in Transformer' - https://arxiv.org/abs/2103.00112
The official mindspore code is released and available at
https://gitee.com/mindspore/mindspore/tree/master/model_zoo/research/cv/TNT
"""
import math
import torch
... | """ Transformer in Transformer (TNT) in PyTorch
A PyTorch implement of TNT as described in
'Transformer in Transformer' - https://arxiv.org/abs/2103.00112
The official mindspore code is released and available at
https://gitee.com/mindspore/mindspore/tree/master/model_zoo/research/cv/TNT
"""
import math
import torch
... |
17,899 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/models/tnt.py | 142,143 | 79,156 | forward | Block | 143,156 | 143,156 | class Block(nn.Module):
""" TNT Block
"""
def __init__(
self,
dim,
dim_out,
num_pixel,
num_heads_in=4,
num_heads_out=12,
mlp_ratio=4.,
qkv_bias=False,
proj_drop=0.,
attn_drop=0.,
... | class Block(nn.Module):
""" TNT Block
"""
def __init__(
self,
dim,
dim_out,
num_pixel,
num_heads_in=4,
num_heads_out=12,
mlp_ratio=4.,
qkv_bias=False,
proj_drop=0.,
attn_drop=0.,
... | # inner
pixel_embed = pixel_embed + self.drop_path(self.attn_in(self.norm_in(pixel_embed)))
pixel_embed = pixel_embed + self.drop_path(self.mlp_in(self.norm_mlp_in(pixel_embed)))
# outer
B, N, C = patch_embed.size()
patch_embed = torch.cat(
[patch_embed[:, 0:1... | # inner
pixel_embed = pixel_embed + self.drop_path(self.attn_in(self.norm_in(pixel_embed)))
pixel_embed = pixel_embed + self.drop_path(self.mlp_in(self.norm_mlp_in(pixel_embed)))
# outer
B, N, C = patch_embed.size()
patch_embed = torch.cat(
[patch_embed[:, 0:1... | 46 | 612 | 252 | 864 | """ Transformer in Transformer (TNT) in PyTorch
A PyTorch implement of TNT as described in
'Transformer in Transformer' - https://arxiv.org/abs/2103.00112
The official mindspore code is released and available at
https://gitee.com/mindspore/mindspore/tree/master/model_zoo/research/cv/TNT
"""
import math
import torch
... | """ Transformer in Transformer (TNT) in PyTorch
A PyTorch implement of TNT as described in
'Transformer in Transformer' - https://arxiv.org/abs/2103.00112
The official mindspore code is released and available at
https://gitee.com/mindspore/mindspore/tree/master/model_zoo/research/cv/TNT
"""
import math
import torch
... |
17,900 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/models/tnt.py | 175,176 | 156,189 | forward | PixelEmbed | 176,189 | 176,189 | class PixelEmbed(nn.Module):
""" Image to Pixel Embedding
"""
def __init__(self, img_size=224, patch_size=16, in_chans=3, in_dim=48, stride=4):
super().__init__()
img_size = to_2tuple(img_size)
patch_size = to_2tuple(patch_size)
# grid_size property necessary for resizing pos... | class PixelEmbed(nn.Module):
""" Image to Pixel Embedding
"""
def __init__(self, img_size=224, patch_size=16, in_chans=3, in_dim=48, stride=4):
super().__init__()
img_size = to_2tuple(img_size)
patch_size = to_2tuple(patch_size)
# grid_size property necessary for resizing pos... | B, C, H, W = x.shape
_assert(H == self.img_size[0],
f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]}).")
_assert(W == self.img_size[1],
f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]}).")
... | B, C, H, W = x.shape
_assert(H == self.img_size[0],
f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]}).")
_assert(W == self.img_size[1],
f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]}).")
... | 58 | 354 | 259 | 613 | """ Transformer in Transformer (TNT) in PyTorch
A PyTorch implement of TNT as described in
'Transformer in Transformer' - https://arxiv.org/abs/2103.00112
The official mindspore code is released and available at
https://gitee.com/mindspore/mindspore/tree/master/model_zoo/research/cv/TNT
"""
import math
import torch
... | """ Transformer in Transformer (TNT) in PyTorch
A PyTorch implement of TNT as described in
'Transformer in Transformer' - https://arxiv.org/abs/2103.00112
The official mindspore code is released and available at
https://gitee.com/mindspore/mindspore/tree/master/model_zoo/research/cv/TNT
"""
import math
import torch
... |
17,901 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/models/tnt.py | 332,333 | 189,338 | forward | TNT | 333,338 | 333,338 | class TNT(nn.Module):
""" Transformer in Transformer - https://arxiv.org/abs/2103.00112
"""
def __init__(
self,
img_size=224,
patch_size=16,
in_chans=3,
num_classes=1000,
global_pool='token',
embed_dim=768,
inner... | class TNT(nn.Module):
""" Transformer in Transformer - https://arxiv.org/abs/2103.00112
"""
def __init__(
self,
img_size=224,
patch_size=16,
in_chans=3,
num_classes=1000,
global_pool='token',
embed_dim=768,
inner... | x = self.forward_features(x)
x = self.forward_head(x)
return x
| x = self.forward_features(x)
x = self.forward_head(x)
return x
| 8 | 1,846 | 35 | 1,881 | """ Transformer in Transformer (TNT) in PyTorch
A PyTorch implement of TNT as described in
'Transformer in Transformer' - https://arxiv.org/abs/2103.00112
The official mindspore code is released and available at
https://gitee.com/mindspore/mindspore/tree/master/model_zoo/research/cv/TNT
"""
import math
import torch
... | """ Transformer in Transformer (TNT) in PyTorch
A PyTorch implement of TNT as described in
'Transformer in Transformer' - https://arxiv.org/abs/2103.00112
The official mindspore code is released and available at
https://gitee.com/mindspore/mindspore/tree/master/model_zoo/research/cv/TNT
"""
import math
import torch
... |
17,903 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/models/tresnet.py | 54,55 | 23,68 | forward | BasicBlock | 55,68 | 55,68 | class BasicBlock(nn.Module):
expansion = 1
def __init__(
self,
inplanes,
planes,
stride=1,
downsample=None,
use_se=True,
aa_layer=None,
drop_path_rate=0.
):
super(BasicBlock, self).__init__()
sel... | class BasicBlock(nn.Module):
expansion = 1
def __init__(
self,
inplanes,
planes,
stride=1,
downsample=None,
use_se=True,
aa_layer=None,
drop_path_rate=0.
):
super(BasicBlock, self).__init__()
sel... | if self.downsample is not None:
shortcut = self.downsample(x)
else:
shortcut = x
out = self.conv1(x)
out = self.conv2(out)
if self.se is not None:
out = self.se(out)
out = self.drop_path(out) + shortcut
out = self.act(out)
... | if self.downsample is not None:
shortcut = self.downsample(x)
else:
shortcut = x
out = self.conv1(x)
out = self.conv2(out)
if self.se is not None:
out = self.se(out)
out = self.drop_path(out) + shortcut
out = self.act(out)
... | 36 | 407 | 116 | 523 | """
TResNet: High Performance GPU-Dedicated Architecture
https://arxiv.org/pdf/2003.13630.pdf
Original model: https://github.com/mrT23/TResNet
"""
from collections import OrderedDict
from functools import partial
import torch
import torch.nn as nn
from timm.layers import SpaceToDepth, BlurPool2d, ClassifierHead, SE... | """
TResNet: High Performance GPU-Dedicated Architecture
https://arxiv.org/pdf/2003.13630.pdf
Original model: https://github.com/mrT23/TResNet
"""
from collections import OrderedDict
from functools import partial
import torch
import torch.nn as nn
from timm.layers import SpaceToDepth, BlurPool2d, ClassifierHead, SE... |
17,904 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/models/tresnet.py | 105,106 | 68,120 | forward | Bottleneck | 106,120 | 106,120 | class Bottleneck(nn.Module):
expansion = 4
def __init__(
self,
inplanes,
planes,
stride=1,
downsample=None,
use_se=True,
act_layer=None,
aa_layer=None,
drop_path_rate=0.,
):
super(Bottleneck,... | class Bottleneck(nn.Module):
expansion = 4
def __init__(
self,
inplanes,
planes,
stride=1,
downsample=None,
use_se=True,
act_layer=None,
aa_layer=None,
drop_path_rate=0.,
):
super(Bottleneck,... | if self.downsample is not None:
shortcut = self.downsample(x)
else:
shortcut = x
out = self.conv1(x)
out = self.conv2(out)
if self.se is not None:
out = self.se(out)
out = self.conv3(out)
out = self.drop_path(out) + shortcut
... | if self.downsample is not None:
shortcut = self.downsample(x)
else:
shortcut = x
out = self.conv1(x)
out = self.conv2(out)
if self.se is not None:
out = self.se(out)
out = self.conv3(out)
out = self.drop_path(out) + shortcut
... | 39 | 464 | 128 | 592 | """
TResNet: High Performance GPU-Dedicated Architecture
https://arxiv.org/pdf/2003.13630.pdf
Original model: https://github.com/mrT23/TResNet
"""
from collections import OrderedDict
from functools import partial
import torch
import torch.nn as nn
from timm.layers import SpaceToDepth, BlurPool2d, ClassifierHead, SE... | """
TResNet: High Performance GPU-Dedicated Architecture
https://arxiv.org/pdf/2003.13630.pdf
Original model: https://github.com/mrT23/TResNet
"""
from collections import OrderedDict
from functools import partial
import torch
import torch.nn as nn
from timm.layers import SpaceToDepth, BlurPool2d, ClassifierHead, SE... |
17,905 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/models/tresnet.py | 198,199 | 120,262 | _make_layer | TResNet | 199,223 | 199,223 | class TResNet(nn.Module):
def __init__(
self,
layers,
in_chans=3,
num_classes=1000,
width_factor=1.0,
v2=False,
global_pool='fast',
drop_rate=0.,
drop_path_rate=0.,
):
self.num_classes = num_class... | class TResNet(nn.Module):
def __init__(
self,
layers,
in_chans=3,
num_classes=1000,
width_factor=1.0,
v2=False,
global_pool='fast',
drop_rate=0.,
drop_path_rate=0.,
):
self.num_classes = num_class... | downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
layers = []
if stride == 2:
# avg pooling before 1x1 conv
layers.append(nn.AvgPool2d(kernel_size=2, stride=2, ceil_mode=True, count_include_pad=False))
layer... | downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
layers = []
if stride == 2:
# avg pooling before 1x1 conv
layers.append(nn.AvgPool2d(kernel_size=2, stride=2, ceil_mode=True, count_include_pad=False))
layer... | 84 | 1,584 | 332 | 1,916 | """
TResNet: High Performance GPU-Dedicated Architecture
https://arxiv.org/pdf/2003.13630.pdf
Original model: https://github.com/mrT23/TResNet
"""
from collections import OrderedDict
from functools import partial
import torch
import torch.nn as nn
from timm.layers import SpaceToDepth, BlurPool2d, ClassifierHead, SE... | """
TResNet: High Performance GPU-Dedicated Architecture
https://arxiv.org/pdf/2003.13630.pdf
Original model: https://github.com/mrT23/TResNet
"""
from collections import OrderedDict
from functools import partial
import torch
import torch.nn as nn
from timm.layers import SpaceToDepth, BlurPool2d, ClassifierHead, SE... |
17,907 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/models/twins.py | 226,227 | 192,232 | forward | Block | 227,232 | 227,232 | class Block(nn.Module):
def __init__(
self,
dim,
num_heads,
mlp_ratio=4.,
proj_drop=0.,
attn_drop=0.,
drop_path=0.,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm,
sr_ratio=1,
ws=None,
... | class Block(nn.Module):
def __init__(
self,
dim,
num_heads,
mlp_ratio=4.,
proj_drop=0.,
attn_drop=0.,
drop_path=0.,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm,
sr_ratio=1,
ws=None,
... | x = x + self.drop_path1(self.attn(self.norm1(x), size))
x = x + self.drop_path2(self.mlp(self.norm2(x)))
return x
| x = x + self.drop_path1(self.attn(self.norm1(x), size))
x = x + self.drop_path2(self.mlp(self.norm2(x)))
return x
| 13 | 400 | 63 | 463 | """ Twins
A PyTorch impl of : `Twins: Revisiting the Design of Spatial Attention in Vision Transformers`
- https://arxiv.org/pdf/2104.13840.pdf
Code/weights from https://github.com/Meituan-AutoML/Twins, original copyright/license info below
"""
# --------------------------------------------------------
# Twins
# ... | """ Twins
A PyTorch impl of : `Twins: Revisiting the Design of Spatial Attention in Vision Transformers`
- https://arxiv.org/pdf/2104.13840.pdf
Code/weights from https://github.com/Meituan-AutoML/Twins, original copyright/license info below
"""
# --------------------------------------------------------
# Twins
# ... |
17,908 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/models/twins.py | 162,163 | 133,192 | forward | GlobalSubSampleAttn | 163,192 | 163,192 | class GlobalSubSampleAttn(nn.Module):
""" GSA: using a key to summarize the information for a group to be efficient.
"""
fused_attn: torch.jit.Final[bool]
def __init__(self, dim, num_heads=8, attn_drop=0., proj_drop=0., sr_ratio=1):
super().__init__()
assert dim % num_heads == 0, f"dim... | class GlobalSubSampleAttn(nn.Module):
""" GSA: using a key to summarize the information for a group to be efficient.
"""
fused_attn: torch.jit.Final[bool]
def __init__(self, dim, num_heads=8, attn_drop=0., proj_drop=0., sr_ratio=1):
super().__init__()
assert dim % num_heads == 0, f"dim... | B, N, C = x.shape
q = self.q(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
if self.sr is not None:
x = x.permute(0, 2, 1).reshape(B, C, *size)
x = self.sr(x).reshape(B, C, -1).permute(0, 2, 1)
x = self.norm(x)
kv = self.kv(... | B, N, C = x.shape
q = self.q(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
if self.sr is not None:
x = x.permute(0, 2, 1).reshape(B, C, *size)
x = self.sr(x).reshape(B, C, -1).permute(0, 2, 1)
x = self.norm(x)
kv = self.kv(... | 106 | 409 | 429 | 838 | """ Twins
A PyTorch impl of : `Twins: Revisiting the Design of Spatial Attention in Vision Transformers`
- https://arxiv.org/pdf/2104.13840.pdf
Code/weights from https://github.com/Meituan-AutoML/Twins, original copyright/license info below
"""
# --------------------------------------------------------
# Twins
# ... | """ Twins
A PyTorch impl of : `Twins: Revisiting the Design of Spatial Attention in Vision Transformers`
- https://arxiv.org/pdf/2104.13840.pdf
Code/weights from https://github.com/Meituan-AutoML/Twins, original copyright/license info below
"""
# --------------------------------------------------------
# Twins
# ... |
17,909 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/models/twins.py | 58,59 | 35,133 | forward | LocallyGroupedAttn | 59,133 | 59,133 | @register_notrace_module # reason: FX can't symbolically trace control flow in forward method
class LocallyGroupedAttn(nn.Module):
""" LSA: self attention within a group
"""
fused_attn: torch.jit.Final[bool]
def __init__(self, dim, num_heads=8, attn_drop=0., proj_drop=0., ws=1):
assert ws != 1... | @register_notrace_module # reason: FX can't symbolically trace control flow in forward method
class LocallyGroupedAttn(nn.Module):
""" LSA: self attention within a group
"""
fused_attn: torch.jit.Final[bool]
def __init__(self, dim, num_heads=8, attn_drop=0., proj_drop=0., ws=1):
assert ws != 1... | # There are two implementations for this function, zero padding or mask. We don't observe obvious difference for
# both. You can choose any one, we recommend forward_padding because it's neat. However,
# the masking implementation is more reasonable and accurate.
B, N, C = x.shape
... | # There are two implementations for this function, zero padding or mask. We don't observe obvious difference for
# both. You can choose any one, we recommend forward_padding because it's neat. However,
# the masking implementation is more reasonable and accurate.
B, N, C = x.shape
... | 506 | 342 | 1,612 | 1,954 | """ Twins
A PyTorch impl of : `Twins: Revisiting the Design of Spatial Attention in Vision Transformers`
- https://arxiv.org/pdf/2104.13840.pdf
Code/weights from https://github.com/Meituan-AutoML/Twins, original copyright/license info below
"""
# --------------------------------------------------------
# Twins
# ... | """ Twins
A PyTorch impl of : `Twins: Revisiting the Design of Spatial Attention in Vision Transformers`
- https://arxiv.org/pdf/2104.13840.pdf
Code/weights from https://github.com/Meituan-AutoML/Twins, original copyright/license info below
"""
# --------------------------------------------------------
# Twins
# ... |
17,910 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/models/twins.py | 272,273 | 254,282 | forward | PatchEmbed | 273,282 | 273,282 | class PatchEmbed(nn.Module):
""" Image to Patch Embedding
"""
def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
super().__init__()
img_size = to_2tuple(img_size)
patch_size = to_2tuple(patch_size)
self.img_size = img_size
self.patch_size = ... | class PatchEmbed(nn.Module):
""" Image to Patch Embedding
"""
def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
super().__init__()
img_size = to_2tuple(img_size)
patch_size = to_2tuple(patch_size)
self.img_size = img_size
self.patch_size = ... | B, C, H, W = x.shape
x = self.proj(x).flatten(2).transpose(1, 2)
x = self.norm(x)
out_size = (H // self.patch_size[0], W // self.patch_size[1])
return x, out_size
| B, C, H, W = x.shape
x = self.proj(x).flatten(2).transpose(1, 2)
x = self.norm(x)
out_size = (H // self.patch_size[0], W // self.patch_size[1])
return x, out_size
| 24 | 327 | 94 | 421 | """ Twins
A PyTorch impl of : `Twins: Revisiting the Design of Spatial Attention in Vision Transformers`
- https://arxiv.org/pdf/2104.13840.pdf
Code/weights from https://github.com/Meituan-AutoML/Twins, original copyright/license info below
"""
# --------------------------------------------------------
# Twins
# ... | """ Twins
A PyTorch impl of : `Twins: Revisiting the Design of Spatial Attention in Vision Transformers`
- https://arxiv.org/pdf/2104.13840.pdf
Code/weights from https://github.com/Meituan-AutoML/Twins, original copyright/license info below
"""
# --------------------------------------------------------
# Twins
# ... |
17,911 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/models/twins.py | 241,242 | 232,254 | forward | PosConv | 242,250 | 242,250 | class PosConv(nn.Module):
# PEG from https://arxiv.org/abs/2102.10882
def __init__(self, in_chans, embed_dim=768, stride=1):
super(PosConv, self).__init__()
self.proj = nn.Sequential(
nn.Conv2d(in_chans, embed_dim, 3, stride, 1, bias=True, groups=embed_dim),
)
self.s... | class PosConv(nn.Module):
# PEG from https://arxiv.org/abs/2102.10882
def __init__(self, in_chans, embed_dim=768, stride=1):
super(PosConv, self).__init__()
self.proj = nn.Sequential(
nn.Conv2d(in_chans, embed_dim, 3, stride, 1, bias=True, groups=embed_dim),
)
self.s... | B, N, C = x.shape
cnn_feat_token = x.transpose(1, 2).view(B, C, *size)
x = self.proj(cnn_feat_token)
if self.stride == 1:
x += cnn_feat_token
x = x.flatten(2).transpose(1, 2)
return x
| B, N, C = x.shape
cnn_feat_token = x.transpose(1, 2).view(B, C, *size)
x = self.proj(cnn_feat_token)
if self.stride == 1:
x += cnn_feat_token
x = x.flatten(2).transpose(1, 2)
return x
| 27 | 199 | 109 | 308 | """ Twins
A PyTorch impl of : `Twins: Revisiting the Design of Spatial Attention in Vision Transformers`
- https://arxiv.org/pdf/2104.13840.pdf
Code/weights from https://github.com/Meituan-AutoML/Twins, original copyright/license info below
"""
# --------------------------------------------------------
# Twins
# ... | """ Twins
A PyTorch impl of : `Twins: Revisiting the Design of Spatial Attention in Vision Transformers`
- https://arxiv.org/pdf/2104.13840.pdf
Code/weights from https://github.com/Meituan-AutoML/Twins, original copyright/license info below
"""
# --------------------------------------------------------
# Twins
# ... |
17,914 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/models/vgg.py | 53,54 | 31,66 | forward | ConvMlp | 54,66 | 54,66 | @register_notrace_module # reason: FX can't symbolically trace control flow in forward method
class ConvMlp(nn.Module):
def __init__(
self,
in_features=512,
out_features=4096,
kernel_size=7,
mlp_ratio=1.0,
drop_rate: float = 0.2,
... | @register_notrace_module # reason: FX can't symbolically trace control flow in forward method
class ConvMlp(nn.Module):
def __init__(
self,
in_features=512,
out_features=4096,
kernel_size=7,
mlp_ratio=1.0,
drop_rate: float = 0.2,
... | if x.shape[-2] < self.input_kernel_size or x.shape[-1] < self.input_kernel_size:
# keep the input size >= 7x7
output_size = (max(self.input_kernel_size, x.shape[-2]), max(self.input_kernel_size, x.shape[-1]))
x = F.adaptive_avg_pool2d(x, output_size)
x = self.fc1(x)
... | if x.shape[-2] < self.input_kernel_size or x.shape[-1] < self.input_kernel_size:
# keep the input size >= 7x7
output_size = (max(self.input_kernel_size, x.shape[-2]), max(self.input_kernel_size, x.shape[-1]))
x = F.adaptive_avg_pool2d(x, output_size)
x = self.fc1(x)
... | 42 | 290 | 178 | 468 | """VGG
Adapted from https://github.com/pytorch/vision 'vgg.py' (BSD-3-Clause) with a few changes for
timm functionality.
Copyright 2021 Ross Wightman
"""
from typing import Union, List, Dict, Any, cast
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.data import IMAGENET_DEFAULT_MEAN, IM... | """VGG
Adapted from https://github.com/pytorch/vision 'vgg.py' (BSD-3-Clause) with a few changes for
timm functionality.
Copyright 2021 Ross Wightman
"""
from typing import Union, List, Dict, Any, cast
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.data import IMAGENET_DEFAULT_MEAN, IM... |
17,915 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/models/vgg.py | 128,130 | 66,177 | group_matcher | VGG | 130,133 | 130,133 | class VGG(nn.Module):
def __init__(
self,
cfg: List[Any],
num_classes: int = 1000,
in_chans: int = 3,
output_stride: int = 32,
mlp_ratio: float = 1.0,
act_layer: nn.Module = nn.ReLU,
conv_layer: nn.Module = nn.Conv2d,
... | class VGG(nn.Module):
def __init__(
self,
cfg: List[Any],
num_classes: int = 1000,
in_chans: int = 3,
output_stride: int = 32,
mlp_ratio: float = 1.0,
act_layer: nn.Module = nn.ReLU,
conv_layer: nn.Module = nn.Conv2d,
... | # this treats BN layers as separate groups for bn variants, a lot of effort to fix that
return dict(stem=r'^features\.0', blocks=r'^features\.(\d+)')
| # this treats BN layers as separate groups for bn variants, a lot of effort to fix that
return dict(stem=r'^features\.0', blocks=r'^features\.(\d+)')
| 21 | 1,236 | 56 | 1,292 | """VGG
Adapted from https://github.com/pytorch/vision 'vgg.py' (BSD-3-Clause) with a few changes for
timm functionality.
Copyright 2021 Ross Wightman
"""
from typing import Union, List, Dict, Any, cast
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.data import IMAGENET_DEFAULT_MEAN, IM... | """VGG
Adapted from https://github.com/pytorch/vision 'vgg.py' (BSD-3-Clause) with a few changes for
timm functionality.
Copyright 2021 Ross Wightman
"""
from typing import Union, List, Dict, Any, cast
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.data import IMAGENET_DEFAULT_MEAN, IM... |
17,917 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/models/visformer.py | 90,91 | 73,112 | forward | Attention | 91,112 | 91,112 | class Attention(nn.Module):
fused_attn: torch.jit.Final[bool]
def __init__(self, dim, num_heads=8, head_dim_ratio=1., attn_drop=0., proj_drop=0.):
super().__init__()
self.dim = dim
self.num_heads = num_heads
head_dim = round(dim // num_heads * head_dim_ratio)
self.head_d... | class Attention(nn.Module):
fused_attn: torch.jit.Final[bool]
def __init__(self, dim, num_heads=8, head_dim_ratio=1., attn_drop=0., proj_drop=0.):
super().__init__()
self.dim = dim
self.num_heads = num_heads
head_dim = round(dim // num_heads * head_dim_ratio)
self.head_d... | B, C, H, W = x.shape
x = self.qkv(x).reshape(B, 3, self.num_heads, self.head_dim, -1).permute(1, 0, 2, 4, 3)
q, k, v = x.unbind(0)
if self.fused_attn:
x = torch.nn.functional.scaled_dot_product_attention(
q.contiguous(), k.contiguous(), v.contiguous(),
... | B, C, H, W = x.shape
x = self.qkv(x).reshape(B, 3, self.num_heads, self.head_dim, -1).permute(1, 0, 2, 4, 3)
q, k, v = x.unbind(0)
if self.fused_attn:
x = torch.nn.functional.scaled_dot_product_attention(
q.contiguous(), k.contiguous(), v.contiguous(),
... | 73 | 315 | 309 | 624 | """ Visformer
Paper: Visformer: The Vision-friendly Transformer - https://arxiv.org/abs/2104.12533
From original at https://github.com/danczs/Visformer
Modifications and additions for timm hacked together by / Copyright 2021, Ross Wightman
"""
import torch
import torch.nn as nn
from timm.data import IMAGENET_DEFAU... | """ Visformer
Paper: Visformer: The Vision-friendly Transformer - https://arxiv.org/abs/2104.12533
From original at https://github.com/danczs/Visformer
Modifications and additions for timm hacked together by / Copyright 2021, Ross Wightman
"""
import torch
import torch.nn as nn
from timm.data import IMAGENET_DEFAU... |
17,918 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/models/visformer.py | 154,155 | 112,161 | forward | Block | 155,161 | 155,161 | class Block(nn.Module):
def __init__(
self,
dim,
num_heads,
head_dim_ratio=1.,
mlp_ratio=4.,
proj_drop=0.,
attn_drop=0.,
drop_path=0.,
act_layer=nn.GELU,
norm_layer=LayerNorm2d,
group=... | class Block(nn.Module):
def __init__(
self,
dim,
num_heads,
head_dim_ratio=1.,
mlp_ratio=4.,
proj_drop=0.,
attn_drop=0.,
drop_path=0.,
act_layer=nn.GELU,
norm_layer=LayerNorm2d,
group=... | if self.attn is not None:
x = x + self.drop_path(self.attn(self.norm1(x)))
x = x + self.drop_path(self.mlp(self.norm2(x)))
return x
| if self.attn is not None:
x = x + self.drop_path(self.attn(self.norm1(x)))
x = x + self.drop_path(self.mlp(self.norm2(x)))
return x
| 17 | 386 | 71 | 457 | """ Visformer
Paper: Visformer: The Vision-friendly Transformer - https://arxiv.org/abs/2104.12533
From original at https://github.com/danczs/Visformer
Modifications and additions for timm hacked together by / Copyright 2021, Ross Wightman
"""
import torch
import torch.nn as nn
from timm.data import IMAGENET_DEFAU... | """ Visformer
Paper: Visformer: The Vision-friendly Transformer - https://arxiv.org/abs/2104.12533
From original at https://github.com/danczs/Visformer
Modifications and additions for timm hacked together by / Copyright 2021, Ross Wightman
"""
import torch
import torch.nn as nn
from timm.data import IMAGENET_DEFAU... |
17,919 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/models/visformer.py | 61,62 | 22,73 | forward | SpatialMlp | 62,73 | 62,73 | class SpatialMlp(nn.Module):
def __init__(
self,
in_features,
hidden_features=None,
out_features=None,
act_layer=nn.GELU,
drop=0.,
group=8,
spatial_conv=False,
):
super().__init__()
out_features = out... | class SpatialMlp(nn.Module):
def __init__(
self,
in_features,
hidden_features=None,
out_features=None,
act_layer=nn.GELU,
drop=0.,
group=8,
spatial_conv=False,
):
super().__init__()
out_features = out... | x = self.conv1(x)
x = self.act1(x)
x = self.drop1(x)
if self.conv2 is not None:
x = self.conv2(x)
x = self.act2(x)
x = self.conv3(x)
x = self.drop3(x)
return x
| x = self.conv1(x)
x = self.act1(x)
x = self.drop1(x)
if self.conv2 is not None:
x = self.conv2(x)
x = self.act2(x)
x = self.conv3(x)
x = self.drop3(x)
return x
| 28 | 470 | 105 | 575 | """ Visformer
Paper: Visformer: The Vision-friendly Transformer - https://arxiv.org/abs/2104.12533
From original at https://github.com/danczs/Visformer
Modifications and additions for timm hacked together by / Copyright 2021, Ross Wightman
"""
import torch
import torch.nn as nn
from timm.data import IMAGENET_DEFAU... | """ Visformer
Paper: Visformer: The Vision-friendly Transformer - https://arxiv.org/abs/2104.12533
From original at https://github.com/danczs/Visformer
Modifications and additions for timm hacked together by / Copyright 2021, Ross Wightman
"""
import torch
import torch.nn as nn
from timm.data import IMAGENET_DEFAU... |
17,922 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/models/vision_transformer_hybrid.py | 119,120 | 32,136 | forward | HybridEmbed | 120,136 | 120,136 | class HybridEmbed(nn.Module):
""" CNN Feature Map Embedding
Extract feature map from CNN, flatten, project to embedding dim.
"""
output_fmt: Format
dynamic_img_pad: torch.jit.Final[bool]
def __init__(
self,
backbone,
img_size=224,
patch_size=1,
... | class HybridEmbed(nn.Module):
""" CNN Feature Map Embedding
Extract feature map from CNN, flatten, project to embedding dim.
"""
output_fmt: Format
dynamic_img_pad: torch.jit.Final[bool]
def __init__(
self,
backbone,
img_size=224,
patch_size=1,
... | x = self.backbone(x)
if isinstance(x, (list, tuple)):
x = x[-1] # last feature if backbone outputs list/tuple of features
_, _, H, W = x.shape
if self.dynamic_img_pad:
pad_h = (self.patch_size[0] - H % self.patch_size[0]) % self.patch_size[0]
pad_w = ... | x = self.backbone(x)
if isinstance(x, (list, tuple)):
x = x[-1] # last feature if backbone outputs list/tuple of features
_, _, H, W = x.shape
if self.dynamic_img_pad:
pad_h = (self.patch_size[0] - H % self.patch_size[0]) % self.patch_size[0]
pad_w = ... | 75 | 1,150 | 267 | 1,417 | """ Hybrid Vision Transformer (ViT) in PyTorch
A PyTorch implement of the Hybrid Vision Transformers as described in:
'An Image Is Worth 16 x 16 Words: Transformers for Image Recognition at Scale'
- https://arxiv.org/abs/2010.11929
`How to train your ViT? Data, Augmentation, and Regularization in Vision Transfor... | """ Hybrid Vision Transformer (ViT) in PyTorch
A PyTorch implement of the Hybrid Vision Transformers as described in:
'An Image Is Worth 16 x 16 Words: Transformers for Image Recognition at Scale'
- https://arxiv.org/abs/2010.11929
`How to train your ViT? Data, Augmentation, and Regularization in Vision Transfor... |
17,923 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/models/vision_transformer_hybrid.py | 160,161 | 136,168 | forward | HybridEmbedWithSize | 161,168 | 161,168 | class HybridEmbedWithSize(nn.Module):
""" CNN Feature Map Embedding
Extract feature map from CNN, flatten, project to embedding dim.
"""
def __init__(
self,
backbone,
img_size=224,
patch_size=1,
feature_size=None,
in_chans=3,
... | class HybridEmbedWithSize(nn.Module):
""" CNN Feature Map Embedding
Extract feature map from CNN, flatten, project to embedding dim.
"""
def __init__(
self,
backbone,
img_size=224,
patch_size=1,
feature_size=None,
in_chans=3,
... | x = self.backbone(x)
if isinstance(x, (list, tuple)):
x = x[-1] # last feature if backbone outputs list/tuple of features
x = self.proj(x)
return x.flatten(2).transpose(1, 2), x.shape[-2:]
| x = self.backbone(x)
if isinstance(x, (list, tuple)):
x = x[-1] # last feature if backbone outputs list/tuple of features
x = self.proj(x)
return x.flatten(2).transpose(1, 2), x.shape[-2:]
| 26 | 225 | 90 | 315 | """ Hybrid Vision Transformer (ViT) in PyTorch
A PyTorch implement of the Hybrid Vision Transformers as described in:
'An Image Is Worth 16 x 16 Words: Transformers for Image Recognition at Scale'
- https://arxiv.org/abs/2010.11929
`How to train your ViT? Data, Augmentation, and Regularization in Vision Transfor... | """ Hybrid Vision Transformer (ViT) in PyTorch
A PyTorch implement of the Hybrid Vision Transformers as described in:
'An Image Is Worth 16 x 16 Words: Transformers for Image Recognition at Scale'
- https://arxiv.org/abs/2010.11929
`How to train your ViT? Data, Augmentation, and Regularization in Vision Transfor... |
17,925 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/models/vision_transformer_sam.py | 149,150 | 107,189 | forward | Attention | 150,189 | 150,189 | class Attention(nn.Module):
fused_attn: Final[bool]
def __init__(
self,
dim,
num_heads=8,
qkv_bias=True,
qk_norm=False,
attn_drop=0.,
proj_drop=0.,
norm_layer=nn.LayerNorm,
use_rel_pos: bool = False,
... | class Attention(nn.Module):
fused_attn: Final[bool]
def __init__(
self,
dim,
num_heads=8,
qkv_bias=True,
qk_norm=False,
attn_drop=0.,
proj_drop=0.,
norm_layer=nn.LayerNorm,
use_rel_pos: bool = False,
... | B, H, W, _ = x.shape
N = H * W
x = x.reshape(B, N, -1)
qkv = self.qkv(x).view(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
# qkv with shape (3, B, nHead, H * W, C)
q, k, v = qkv.reshape(3, B * self.num_heads, N, -1).unbind(0)
# q, k, v with shape (B * nHead... | B, H, W, _ = x.shape
N = H * W
x = x.reshape(B, N, -1)
qkv = self.qkv(x).view(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
# qkv with shape (3, B, nHead, H * W, C)
q, k, v = qkv.reshape(3, B * self.num_heads, N, -1).unbind(0)
# q, k, v with shape (B * nHead... | 165 | 554 | 618 | 1,172 | """ Vision Transformer (ViT) in PyTorch
A PyTorch implement of Vision Transformers as described in:
'Exploring Plain Vision Transformer Backbones for Object Detection'
- https://arxiv.org/abs/2203.16527
'Segment Anything Model (SAM)'
- https://github.com/facebookresearch/segment-anything/
"""
import logging... | """ Vision Transformer (ViT) in PyTorch
A PyTorch implement of Vision Transformers as described in:
'Exploring Plain Vision Transformer Backbones for Object Detection'
- https://arxiv.org/abs/2203.16527
'Segment Anything Model (SAM)'
- https://github.com/facebookresearch/segment-anything/
"""
import logging... |
17,926 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/models/vision_transformer_sam.py | 248,249 | 199,273 | forward | Block | 249,273 | 249,273 | class Block(nn.Module):
def __init__(
self,
dim,
num_heads,
mlp_ratio=4.,
qkv_bias=True,
qk_norm=False,
proj_drop=0.,
attn_drop=0.,
init_values=None,
drop_path=0.,
act_layer=nn.GELU,
... | class Block(nn.Module):
def __init__(
self,
dim,
num_heads,
mlp_ratio=4.,
qkv_bias=True,
qk_norm=False,
proj_drop=0.,
attn_drop=0.,
init_values=None,
drop_path=0.,
act_layer=nn.GELU,
... | B, H, W, _ = x.shape
shortcut = x
x = self.norm1(x)
# Window partition
pad_hw: Optional[Tuple[int, int]] = None
if self.window_size > 0:
x, pad_hw = window_partition(x, self.window_size)
x = self.drop_path1(self.ls1(self.attn(x)))
# Reverse ... | B, H, W, _ = x.shape
shortcut = x
x = self.norm1(x)
# Window partition
pad_hw: Optional[Tuple[int, int]] = None
if self.window_size > 0:
x, pad_hw = window_partition(x, self.window_size)
x = self.drop_path1(self.ls1(self.attn(x)))
# Reverse ... | 81 | 540 | 273 | 813 | """ Vision Transformer (ViT) in PyTorch
A PyTorch implement of Vision Transformers as described in:
'Exploring Plain Vision Transformer Backbones for Object Detection'
- https://arxiv.org/abs/2203.16527
'Segment Anything Model (SAM)'
- https://github.com/facebookresearch/segment-anything/
"""
import logging... | """ Vision Transformer (ViT) in PyTorch
A PyTorch implement of Vision Transformers as described in:
'Exploring Plain Vision Transformer Backbones for Object Detection'
- https://arxiv.org/abs/2203.16527
'Segment Anything Model (SAM)'
- https://github.com/facebookresearch/segment-anything/
"""
import logging... |
17,927 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/models/vision_transformer_sam.py | 195,196 | 189,199 | forward | LayerScale | 196,199 | 196,199 | class LayerScale(nn.Module):
def __init__(self, dim, init_values=1e-5, inplace=False):
super().__init__()
self.inplace = inplace
self.gamma = nn.Parameter(init_values * torch.ones(dim))
def forward(self, x):
FILL_FUNC_BODY | class LayerScale(nn.Module):
def __init__(self, dim, init_values=1e-5, inplace=False):
super().__init__()
self.inplace = inplace
self.gamma = nn.Parameter(init_values * torch.ones(dim))
def forward(self, x):
FILL_FUNC_BODY | return x.mul_(self.gamma) if self.inplace else x * self.gamma
| return x.mul_(self.gamma) if self.inplace else x * self.gamma
| 8 | 93 | 28 | 121 | """ Vision Transformer (ViT) in PyTorch
A PyTorch implement of Vision Transformers as described in:
'Exploring Plain Vision Transformer Backbones for Object Detection'
- https://arxiv.org/abs/2203.16527
'Segment Anything Model (SAM)'
- https://github.com/facebookresearch/segment-anything/
"""
import logging... | """ Vision Transformer (ViT) in PyTorch
A PyTorch implement of Vision Transformers as described in:
'Exploring Plain Vision Transformer Backbones for Object Detection'
- https://arxiv.org/abs/2203.16527
'Segment Anything Model (SAM)'
- https://github.com/facebookresearch/segment-anything/
"""
import logging... |
17,930 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/models/volo.py | 162,163 | 140,187 | forward | Attention | 163,187 | 163,187 | class Attention(nn.Module):
fused_attn: torch.jit.Final[bool]
def __init__(
self,
dim,
num_heads=8,
qkv_bias=False,
attn_drop=0.,
proj_drop=0.,
):
super().__init__()
self.num_heads = num_heads
head_dim = dim // ... | class Attention(nn.Module):
fused_attn: torch.jit.Final[bool]
def __init__(
self,
dim,
num_heads=8,
qkv_bias=False,
attn_drop=0.,
proj_drop=0.,
):
super().__init__()
self.num_heads = num_heads
head_dim = dim // ... | B, H, W, C = x.shape
qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
q, k, v = qkv.unbind(0)
if self.fused_attn:
x = F.scaled_dot_product_attention(
q, k, v,
dropout_p=self.attn_drop.p if sel... | B, H, W, C = x.shape
qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
q, k, v = qkv.unbind(0)
if self.fused_attn:
x = F.scaled_dot_product_attention(
q, k, v,
dropout_p=self.attn_drop.p if sel... | 78 | 252 | 300 | 552 | """ Vision OutLOoker (VOLO) implementation
Paper: `VOLO: Vision Outlooker for Visual Recognition` - https://arxiv.org/abs/2106.13112
Code adapted from official impl at https://github.com/sail-sg/volo, original copyright in comment below
Modifications and additions for timm by / Copyright 2022, Ross Wightman
"""
# Co... | """ Vision OutLOoker (VOLO) implementation
Paper: `VOLO: Vision Outlooker for Visual Recognition` - https://arxiv.org/abs/2106.13112
Code adapted from official impl at https://github.com/sail-sg/volo, original copyright in comment below
Modifications and additions for timm by / Copyright 2022, Ross Wightman
"""
# Co... |
17,931 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/models/volo.py | 241,242 | 215,258 | forward | ClassAttention | 242,258 | 242,258 | class ClassAttention(nn.Module):
def __init__(
self,
dim,
num_heads=8,
head_dim=None,
qkv_bias=False,
attn_drop=0.,
proj_drop=0.,
):
super().__init__()
self.num_heads = num_heads
if head_dim is not None:... | class ClassAttention(nn.Module):
def __init__(
self,
dim,
num_heads=8,
head_dim=None,
qkv_bias=False,
attn_drop=0.,
proj_drop=0.,
):
super().__init__()
self.num_heads = num_heads
if head_dim is not None:... | B, N, C = x.shape
kv = self.kv(x).reshape(B, N, 2, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
k, v = kv.unbind(0)
q = self.q(x[:, :1, :]).reshape(B, self.num_heads, 1, self.head_dim) * self.scale
attn = q @ k.transpose(-2, -1)
attn = attn.softmax(dim=-1)
... | B, N, C = x.shape
kv = self.kv(x).reshape(B, N, 2, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
k, v = kv.unbind(0)
q = self.q(x[:, :1, :]).reshape(B, self.num_heads, 1, self.head_dim) * self.scale
attn = q @ k.transpose(-2, -1)
attn = attn.softmax(dim=-1)
... | 60 | 317 | 270 | 587 | """ Vision OutLOoker (VOLO) implementation
Paper: `VOLO: Vision Outlooker for Visual Recognition` - https://arxiv.org/abs/2106.13112
Code adapted from official impl at https://github.com/sail-sg/volo, original copyright in comment below
Modifications and additions for timm by / Copyright 2022, Ross Wightman
"""
# Co... | """ Vision OutLOoker (VOLO) implementation
Paper: `VOLO: Vision Outlooker for Visual Recognition` - https://arxiv.org/abs/2106.13112
Code adapted from official impl at https://github.com/sail-sg/volo, original copyright in comment below
Modifications and additions for timm by / Copyright 2022, Ross Wightman
"""
# Co... |
17,932 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/models/volo.py | 294,295 | 258,301 | forward | ClassBlock | 295,301 | 295,301 | class ClassBlock(nn.Module):
def __init__(
self,
dim,
num_heads,
head_dim=None,
mlp_ratio=4.,
qkv_bias=False,
drop=0.,
attn_drop=0.,
drop_path=0.,
act_layer=nn.GELU,
norm_layer=nn.Lay... | class ClassBlock(nn.Module):
def __init__(
self,
dim,
num_heads,
head_dim=None,
mlp_ratio=4.,
qkv_bias=False,
drop=0.,
attn_drop=0.,
drop_path=0.,
act_layer=nn.GELU,
norm_layer=nn.Lay... | cls_embed = x[:, :1]
cls_embed = cls_embed + self.drop_path1(self.attn(self.norm1(x)))
cls_embed = cls_embed + self.drop_path2(self.mlp(self.norm2(cls_embed)))
return torch.cat([cls_embed, x[:, 1:]], dim=1)
| cls_embed = x[:, :1]
cls_embed = cls_embed + self.drop_path1(self.attn(self.norm1(x)))
cls_embed = cls_embed + self.drop_path2(self.mlp(self.norm2(cls_embed)))
return torch.cat([cls_embed, x[:, 1:]], dim=1)
| 19 | 351 | 108 | 459 | """ Vision OutLOoker (VOLO) implementation
Paper: `VOLO: Vision Outlooker for Visual Recognition` - https://arxiv.org/abs/2106.13112
Code adapted from official impl at https://github.com/sail-sg/volo, original copyright in comment below
Modifications and additions for timm by / Copyright 2022, Ross Wightman
"""
# Co... | """ Vision OutLOoker (VOLO) implementation
Paper: `VOLO: Vision Outlooker for Visual Recognition` - https://arxiv.org/abs/2106.13112
Code adapted from official impl at https://github.com/sail-sg/volo, original copyright in comment below
Modifications and additions for timm by / Copyright 2022, Ross Wightman
"""
# Co... |
17,933 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/models/volo.py | 380,381 | 372,387 | forward | Downsample | 381,387 | 381,387 | class Downsample(nn.Module):
""" Image to Patch Embedding, downsampling between stage1 and stage2
"""
def __init__(self, in_embed_dim, out_embed_dim, patch_size=2):
super().__init__()
self.proj = nn.Conv2d(in_embed_dim, out_embed_dim, kernel_size=patch_size, stride=patch_size)
def forw... | class Downsample(nn.Module):
""" Image to Patch Embedding, downsampling between stage1 and stage2
"""
def __init__(self, in_embed_dim, out_embed_dim, patch_size=2):
super().__init__()
self.proj = nn.Conv2d(in_embed_dim, out_embed_dim, kernel_size=patch_size, stride=patch_size)
def forw... | x = x.permute(0, 3, 1, 2)
x = self.proj(x) # B, C, H, W
x = x.permute(0, 2, 3, 1)
return x
| x = x.permute(0, 3, 1, 2)
x = self.proj(x) # B, C, H, W
x = x.permute(0, 2, 3, 1)
return x
| 22 | 127 | 73 | 200 | """ Vision OutLOoker (VOLO) implementation
Paper: `VOLO: Vision Outlooker for Visual Recognition` - https://arxiv.org/abs/2106.13112
Code adapted from official impl at https://github.com/sail-sg/volo, original copyright in comment below
Modifications and additions for timm by / Copyright 2022, Ross Wightman
"""
# Co... | """ Vision OutLOoker (VOLO) implementation
Paper: `VOLO: Vision Outlooker for Visual Recognition` - https://arxiv.org/abs/2106.13112
Code adapted from official impl at https://github.com/sail-sg/volo, original copyright in comment below
Modifications and additions for timm by / Copyright 2022, Ross Wightman
"""
# Co... |
17,934 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/models/volo.py | 71,72 | 40,98 | forward | OutlookAttention | 72,98 | 72,98 | class OutlookAttention(nn.Module):
def __init__(
self,
dim,
num_heads,
kernel_size=3,
padding=1,
stride=1,
qkv_bias=False,
attn_drop=0.,
proj_drop=0.,
):
super().__init__()
head_dim = dim... | class OutlookAttention(nn.Module):
def __init__(
self,
dim,
num_heads,
kernel_size=3,
padding=1,
stride=1,
qkv_bias=False,
attn_drop=0.,
proj_drop=0.,
):
super().__init__()
head_dim = dim... | B, H, W, C = x.shape
v = self.v(x).permute(0, 3, 1, 2) # B, C, H, W
h, w = math.ceil(H / self.stride), math.ceil(W / self.stride)
v = self.unfold(v).reshape(
B, self.num_heads, C // self.num_heads,
self.kernel_size * self.kernel_size, h * w).permute(0, 1, 4, 3,... | B, H, W, C = x.shape
v = self.v(x).permute(0, 3, 1, 2) # B, C, H, W
h, w = math.ceil(H / self.stride), math.ceil(W / self.stride)
v = self.unfold(v).reshape(
B, self.num_heads, C // self.num_heads,
self.kernel_size * self.kernel_size, h * w).permute(0, 1, 4, 3,... | 122 | 353 | 502 | 855 | """ Vision OutLOoker (VOLO) implementation
Paper: `VOLO: Vision Outlooker for Visual Recognition` - https://arxiv.org/abs/2106.13112
Code adapted from official impl at https://github.com/sail-sg/volo, original copyright in comment below
Modifications and additions for timm by / Copyright 2022, Ross Wightman
"""
# Co... | """ Vision OutLOoker (VOLO) implementation
Paper: `VOLO: Vision Outlooker for Visual Recognition` - https://arxiv.org/abs/2106.13112
Code adapted from official impl at https://github.com/sail-sg/volo, original copyright in comment below
Modifications and additions for timm by / Copyright 2022, Ross Wightman
"""
# Co... |
17,935 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/models/volo.py | 134,135 | 98,140 | forward | Outlooker | 135,140 | 135,140 | class Outlooker(nn.Module):
def __init__(
self,
dim,
kernel_size,
padding,
stride=1,
num_heads=1,
mlp_ratio=3.,
attn_drop=0.,
drop_path=0.,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm,
... | class Outlooker(nn.Module):
def __init__(
self,
dim,
kernel_size,
padding,
stride=1,
num_heads=1,
mlp_ratio=3.,
attn_drop=0.,
drop_path=0.,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm,
... | x = x + self.drop_path1(self.attn(self.norm1(x)))
x = x + self.drop_path2(self.mlp(self.norm2(x)))
return x
| x = x + self.drop_path1(self.attn(self.norm1(x)))
x = x + self.drop_path2(self.mlp(self.norm2(x)))
return x
| 12 | 351 | 61 | 412 | """ Vision OutLOoker (VOLO) implementation
Paper: `VOLO: Vision Outlooker for Visual Recognition` - https://arxiv.org/abs/2106.13112
Code adapted from official impl at https://github.com/sail-sg/volo, original copyright in comment below
Modifications and additions for timm by / Copyright 2022, Ross Wightman
"""
# Co... | """ Vision OutLOoker (VOLO) implementation
Paper: `VOLO: Vision Outlooker for Visual Recognition` - https://arxiv.org/abs/2106.13112
Code adapted from official impl at https://github.com/sail-sg/volo, original copyright in comment below
Modifications and additions for timm by / Copyright 2022, Ross Wightman
"""
# Co... |
17,936 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/models/volo.py | 365,366 | 329,372 | forward | PatchEmbed | 366,372 | 366,372 | class PatchEmbed(nn.Module):
""" Image to Patch Embedding.
Different with ViT use 1 conv layer, we use 4 conv layers to do patch embedding
"""
def __init__(
self,
img_size=224,
stem_conv=False,
stem_stride=1,
patch_size=8,
in_chans... | class PatchEmbed(nn.Module):
""" Image to Patch Embedding.
Different with ViT use 1 conv layer, we use 4 conv layers to do patch embedding
"""
def __init__(
self,
img_size=224,
stem_conv=False,
stem_stride=1,
patch_size=8,
in_chans... | if self.conv is not None:
x = self.conv(x)
x = self.proj(x) # B, C, H, W
return x
| if self.conv is not None:
x = self.conv(x)
x = self.proj(x) # B, C, H, W
return x
| 18 | 512 | 51 | 563 | """ Vision OutLOoker (VOLO) implementation
Paper: `VOLO: Vision Outlooker for Visual Recognition` - https://arxiv.org/abs/2106.13112
Code adapted from official impl at https://github.com/sail-sg/volo, original copyright in comment below
Modifications and additions for timm by / Copyright 2022, Ross Wightman
"""
# Co... | """ Vision OutLOoker (VOLO) implementation
Paper: `VOLO: Vision Outlooker for Visual Recognition` - https://arxiv.org/abs/2106.13112
Code adapted from official impl at https://github.com/sail-sg/volo, original copyright in comment below
Modifications and additions for timm by / Copyright 2022, Ross Wightman
"""
# Co... |
17,937 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/models/volo.py | 209,210 | 187,215 | forward | Transformer | 210,215 | 210,215 | class Transformer(nn.Module):
def __init__(
self,
dim,
num_heads,
mlp_ratio=4.,
qkv_bias=False,
attn_drop=0.,
drop_path=0.,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm,
):
super().__init__()
... | class Transformer(nn.Module):
def __init__(
self,
dim,
num_heads,
mlp_ratio=4.,
qkv_bias=False,
attn_drop=0.,
drop_path=0.,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm,
):
super().__init__()
... | x = x + self.drop_path1(self.attn(self.norm1(x)))
x = x + self.drop_path2(self.mlp(self.norm2(x)))
return x
| x = x + self.drop_path1(self.attn(self.norm1(x)))
x = x + self.drop_path2(self.mlp(self.norm2(x)))
return x
| 12 | 291 | 61 | 352 | """ Vision OutLOoker (VOLO) implementation
Paper: `VOLO: Vision Outlooker for Visual Recognition` - https://arxiv.org/abs/2106.13112
Code adapted from official impl at https://github.com/sail-sg/volo, original copyright in comment below
Modifications and additions for timm by / Copyright 2022, Ross Wightman
"""
# Co... | """ Vision OutLOoker (VOLO) implementation
Paper: `VOLO: Vision Outlooker for Visual Recognition` - https://arxiv.org/abs/2106.13112
Code adapted from official impl at https://github.com/sail-sg/volo, original copyright in comment below
Modifications and additions for timm by / Copyright 2022, Ross Wightman
"""
# Co... |
17,940 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/models/vovnet.py | 89,90 | 43,104 | forward | OsaBlock | 90,104 | 90,104 | class OsaBlock(nn.Module):
def __init__(
self,
in_chs,
mid_chs,
out_chs,
layer_per_block,
residual=False,
depthwise=False,
attn='',
norm_layer=BatchNormAct2d,
act_layer=nn.ReLU,
drop_... | class OsaBlock(nn.Module):
def __init__(
self,
in_chs,
mid_chs,
out_chs,
layer_per_block,
residual=False,
depthwise=False,
attn='',
norm_layer=BatchNormAct2d,
act_layer=nn.ReLU,
drop_... | output = [x]
if self.conv_reduction is not None:
x = self.conv_reduction(x)
x = self.conv_mid(x, output)
x = self.conv_concat(x)
if self.attn is not None:
x = self.attn(x)
if self.drop_path is not None:
x = self.drop_path(x)
if ... | output = [x]
if self.conv_reduction is not None:
x = self.conv_reduction(x)
x = self.conv_mid(x, output)
x = self.conv_concat(x)
if self.attn is not None:
x = self.attn(x)
if self.drop_path is not None:
x = self.drop_path(x)
if ... | 43 | 501 | 143 | 644 | """ VoVNet (V1 & V2)
Papers:
* `An Energy and GPU-Computation Efficient Backbone Network` - https://arxiv.org/abs/1904.09730
* `CenterMask : Real-Time Anchor-Free Instance Segmentation` - https://arxiv.org/abs/1911.06667
Looked at https://github.com/youngwanLEE/vovnet-detectron2 &
https://github.com/stigma0617/VoVNe... | """ VoVNet (V1 & V2)
Papers:
* `An Energy and GPU-Computation Efficient Backbone Network` - https://arxiv.org/abs/1904.09730
* `CenterMask : Real-Time Anchor-Free Instance Segmentation` - https://arxiv.org/abs/1911.06667
Looked at https://github.com/youngwanLEE/vovnet-detectron2 &
https://github.com/stigma0617/VoVNe... |
17,941 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/models/vovnet.py | 143,144 | 104,153 | forward | OsaStage | 144,153 | 144,153 | class OsaStage(nn.Module):
def __init__(
self,
in_chs,
mid_chs,
out_chs,
block_per_stage,
layer_per_block,
downsample=True,
residual=True,
depthwise=False,
attn='ese',
norm_layer=Batc... | class OsaStage(nn.Module):
def __init__(
self,
in_chs,
mid_chs,
out_chs,
block_per_stage,
layer_per_block,
downsample=True,
residual=True,
depthwise=False,
attn='ese',
norm_layer=Batc... | if self.pool is not None:
x = self.pool(x)
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint_seq(self.blocks, x)
else:
x = self.blocks(x)
return x
| if self.pool is not None:
x = self.pool(x)
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint_seq(self.blocks, x)
else:
x = self.blocks(x)
return x
| 23 | 420 | 87 | 507 | """ VoVNet (V1 & V2)
Papers:
* `An Energy and GPU-Computation Efficient Backbone Network` - https://arxiv.org/abs/1904.09730
* `CenterMask : Real-Time Anchor-Free Instance Segmentation` - https://arxiv.org/abs/1911.06667
Looked at https://github.com/youngwanLEE/vovnet-detectron2 &
https://github.com/stigma0617/VoVNe... | """ VoVNet (V1 & V2)
Papers:
* `An Energy and GPU-Computation Efficient Backbone Network` - https://arxiv.org/abs/1904.09730
* `CenterMask : Real-Time Anchor-Free Instance Segmentation` - https://arxiv.org/abs/1911.06667
Looked at https://github.com/youngwanLEE/vovnet-detectron2 &
https://github.com/stigma0617/VoVNe... |
17,942 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/models/vovnet.py | 33,34 | 29,43 | forward | SequentialAppendList | 34,43 | 34,43 | class SequentialAppendList(nn.Sequential):
def __init__(self, *args):
super(SequentialAppendList, self).__init__(*args)
def forward(self, x: torch.Tensor, concat_list: List[torch.Tensor]) -> torch.Tensor:
FILL_FUNC_BODY | class SequentialAppendList(nn.Sequential):
def __init__(self, *args):
super(SequentialAppendList, self).__init__(*args)
def forward(self, x: torch.Tensor, concat_list: List[torch.Tensor]) -> torch.Tensor:
FILL_FUNC_BODY | for i, module in enumerate(self):
if i == 0:
concat_list.append(module(x))
else:
concat_list.append(module(concat_list[-1]))
x = torch.cat(concat_list, dim=1)
return x
| for i, module in enumerate(self):
if i == 0:
concat_list.append(module(x))
else:
concat_list.append(module(concat_list[-1]))
x = torch.cat(concat_list, dim=1)
return x
| 18 | 90 | 85 | 175 | """ VoVNet (V1 & V2)
Papers:
* `An Energy and GPU-Computation Efficient Backbone Network` - https://arxiv.org/abs/1904.09730
* `CenterMask : Real-Time Anchor-Free Instance Segmentation` - https://arxiv.org/abs/1911.06667
Looked at https://github.com/youngwanLEE/vovnet-detectron2 &
https://github.com/stigma0617/VoVNe... | """ VoVNet (V1 & V2)
Papers:
* `An Energy and GPU-Computation Efficient Backbone Network` - https://arxiv.org/abs/1904.09730
* `CenterMask : Real-Time Anchor-Free Instance Segmentation` - https://arxiv.org/abs/1911.06667
Looked at https://github.com/youngwanLEE/vovnet-detectron2 &
https://github.com/stigma0617/VoVNe... |
17,943 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/models/vovnet.py | 261,262 | 153,272 | forward_head | VovNet | 262,264 | 262,264 | class VovNet(nn.Module):
def __init__(
self,
cfg,
in_chans=3,
num_classes=1000,
global_pool='avg',
output_stride=32,
norm_layer=BatchNormAct2d,
act_layer=nn.ReLU,
drop_rate=0.,
drop_path_rate=0.,... | class VovNet(nn.Module):
def __init__(
self,
cfg,
in_chans=3,
num_classes=1000,
global_pool='avg',
output_stride=32,
norm_layer=BatchNormAct2d,
act_layer=nn.ReLU,
drop_rate=0.,
drop_path_rate=0.,... | return self.head(x, pre_logits=pre_logits) if pre_logits else self.head(x)
| return self.head(x, pre_logits=pre_logits) if pre_logits else self.head(x)
| 7 | 1,631 | 34 | 1,665 | """ VoVNet (V1 & V2)
Papers:
* `An Energy and GPU-Computation Efficient Backbone Network` - https://arxiv.org/abs/1904.09730
* `CenterMask : Real-Time Anchor-Free Instance Segmentation` - https://arxiv.org/abs/1911.06667
Looked at https://github.com/youngwanLEE/vovnet-detectron2 &
https://github.com/stigma0617/VoVNe... | """ VoVNet (V1 & V2)
Papers:
* `An Energy and GPU-Computation Efficient Backbone Network` - https://arxiv.org/abs/1904.09730
* `CenterMask : Real-Time Anchor-Free Instance Segmentation` - https://arxiv.org/abs/1911.06667
Looked at https://github.com/youngwanLEE/vovnet-detectron2 &
https://github.com/stigma0617/VoVNe... |
17,945 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/models/xception.py | 80,81 | 49,93 | forward | Block | 81,93 | 81,93 | class Block(nn.Module):
def __init__(self, in_channels, out_channels, reps, strides=1, start_with_relu=True, grow_first=True):
super(Block, self).__init__()
if out_channels != in_channels or strides != 1:
self.skip = nn.Conv2d(in_channels, out_channels, 1, stride=strides, bias=False)
... | class Block(nn.Module):
def __init__(self, in_channels, out_channels, reps, strides=1, start_with_relu=True, grow_first=True):
super(Block, self).__init__()
if out_channels != in_channels or strides != 1:
self.skip = nn.Conv2d(in_channels, out_channels, 1, stride=strides, bias=False)
... | x = self.rep(inp)
if self.skip is not None:
skip = self.skip(inp)
skip = self.skipbn(skip)
else:
skip = inp
x += skip
return x
| x = self.rep(inp)
if self.skip is not None:
skip = self.skip(inp)
skip = self.skipbn(skip)
else:
skip = inp
x += skip
return x
| 23 | 408 | 78 | 486 | """
Ported to pytorch thanks to [tstandley](https://github.com/tstandley/Xception-PyTorch)
@author: tstandley
Adapted by cadene
Creates an Xception Model as defined in:
Francois Chollet
Xception: Deep Learning with Depthwise Separable Convolutions
https://arxiv.org/pdf/1610.02357.pdf
This weights ported from the Ke... | """
Ported to pytorch thanks to [tstandley](https://github.com/tstandley/Xception-PyTorch)
@author: tstandley
Adapted by cadene
Creates an Xception Model as defined in:
Francois Chollet
Xception: Deep Learning with Depthwise Separable Convolutions
https://arxiv.org/pdf/1610.02357.pdf
This weights ported from the Ke... |
17,946 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/models/xception.py | 43,44 | 35,49 | forward | SeparableConv2d | 44,49 | 44,49 | class SeparableConv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, padding=0, dilation=1):
super(SeparableConv2d, self).__init__()
self.conv1 = nn.Conv2d(
in_channels, in_channels, kernel_size, stride, padding, dilation, groups=in_channels, bias=Fal... | class SeparableConv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, padding=0, dilation=1):
super(SeparableConv2d, self).__init__()
self.conv1 = nn.Conv2d(
in_channels, in_channels, kernel_size, stride, padding, dilation, groups=in_channels, bias=Fal... | x = self.conv1(x)
x = self.pointwise(x)
return x
| x = self.conv1(x)
x = self.pointwise(x)
return x
| 8 | 184 | 33 | 217 | """
Ported to pytorch thanks to [tstandley](https://github.com/tstandley/Xception-PyTorch)
@author: tstandley
Adapted by cadene
Creates an Xception Model as defined in:
Francois Chollet
Xception: Deep Learning with Depthwise Separable Convolutions
https://arxiv.org/pdf/1610.02357.pdf
This weights ported from the Ke... | """
Ported to pytorch thanks to [tstandley](https://github.com/tstandley/Xception-PyTorch)
@author: tstandley
Adapted by cadene
Creates an Xception Model as defined in:
Francois Chollet
Xception: Deep Learning with Depthwise Separable Convolutions
https://arxiv.org/pdf/1610.02357.pdf
This weights ported from the Ke... |
17,947 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/models/xception.py | 181,182 | 93,224 | forward_features | Xception | 182,212 | 182,212 | class Xception(nn.Module):
"""
Xception optimized for the ImageNet dataset, as specified in
https://arxiv.org/pdf/1610.02357.pdf
"""
def __init__(self, num_classes=1000, in_chans=3, drop_rate=0., global_pool='avg'):
""" Constructor
Args:
num_classes: number of classes
... | class Xception(nn.Module):
"""
Xception optimized for the ImageNet dataset, as specified in
https://arxiv.org/pdf/1610.02357.pdf
"""
def __init__(self, num_classes=1000, in_chans=3, drop_rate=0., global_pool='avg'):
""" Constructor
Args:
num_classes: number of classes
... | x = self.conv1(x)
x = self.bn1(x)
x = self.act1(x)
x = self.conv2(x)
x = self.bn2(x)
x = self.act2(x)
x = self.block1(x)
x = self.block2(x)
x = self.block3(x)
x = self.block4(x)
x = self.block5(x)
x = self.block6(x)
... | x = self.conv1(x)
x = self.bn1(x)
x = self.act1(x)
x = self.conv2(x)
x = self.bn2(x)
x = self.act2(x)
x = self.block1(x)
x = self.block2(x)
x = self.block3(x)
x = self.block4(x)
x = self.block5(x)
x = self.block6(x)
... | 74 | 1,440 | 306 | 1,746 | """
Ported to pytorch thanks to [tstandley](https://github.com/tstandley/Xception-PyTorch)
@author: tstandley
Adapted by cadene
Creates an Xception Model as defined in:
Francois Chollet
Xception: Deep Learning with Depthwise Separable Convolutions
https://arxiv.org/pdf/1610.02357.pdf
This weights ported from the Ke... | """
Ported to pytorch thanks to [tstandley](https://github.com/tstandley/Xception-PyTorch)
@author: tstandley
Adapted by cadene
Creates an Xception Model as defined in:
Francois Chollet
Xception: Deep Learning with Depthwise Separable Convolutions
https://arxiv.org/pdf/1610.02357.pdf
This weights ported from the Ke... |
17,951 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/optim/adamp.py | 25,26 | 1,106 | projection | 26,43 | 26,43 | """
AdamP Optimizer Implementation copied from https://github.com/clovaai/AdamP/blob/master/adamp/adamp.py
Paper: `Slowing Down the Weight Norm Increase in Momentum-based Optimizers` - https://arxiv.org/abs/2006.08217
Code: https://github.com/clovaai/AdamP
Copyright (c) 2020-present NAVER Corp.
MIT license
"""
impor... | """
AdamP Optimizer Implementation copied from https://github.com/clovaai/AdamP/blob/master/adamp/adamp.py
Paper: `Slowing Down the Weight Norm Increase in Momentum-based Optimizers` - https://arxiv.org/abs/2006.08217
Code: https://github.com/clovaai/AdamP
Copyright (c) 2020-present NAVER Corp.
MIT license
"""
impor... | wd = 1.
expand_size = (-1,) + (1,) * (len(p.shape) - 1)
for view_func in [_channel_view, _layer_view]:
param_view = view_func(p)
grad_view = view_func(grad)
cosine_sim = F.cosine_similarity(grad_view, param_view, dim=1, eps=eps).abs_()
# FIXME this is a problem for PyTorch X... | wd = 1.
expand_size = (-1,) + (1,) * (len(p.shape) - 1)
for view_func in [_channel_view, _layer_view]:
param_view = view_func(p)
grad_view = view_func(grad)
cosine_sim = F.cosine_similarity(grad_view, param_view, dim=1, eps=eps).abs_()
# FIXME this is a problem for PyTorch X... | 66 | 971 | 259 | 1,230 | """
AdamP Optimizer Implementation copied from https://github.com/clovaai/AdamP/blob/master/adamp/adamp.py
Paper: `Slowing Down the Weight Norm Increase in Momentum-based Optimizers` - https://arxiv.org/abs/2006.08217
Code: https://github.com/clovaai/AdamP
Copyright (c) 2020-present NAVER Corp.
MIT license
"""
impor... | """
AdamP Optimizer Implementation copied from https://github.com/clovaai/AdamP/blob/master/adamp/adamp.py
Paper: `Slowing Down the Weight Norm Increase in Momentum-based Optimizers` - https://arxiv.org/abs/2006.08217
Code: https://github.com/clovaai/AdamP
Copyright (c) 2020-present NAVER Corp.
MIT license
"""
impor... | |
17,952 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/optim/adamp.py | 51,53 | 43,106 | step | AdamP | 53,106 | 53,106 | class AdamP(Optimizer):
def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,
weight_decay=0, delta=0.1, wd_ratio=0.1, nesterov=False):
defaults = dict(
lr=lr, betas=betas, eps=eps, weight_decay=weight_decay,
delta=delta, wd_ratio=wd_ratio, nesterov=neste... | class AdamP(Optimizer):
def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,
weight_decay=0, delta=0.1, wd_ratio=0.1, nesterov=False):
defaults = dict(
lr=lr, betas=betas, eps=eps, weight_decay=weight_decay,
delta=delta, wd_ratio=wd_ratio, nesterov=neste... | loss = None
if closure is not None:
with torch.enable_grad():
loss = closure()
for group in self.param_groups:
for p in group['params']:
if p.grad is None:
continue
grad = p.grad
beta1, ... | loss = None
if closure is not None:
with torch.enable_grad():
loss = closure()
for group in self.param_groups:
for p in group['params']:
if p.grad is None:
continue
grad = p.grad
beta1, ... | 157 | 187 | 590 | 777 | """
AdamP Optimizer Implementation copied from https://github.com/clovaai/AdamP/blob/master/adamp/adamp.py
Paper: `Slowing Down the Weight Norm Increase in Momentum-based Optimizers` - https://arxiv.org/abs/2006.08217
Code: https://github.com/clovaai/AdamP
Copyright (c) 2020-present NAVER Corp.
MIT license
"""
impor... | """
AdamP Optimizer Implementation copied from https://github.com/clovaai/AdamP/blob/master/adamp/adamp.py
Paper: `Slowing Down the Weight Norm Increase in Momentum-based Optimizers` - https://arxiv.org/abs/2006.08217
Code: https://github.com/clovaai/AdamP
Copyright (c) 2020-present NAVER Corp.
MIT license
"""
impor... |
17,953 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/optim/adamw.py | 58,60 | 12,123 | step | AdamW | 66,123 | 60,123 | class AdamW(Optimizer):
r"""Implements AdamW algorithm.
The original Adam algorithm was proposed in `Adam: A Method for Stochastic Optimization`_.
The AdamW variant was proposed in `Decoupled Weight Decay Regularization`_.
Arguments:
params (iterable): iterable of parameters to optimize or dic... | class AdamW(Optimizer):
r"""Implements AdamW algorithm.
The original Adam algorithm was proposed in `Adam: A Method for Stochastic Optimization`_.
The AdamW variant was proposed in `Decoupled Weight Decay Regularization`_.
Arguments:
params (iterable): iterable of parameters to optimize or dic... | loss = None
if closure is not None:
with torch.enable_grad():
loss = closure()
for group in self.param_groups:
for p in group['params']:
if p.grad is None:
continue
# Perform stepweight decay
... | """Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
loss = None
if closure is not None:
with torch.enable_grad():
loss = closure()
... | 207 | 752 | 743 | 1,495 | """ AdamW Optimizer
Impl copied from PyTorch master
NOTE: Builtin optim.AdamW is used by the factory, this impl only serves as a Python based reference, will be removed
someday
"""
import math
import torch
from torch.optim.optimizer import Optimizer
class AdamW(Optimizer):
r"""Implements AdamW algorithm.
Th... | """ AdamW Optimizer
Impl copied from PyTorch master
NOTE: Builtin optim.AdamW is used by the factory, this impl only serves as a Python based reference, will be removed
someday
"""
import math
import torch
from torch.optim.optimizer import Optimizer
class AdamW(Optimizer):
r"""Implements AdamW algorithm.
Th... |
17,954 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/optim/adan.py | 71,73 | 16,125 | step | Adan | 75,125 | 73,125 | class Adan(Optimizer):
"""
Implements a pytorch variant of Adan
Adan was proposed in
Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models[J]. arXiv preprint arXiv:2208.06677, 2022.
https://arxiv.org/abs/2208.06677
Arguments:
params (iterable): iterable of paramete... | class Adan(Optimizer):
"""
Implements a pytorch variant of Adan
Adan was proposed in
Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models[J]. arXiv preprint arXiv:2208.06677, 2022.
https://arxiv.org/abs/2208.06677
Arguments:
params (iterable): iterable of paramete... | loss = None
if closure is not None:
with torch.enable_grad():
loss = closure()
for group in self.param_groups:
beta1, beta2, beta3 = group['betas']
# assume same step across group now to simplify things
# per parameter step can be ... | """ Performs a single optimization step.
"""
loss = None
if closure is not None:
with torch.enable_grad():
loss = closure()
for group in self.param_groups:
beta1, beta2, beta3 = group['betas']
# assume same step across group no... | 187 | 829 | 702 | 1,531 | """ Adan Optimizer
Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models[J]. arXiv preprint arXiv:2208.06677, 2022.
https://arxiv.org/abs/2208.06677
Implementation adapted from https://github.com/sail-sg/Adan
"""
import math
import torch
from torch.optim import Optimizer
class Adan(Opt... | """ Adan Optimizer
Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models[J]. arXiv preprint arXiv:2208.06677, 2022.
https://arxiv.org/abs/2208.06677
Implementation adapted from https://github.com/sail-sg/Adan
"""
import math
import torch
from torch.optim import Optimizer
class Adan(Opt... |
17,955 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/optim/lamb.py | 96,98 | 60,193 | step | Lamb | 103,193 | 98,193 | class Lamb(Optimizer):
"""Implements a pure pytorch variant of FuseLAMB (NvLamb variant) optimizer from apex.optimizers.FusedLAMB
reference: https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/LanguageModeling/Transformer-XL/pytorch/lamb.py
LAMB was proposed in `Large Batch Optimization for ... | class Lamb(Optimizer):
"""Implements a pure pytorch variant of FuseLAMB (NvLamb variant) optimizer from apex.optimizers.FusedLAMB
reference: https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/LanguageModeling/Transformer-XL/pytorch/lamb.py
LAMB was proposed in `Large Batch Optimization for ... | loss = None
if closure is not None:
with torch.enable_grad():
loss = closure()
device = self.param_groups[0]['params'][0].device
one_tensor = torch.tensor(1.0, device=device) # because torch.where doesn't handle scalars correctly
global_grad_norm = t... | """Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
loss = None
if closure is not None:
with torch.enable_grad():
loss = closure()
... | 360 | 730 | 1,218 | 1,948 | """ PyTorch Lamb optimizer w/ behaviour similar to NVIDIA FusedLamb
This optimizer code was adapted from the following (starting with latest)
* https://github.com/HabanaAI/Model-References/blob/2b435114fe8e31f159b1d3063b8280ae37af7423/PyTorch/nlp/bert/pretraining/lamb.py
* https://github.com/NVIDIA/DeepLearningExample... | """ PyTorch Lamb optimizer w/ behaviour similar to NVIDIA FusedLamb
This optimizer code was adapted from the following (starting with latest)
* https://github.com/HabanaAI/Model-References/blob/2b435114fe8e31f159b1d3063b8280ae37af7423/PyTorch/nlp/bert/pretraining/lamb.py
* https://github.com/NVIDIA/DeepLearningExample... |
17,956 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/optim/lars.py | 75,77 | 17,136 | step | Lars | 82,136 | 77,136 | class Lars(Optimizer):
""" LARS for PyTorch
Paper: `Large batch training of Convolutional Networks` - https://arxiv.org/pdf/1708.03888.pdf
Args:
params (iterable): iterable of parameters to optimize or dicts defining parameter groups.
lr (float, optional): learning rate (default: 1.0).... | class Lars(Optimizer):
""" LARS for PyTorch
Paper: `Large batch training of Convolutional Networks` - https://arxiv.org/pdf/1708.03888.pdf
Args:
params (iterable): iterable of parameters to optimize or dicts defining parameter groups.
lr (float, optional): learning rate (default: 1.0).... | loss = None
if closure is not None:
with torch.enable_grad():
loss = closure()
device = self.param_groups[0]['params'][0].device
one_tensor = torch.tensor(1.0, device=device) # because torch.where doesn't handle scalars correctly
for group in self.p... | """Performs a single optimization step.
Args:
closure (callable, optional): A closure that reevaluates the model and returns the loss.
"""
loss = None
if closure is not None:
with torch.enable_grad():
loss = closure()
device = sel... | 179 | 739 | 698 | 1,437 | """ PyTorch LARS / LARC Optimizer
An implementation of LARS (SGD) + LARC in PyTorch
Based on:
* PyTorch SGD: https://github.com/pytorch/pytorch/blob/1.7/torch/optim/sgd.py#L100
* NVIDIA APEX LARC: https://github.com/NVIDIA/apex/blob/master/apex/parallel/LARC.py
Additional cleanup and modifications to properly su... | """ PyTorch LARS / LARC Optimizer
An implementation of LARS (SGD) + LARC in PyTorch
Based on:
* PyTorch SGD: https://github.com/pytorch/pytorch/blob/1.7/torch/optim/sgd.py#L100
* NVIDIA APEX LARC: https://github.com/NVIDIA/apex/blob/master/apex/parallel/LARC.py
Additional cleanup and modifications to properly su... |
17,958 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/optim/lion.py | 69,71 | 25,122 | step | Lion | 80,122 | 71,122 | class Lion(Optimizer):
r"""Implements Lion algorithm."""
def __init__(
self,
params,
lr=1e-4,
betas=(0.9, 0.99),
weight_decay=0.0,
maximize=False,
foreach=None,
):
"""Initialize the hyperparameters.
Args:
... | class Lion(Optimizer):
r"""Implements Lion algorithm."""
def __init__(
self,
params,
lr=1e-4,
betas=(0.9, 0.99),
weight_decay=0.0,
maximize=False,
foreach=None,
):
"""Initialize the hyperparameters.
Args:
... | loss = None
if closure is not None:
with torch.enable_grad():
loss = closure()
for group in self.param_groups:
params_with_grad = []
grads = []
exp_avgs = []
beta1, beta2 = group['betas']
for p in group['pa... | """Performs a single optimization step.
Args:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
Returns:
the loss.
"""
loss = None
if closure is not None:
with torch.enable_grad():
... | 78 | 524 | 361 | 885 | """ Lion Optimizer
Paper: `Symbolic Discovery of Optimization Algorithms` - https://arxiv.org/abs/2302.06675
Original Impl: https://github.com/google/automl/tree/master/lion
"""
# Copyright 2023 Google Research. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use t... | """ Lion Optimizer
Paper: `Symbolic Discovery of Optimization Algorithms` - https://arxiv.org/abs/2302.06675
Original Impl: https://github.com/google/automl/tree/master/lion
"""
# Copyright 2023 Google Research. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use t... |
17,959 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/optim/lookahead.py | 35,37 | 15,67 | update_slow | Lookahead | 37,48 | 37,48 | class Lookahead(Optimizer):
def __init__(self, base_optimizer, alpha=0.5, k=6):
# NOTE super().__init__() not called on purpose
self._optimizer_step_pre_hooks: Dict[int, Callable] = OrderedDict()
self._optimizer_step_post_hooks: Dict[int, Callable] = OrderedDict()
if not 0.0 <= alpha... | class Lookahead(Optimizer):
def __init__(self, base_optimizer, alpha=0.5, k=6):
# NOTE super().__init__() not called on purpose
self._optimizer_step_pre_hooks: Dict[int, Callable] = OrderedDict()
self._optimizer_step_post_hooks: Dict[int, Callable] = OrderedDict()
if not 0.0 <= alpha... | for fast_p in group["params"]:
if fast_p.grad is None:
continue
param_state = self._base_optimizer.state[fast_p]
if 'lookahead_slow_buff' not in param_state:
param_state['lookahead_slow_buff'] = torch.empty_like(fast_p)
param_st... | for fast_p in group["params"]:
if fast_p.grad is None:
continue
param_state = self._base_optimizer.state[fast_p]
if 'lookahead_slow_buff' not in param_state:
param_state['lookahead_slow_buff'] = torch.empty_like(fast_p)
param_st... | 29 | 552 | 169 | 721 | """ Lookahead Optimizer Wrapper.
Implementation modified from: https://github.com/alphadl/lookahead.pytorch
Paper: `Lookahead Optimizer: k steps forward, 1 step back` - https://arxiv.org/abs/1907.08610
Hacked together by / Copyright 2020 Ross Wightman
"""
from collections import OrderedDict
from typing import Callable... | """ Lookahead Optimizer Wrapper.
Implementation modified from: https://github.com/alphadl/lookahead.pytorch
Paper: `Lookahead Optimizer: k steps forward, 1 step back` - https://arxiv.org/abs/1907.08610
Hacked together by / Copyright 2020 Ross Wightman
"""
from collections import OrderedDict
from typing import Callable... |
17,960 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/optim/madgrad.py | 85,87 | 24,185 | step | MADGRAD | 92,185 | 87,185 | class MADGRAD(torch.optim.Optimizer):
"""
MADGRAD_: A Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic
Optimization.
.. _MADGRAD: https://arxiv.org/abs/2101.11075
MADGRAD is a general purpose optimizer that can be used in place of SGD or
Adam may converge faster and general... | class MADGRAD(torch.optim.Optimizer):
"""
MADGRAD_: A Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic
Optimization.
.. _MADGRAD: https://arxiv.org/abs/2101.11075
MADGRAD is a general purpose optimizer that can be used in place of SGD or
Adam may converge faster and general... | loss = None
if closure is not None:
with torch.enable_grad():
loss = closure()
for group in self.param_groups:
eps = group['eps']
lr = group['lr'] + eps
weight_decay = group['weight_decay']
momentum = group['momentum']
... | """Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model and returns the loss.
"""
loss = None
if closure is not None:
with torch.enable_grad():
loss = closure()
for gro... | 286 | 775 | 1,153 | 1,928 | """ PyTorch MADGRAD optimizer
MADGRAD: https://arxiv.org/abs/2101.11075
Code from: https://github.com/facebookresearch/madgrad
"""
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import ma... | """ PyTorch MADGRAD optimizer
MADGRAD: https://arxiv.org/abs/2101.11075
Code from: https://github.com/facebookresearch/madgrad
"""
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import ma... |
17,961 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/optim/nadam.py | 43,45 | 7,98 | step | Nadam | 51,98 | 45,98 | class Nadam(Optimizer):
"""Implements Nadam algorithm (a variant of Adam based on Nesterov momentum).
It has been proposed in `Incorporating Nesterov Momentum into Adam`__.
Arguments:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
lr (f... | class Nadam(Optimizer):
"""Implements Nadam algorithm (a variant of Adam based on Nesterov momentum).
It has been proposed in `Incorporating Nesterov Momentum into Adam`__.
Arguments:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
lr (f... | loss = None
if closure is not None:
with torch.enable_grad():
loss = closure()
for group in self.param_groups:
for p in group['params']:
if p.grad is None:
continue
grad = p.grad
state = ... | """Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
loss = None
if closure is not None:
with torch.enable_grad():
loss = closure()
... | 183 | 548 | 689 | 1,237 | import math
import torch
from torch.optim.optimizer import Optimizer
class Nadam(Optimizer):
"""Implements Nadam algorithm (a variant of Adam based on Nesterov momentum).
It has been proposed in `Incorporating Nesterov Momentum into Adam`__.
Arguments:
params (iterable): iterable of parameters ... | import math
import torch
from torch.optim.optimizer import Optimizer
class Nadam(Optimizer):
"""Implements Nadam algorithm (a variant of Adam based on Nesterov momentum).
It has been proposed in `Incorporating Nesterov Momentum into Adam`__.
Arguments:
params (iterable): iterable of parameters ... |
17,963 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/optim/nadamw.py | 80,82 | 15,143 | step | NAdamW | 88,143 | 82,143 | class NAdamW(torch.optim.Optimizer):
r"""Implements NAdamW algorithm.
See Table 1 in https://arxiv.org/abs/1910.05446 for the implementation of
the NAdam algorithm (there is also a comment in the code which highlights
the only difference of NAdamW and AdamW).
For further details regarding t... | class NAdamW(torch.optim.Optimizer):
r"""Implements NAdamW algorithm.
See Table 1 in https://arxiv.org/abs/1910.05446 for the implementation of
the NAdam algorithm (there is also a comment in the code which highlights
the only difference of NAdamW and AdamW).
For further details regarding t... | self._cuda_graph_capture_health_check()
loss = None
if closure is not None:
with torch.enable_grad():
loss = closure()
for group in self.param_groups:
params_with_grad = []
grads = []
exp_avgs = []
exp_avg_sqs ... | """Performs a single optimization step.
Args:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
self._cuda_graph_capture_health_check()
loss = None
if closure is not None:
with to... | 112 | 872 | 544 | 1,416 | """ NAdamW Optimizer
Based on simplified algorithm in https://github.com/mlcommons/algorithmic-efficiency/tree/main/baselines/nadamw
Added multi-tensor (foreach) path.
"""
import math
from typing import List, Optional
import torch
from torch import Tensor
# Modified from github.com/pytorch/pytorch/blob/v1.12.1/tor... | """ NAdamW Optimizer
Based on simplified algorithm in https://github.com/mlcommons/algorithmic-efficiency/tree/main/baselines/nadamw
Added multi-tensor (foreach) path.
"""
import math
from typing import List, Optional
import torch
from torch import Tensor
# Modified from github.com/pytorch/pytorch/blob/v1.12.1/tor... |
17,964 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/optim/nvnovograd.py | 54,56 | 13,121 | step | NvNovoGrad | 62,121 | 56,121 | class NvNovoGrad(Optimizer):
"""
Implements Novograd algorithm.
Args:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
lr (float, optional): learning rate (default: 1e-3)
betas (Tuple[float, float], optional): coefficients used for... | class NvNovoGrad(Optimizer):
"""
Implements Novograd algorithm.
Args:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
lr (float, optional): learning rate (default: 1e-3)
betas (Tuple[float, float], optional): coefficients used for... | loss = None
if closure is not None:
with torch.enable_grad():
loss = closure()
for group in self.param_groups:
for p in group['params']:
if p.grad is None:
continue
grad = p.grad
if grad.... | """Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
loss = None
if closure is not None:
with torch.enable_grad():
loss = closure()
... | 173 | 636 | 722 | 1,358 | """ Nvidia NovoGrad Optimizer.
Original impl by Nvidia from Jasper example:
- https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/SpeechRecognition/Jasper
Paper: `Stochastic Gradient Methods with Layer-wise Adaptive Moments for Training of Deep Networks`
- https://arxiv.org/abs/1905.11286
"""
im... | """ Nvidia NovoGrad Optimizer.
Original impl by Nvidia from Jasper example:
- https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/SpeechRecognition/Jasper
Paper: `Stochastic Gradient Methods with Layer-wise Adaptive Moments for Training of Deep Networks`
- https://arxiv.org/abs/1905.11286
"""
im... |
17,965 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/optim/radam.py | 21,23 | 10,90 | step | RAdam | 23,90 | 23,90 | class RAdam(Optimizer):
def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0):
defaults = dict(
lr=lr, betas=betas, eps=eps, weight_decay=weight_decay,
buffer=[[None, None, None] for _ in range(10)])
super(RAdam, self).__init__(params, defaults)
... | class RAdam(Optimizer):
def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0):
defaults = dict(
lr=lr, betas=betas, eps=eps, weight_decay=weight_decay,
buffer=[[None, None, None] for _ in range(10)])
super(RAdam, self).__init__(params, defaults)
... | loss = None
if closure is not None:
with torch.enable_grad():
loss = closure()
for group in self.param_groups:
for p in group['params']:
if p.grad is None:
continue
grad = p.grad.float()
... | loss = None
if closure is not None:
with torch.enable_grad():
loss = closure()
for group in self.param_groups:
for p in group['params']:
if p.grad is None:
continue
grad = p.grad.float()
... | 224 | 191 | 904 | 1,095 | """RAdam Optimizer.
Implementation lifted from: https://github.com/LiyuanLucasLiu/RAdam
Paper: `On the Variance of the Adaptive Learning Rate and Beyond` - https://arxiv.org/abs/1908.03265
"""
import math
import torch
from torch.optim.optimizer import Optimizer
class RAdam(Optimizer):
def __init__(self, params, ... | """RAdam Optimizer.
Implementation lifted from: https://github.com/LiyuanLucasLiu/RAdam
Paper: `On the Variance of the Adaptive Learning Rate and Beyond` - https://arxiv.org/abs/1908.03265
"""
import math
import torch
from torch.optim.optimizer import Optimizer
class RAdam(Optimizer):
def __init__(self, params, ... |
17,966 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/optim/rmsprop_tf.py | 72,74 | 14,140 | step | RMSpropTF | 80,140 | 74,140 | class RMSpropTF(Optimizer):
"""Implements RMSprop algorithm (TensorFlow style epsilon)
NOTE: This is a direct cut-and-paste of PyTorch RMSprop with eps applied before sqrt
and a few other modifications to closer match Tensorflow for matching hyper-params.
Noteworthy changes include:
1. Epsilon app... | class RMSpropTF(Optimizer):
"""Implements RMSprop algorithm (TensorFlow style epsilon)
NOTE: This is a direct cut-and-paste of PyTorch RMSprop with eps applied before sqrt
and a few other modifications to closer match Tensorflow for matching hyper-params.
Noteworthy changes include:
1. Epsilon app... | loss = None
if closure is not None:
with torch.enable_grad():
loss = closure()
for group in self.param_groups:
for p in group['params']:
if p.grad is None:
continue
grad = p.grad
if grad.... | """Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
loss = None
if closure is not None:
with torch.enable_grad():
loss = closure()
... | 197 | 931 | 801 | 1,732 | """ RMSProp modified to behave like Tensorflow impl
Originally cut & paste from PyTorch RMSProp
https://github.com/pytorch/pytorch/blob/063946d2b3f3f1e953a2a3b54e0b34f1393de295/torch/optim/rmsprop.py
Licensed under BSD-Clause 3 (ish), https://github.com/pytorch/pytorch/blob/master/LICENSE
Modifications Copyright 2021... | """ RMSProp modified to behave like Tensorflow impl
Originally cut & paste from PyTorch RMSProp
https://github.com/pytorch/pytorch/blob/063946d2b3f3f1e953a2a3b54e0b34f1393de295/torch/optim/rmsprop.py
Licensed under BSD-Clause 3 (ish), https://github.com/pytorch/pytorch/blob/master/LICENSE
Modifications Copyright 2021... |
17,967 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/optim/sgdp.py | 27,29 | 19,71 | step | SGDP | 29,71 | 29,71 | class SGDP(Optimizer):
def __init__(self, params, lr=required, momentum=0, dampening=0,
weight_decay=0, nesterov=False, eps=1e-8, delta=0.1, wd_ratio=0.1):
defaults = dict(
lr=lr, momentum=momentum, dampening=dampening, weight_decay=weight_decay,
nesterov=nesterov, e... | class SGDP(Optimizer):
def __init__(self, params, lr=required, momentum=0, dampening=0,
weight_decay=0, nesterov=False, eps=1e-8, delta=0.1, wd_ratio=0.1):
defaults = dict(
lr=lr, momentum=momentum, dampening=dampening, weight_decay=weight_decay,
nesterov=nesterov, e... | loss = None
if closure is not None:
with torch.enable_grad():
loss = closure()
for group in self.param_groups:
weight_decay = group['weight_decay']
momentum = group['momentum']
dampening = group['dampening']
nesterov = ... | loss = None
if closure is not None:
with torch.enable_grad():
loss = closure()
for group in self.param_groups:
weight_decay = group['weight_decay']
momentum = group['momentum']
dampening = group['dampening']
nesterov = ... | 116 | 185 | 411 | 596 | """
SGDP Optimizer Implementation copied from https://github.com/clovaai/AdamP/blob/master/adamp/sgdp.py
Paper: `Slowing Down the Weight Norm Increase in Momentum-based Optimizers` - https://arxiv.org/abs/2006.08217
Code: https://github.com/clovaai/AdamP
Copyright (c) 2020-present NAVER Corp.
MIT license
"""
import ... | """
SGDP Optimizer Implementation copied from https://github.com/clovaai/AdamP/blob/master/adamp/sgdp.py
Paper: `Slowing Down the Weight Norm Increase in Momentum-based Optimizers` - https://arxiv.org/abs/2006.08217
Code: https://github.com/clovaai/AdamP
Copyright (c) 2020-present NAVER Corp.
MIT license
"""
import ... |
17,969 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/optim/sgdw.py | 74,76 | 16,116 | step | SGDW | 82,116 | 76,116 | class SGDW(Optimizer):
def __init__(
self,
params,
lr=1e-3,
momentum=0,
dampening=0,
weight_decay=0,
nesterov=False,
*,
maximize: bool = False,
foreach: Optional[bool] = None,
differen... | class SGDW(Optimizer):
def __init__(
self,
params,
lr=1e-3,
momentum=0,
dampening=0,
weight_decay=0,
nesterov=False,
*,
maximize: bool = False,
foreach: Optional[bool] = None,
differen... | loss = None
if closure is not None:
with torch.enable_grad():
loss = closure()
for group in self.param_groups:
params_with_grad = []
d_p_list = []
momentum_buffer_list = []
has_sparse_grad = self._init_group(group, par... | """Performs a single optimization step.
Args:
closure (Callable, optional): A closure that reevaluates the model
and returns the loss.
"""
loss = None
if closure is not None:
with torch.enable_grad():
loss = closure()
... | 64 | 679 | 327 | 1,006 | from functools import update_wrapper, wraps
import torch
from torch import Tensor
from torch.optim.optimizer import Optimizer
try:
from torch.optim.optimizer import _use_grad_for_differentiable, _default_to_fused_or_foreach
has_recent_pt = True
except ImportError:
has_recent_pt = False
from typing import L... | from functools import update_wrapper, wraps
import torch
from torch import Tensor
from torch.optim.optimizer import Optimizer
try:
from torch.optim.optimizer import _use_grad_for_differentiable, _default_to_fused_or_foreach
has_recent_pt = True
except ImportError:
has_recent_pt = False
from typing import L... |
17,970 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/scheduler/cosine_lr.py | 81,82 | 19,117 | _get_lr | CosineLRScheduler | 82,111 | 82,111 | class CosineLRScheduler(Scheduler):
"""
Cosine decay with restarts.
This is described in the paper https://arxiv.org/abs/1608.03983.
Inspiration from
https://github.com/allenai/allennlp/blob/master/allennlp/training/learning_rate_schedulers/cosine.py
k-decay option based on `k-decay: A New Met... | class CosineLRScheduler(Scheduler):
"""
Cosine decay with restarts.
This is described in the paper https://arxiv.org/abs/1608.03983.
Inspiration from
https://github.com/allenai/allennlp/blob/master/allennlp/training/learning_rate_schedulers/cosine.py
k-decay option based on `k-decay: A New Met... | if t < self.warmup_t:
lrs = [self.warmup_lr_init + t * s for s in self.warmup_steps]
else:
if self.warmup_prefix:
t = t - self.warmup_t
if self.cycle_mul != 1:
i = math.floor(math.log(1 - t / self.t_initial * (1 - self.cycle_mul), self... | if t < self.warmup_t:
lrs = [self.warmup_lr_init + t * s for s in self.warmup_steps]
else:
if self.warmup_prefix:
t = t - self.warmup_t
if self.cycle_mul != 1:
i = math.floor(math.log(1 - t / self.t_initial * (1 - self.cycle_mul), self... | 135 | 863 | 419 | 1,282 | """ Cosine Scheduler
Cosine LR schedule with warmup, cycle/restarts, noise, k-decay.
Hacked together by / Copyright 2021 Ross Wightman
"""
import logging
import math
import numpy as np
import torch
from typing import List
from .scheduler import Scheduler
_logger = logging.getLogger(__name__)
class CosineLRSchedu... | """ Cosine Scheduler
Cosine LR schedule with warmup, cycle/restarts, noise, k-decay.
Hacked together by / Copyright 2021 Ross Wightman
"""
import logging
import math
import numpy as np
import torch
from typing import List
from .scheduler import Scheduler
_logger = logging.getLogger(__name__)
class CosineLRSchedu... |
17,971 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/scheduler/multistep_lr.py | 51,52 | 10,64 | get_curr_decay_steps | MultiStepLRScheduler | 52,56 | 52,56 | class MultiStepLRScheduler(Scheduler):
"""
"""
def __init__(
self,
optimizer: torch.optim.Optimizer,
decay_t: List[int],
decay_rate: float = 1.,
warmup_t=0,
warmup_lr_init=0,
warmup_prefix=True,
t_in_epochs=True... | class MultiStepLRScheduler(Scheduler):
"""
"""
def __init__(
self,
optimizer: torch.optim.Optimizer,
decay_t: List[int],
decay_rate: float = 1.,
warmup_t=0,
warmup_lr_init=0,
warmup_prefix=True,
t_in_epochs=True... | # find where in the array t goes,
# assumes self.decay_t is sorted
return bisect.bisect_right(self.decay_t, t + 1)
| # find where in the array t goes,
# assumes self.decay_t is sorted
return bisect.bisect_right(self.decay_t, t + 1)
| 18 | 589 | 50 | 639 | """ MultiStep LR Scheduler
Basic multi step LR schedule with warmup, noise.
"""
import torch
import bisect
from timm.scheduler.scheduler import Scheduler
from typing import List
class MultiStepLRScheduler(Scheduler):
"""
"""
def __init__(
self,
optimizer: torch.optim.Optimizer,
... | """ MultiStep LR Scheduler
Basic multi step LR schedule with warmup, noise.
"""
import torch
import bisect
from timm.scheduler.scheduler import Scheduler
from typing import List
class MultiStepLRScheduler(Scheduler):
"""
"""
def __init__(
self,
optimizer: torch.optim.Optimizer,
... |
17,972 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/scheduler/plateau_lr.py | 72,73 | 13,112 | load_state_dict | PlateauLRScheduler | 73,78 | 73,78 | class PlateauLRScheduler(Scheduler):
"""Decay the LR by a factor every time the validation loss plateaus."""
def __init__(
self,
optimizer,
decay_rate=0.1,
patience_t=10,
verbose=True,
threshold=1e-4,
cooldown_t=0,
... | class PlateauLRScheduler(Scheduler):
"""Decay the LR by a factor every time the validation loss plateaus."""
def __init__(
self,
optimizer,
decay_rate=0.1,
patience_t=10,
verbose=True,
threshold=1e-4,
cooldown_t=0,
... | self.lr_scheduler.best = state_dict['best']
if 'last_epoch' in state_dict:
self.lr_scheduler.last_epoch = state_dict['last_epoch']
# override the base class step fn completely
| self.lr_scheduler.best = state_dict['best']
if 'last_epoch' in state_dict:
self.lr_scheduler.last_epoch = state_dict['last_epoch']
# override the base class step fn completely
| 18 | 1,030 | 72 | 1,102 | """ Plateau Scheduler
Adapts PyTorch plateau scheduler and allows application of noise, warmup.
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from typing import List
from .scheduler import Scheduler
class PlateauLRScheduler(Scheduler):
"""Decay the LR by a factor every time the validation ... | """ Plateau Scheduler
Adapts PyTorch plateau scheduler and allows application of noise, warmup.
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from typing import List
from .scheduler import Scheduler
class PlateauLRScheduler(Scheduler):
"""Decay the LR by a factor every time the validation ... |
17,973 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/scheduler/poly_lr.py | 107,108 | 19,113 | get_cycle_length | PolyLRScheduler | 108,113 | 108,113 | class PolyLRScheduler(Scheduler):
""" Polynomial LR Scheduler w/ warmup, noise, and k-decay
k-decay option based on `k-decay: A New Method For Learning Rate Schedule` - https://arxiv.org/abs/2004.05909
"""
def __init__(
self,
optimizer: torch.optim.Optimizer,
t_init... | class PolyLRScheduler(Scheduler):
""" Polynomial LR Scheduler w/ warmup, noise, and k-decay
k-decay option based on `k-decay: A New Method For Learning Rate Schedule` - https://arxiv.org/abs/2004.05909
"""
def __init__(
self,
optimizer: torch.optim.Optimizer,
t_init... | cycles = max(1, cycles or self.cycle_limit)
if self.cycle_mul == 1.0:
return self.t_initial * cycles
else:
return int(math.floor(-self.t_initial * (self.cycle_mul ** cycles - 1) / (1 - self.cycle_mul)))
| cycles = max(1, cycles or self.cycle_limit)
if self.cycle_mul == 1.0:
return self.t_initial * cycles
else:
return int(math.floor(-self.t_initial * (self.cycle_mul ** cycles - 1) / (1 - self.cycle_mul)))
| 27 | 1,111 | 88 | 1,199 | """ Polynomial Scheduler
Polynomial LR schedule with warmup, noise.
Hacked together by / Copyright 2021 Ross Wightman
"""
import math
import logging
from typing import List
import torch
from .scheduler import Scheduler
_logger = logging.getLogger(__name__)
class PolyLRScheduler(Scheduler):
""" Polynomial LR... | """ Polynomial Scheduler
Polynomial LR schedule with warmup, noise.
Hacked together by / Copyright 2021 Ross Wightman
"""
import math
import logging
from typing import List
import torch
from .scheduler import Scheduler
_logger = logging.getLogger(__name__)
class PolyLRScheduler(Scheduler):
""" Polynomial LR... |
17,974 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/scheduler/scheduler.py | 106,107 | 8,128 | _is_apply_noise | Scheduler | 108,116 | 107,116 | class Scheduler(ABC):
""" Parameter Scheduler Base Class
A scheduler base class that can be used to schedule any optimizer parameter groups.
Unlike the builtin PyTorch schedulers, this is intended to be consistently called
* At the END of each epoch, before incrementing the epoch count, to calculate ne... | class Scheduler(ABC):
""" Parameter Scheduler Base Class
A scheduler base class that can be used to schedule any optimizer parameter groups.
Unlike the builtin PyTorch schedulers, this is intended to be consistently called
* At the END of each epoch, before incrementing the epoch count, to calculate ne... | apply_noise = False
if self.noise_range_t is not None:
if isinstance(self.noise_range_t, (list, tuple)):
apply_noise = self.noise_range_t[0] <= t < self.noise_range_t[1]
else:
apply_noise = t >= self.noise_range_t
return apply_noise
| """Return True if scheduler in noise range."""
apply_noise = False
if self.noise_range_t is not None:
if isinstance(self.noise_range_t, (list, tuple)):
apply_noise = self.noise_range_t[0] <= t < self.noise_range_t[1]
else:
apply_noise = t >... | 27 | 1,514 | 104 | 1,618 | import abc
from abc import ABC
from typing import Any, Dict, List, Optional
import torch
class Scheduler(ABC):
""" Parameter Scheduler Base Class
A scheduler base class that can be used to schedule any optimizer parameter groups.
Unlike the builtin PyTorch schedulers, this is intended to be consistently... | import abc
from abc import ABC
from typing import Any, Dict, List, Optional
import torch
class Scheduler(ABC):
""" Parameter Scheduler Base Class
A scheduler base class that can be used to schedule any optimizer parameter groups.
Unlike the builtin PyTorch schedulers, this is intended to be consistently... |
17,975 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/scheduler/step_lr.py | 56,57 | 15,64 | _get_lr | StepLRScheduler | 57,64 | 57,64 | class StepLRScheduler(Scheduler):
"""
"""
def __init__(
self,
optimizer: torch.optim.Optimizer,
decay_t: float,
decay_rate: float = 1.,
warmup_t=0,
warmup_lr_init=0,
warmup_prefix=True,
t_in_epochs=True,
... | class StepLRScheduler(Scheduler):
"""
"""
def __init__(
self,
optimizer: torch.optim.Optimizer,
decay_t: float,
decay_rate: float = 1.,
warmup_t=0,
warmup_lr_init=0,
warmup_prefix=True,
t_in_epochs=True,
... | if t < self.warmup_t:
lrs = [self.warmup_lr_init + t * s for s in self.warmup_steps]
else:
if self.warmup_prefix:
t = t - self.warmup_t
lrs = [v * (self.decay_rate ** (t // self.decay_t)) for v in self.base_values]
return lrs
| if t < self.warmup_t:
lrs = [self.warmup_lr_init + t * s for s in self.warmup_steps]
else:
if self.warmup_prefix:
t = t - self.warmup_t
lrs = [v * (self.decay_rate ** (t // self.decay_t)) for v in self.base_values]
return lrs
| 38 | 453 | 119 | 572 | """ Step Scheduler
Basic step LR schedule with warmup, noise.
Hacked together by / Copyright 2020 Ross Wightman
"""
import math
import torch
from typing import List
from .scheduler import Scheduler
class StepLRScheduler(Scheduler):
"""
"""
def __init__(
self,
optimizer: torch.... | """ Step Scheduler
Basic step LR schedule with warmup, noise.
Hacked together by / Copyright 2020 Ross Wightman
"""
import math
import torch
from typing import List
from .scheduler import Scheduler
class StepLRScheduler(Scheduler):
"""
"""
def __init__(
self,
optimizer: torch.... |
17,976 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/scheduler/tanh_lr.py | 79,80 | 19,114 | _get_lr | TanhLRScheduler | 80,108 | 80,108 | class TanhLRScheduler(Scheduler):
"""
Hyberbolic-Tangent decay with restarts.
This is described in the paper https://arxiv.org/abs/1806.01593
"""
def __init__(
self,
optimizer: torch.optim.Optimizer,
t_initial: int,
lb: float = -7.,
ub: fl... | class TanhLRScheduler(Scheduler):
"""
Hyberbolic-Tangent decay with restarts.
This is described in the paper https://arxiv.org/abs/1806.01593
"""
def __init__(
self,
optimizer: torch.optim.Optimizer,
t_initial: int,
lb: float = -7.,
ub: fl... | if t < self.warmup_t:
lrs = [self.warmup_lr_init + t * s for s in self.warmup_steps]
else:
if self.warmup_prefix:
t = t - self.warmup_t
if self.cycle_mul != 1:
i = math.floor(math.log(1 - t / self.t_initial * (1 - self.cycle_mul), self... | if t < self.warmup_t:
lrs = [self.warmup_lr_init + t * s for s in self.warmup_steps]
else:
if self.warmup_prefix:
t = t - self.warmup_t
if self.cycle_mul != 1:
i = math.floor(math.log(1 - t / self.t_initial * (1 - self.cycle_mul), self... | 137 | 783 | 420 | 1,203 | """ TanH Scheduler
TanH schedule with warmup, cycle/restarts, noise.
Hacked together by / Copyright 2021 Ross Wightman
"""
import logging
import math
import numpy as np
import torch
from typing import List
from .scheduler import Scheduler
_logger = logging.getLogger(__name__)
class TanhLRScheduler(Scheduler):
... | """ TanH Scheduler
TanH schedule with warmup, cycle/restarts, noise.
Hacked together by / Copyright 2021 Ross Wightman
"""
import logging
import math
import numpy as np
import torch
from typing import List
from .scheduler import Scheduler
_logger = logging.getLogger(__name__)
class TanhLRScheduler(Scheduler):
... |
17,977 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/utils/agc.py | 21,22 | 1,43 | unitwise_norm | 22,30 | 22,30 | """ Adaptive Gradient Clipping
An impl of AGC, as per (https://arxiv.org/abs/2102.06171):
@article{brock2021high,
author={Andrew Brock and Soham De and Samuel L. Smith and Karen Simonyan},
title={High-Performance Large-Scale Image Recognition Without Normalization},
journal={arXiv preprint arXiv:},
year={2021... | """ Adaptive Gradient Clipping
An impl of AGC, as per (https://arxiv.org/abs/2102.06171):
@article{brock2021high,
author={Andrew Brock and Soham De and Samuel L. Smith and Karen Simonyan},
title={High-Performance Large-Scale Image Recognition Without Normalization},
journal={arXiv preprint arXiv:},
year={2021... | if x.ndim <= 1:
return x.norm(norm_type)
else:
# works for nn.ConvNd and nn,Linear where output dim is first in the kernel/weight tensor
# might need special cases for other weights (possibly MHA) where this may not be true
return x.norm(norm_type, dim=tuple(range(1, x.ndim)), ke... | if x.ndim <= 1:
return x.norm(norm_type)
else:
# works for nn.ConvNd and nn,Linear where output dim is first in the kernel/weight tensor
# might need special cases for other weights (possibly MHA) where this may not be true
return x.norm(norm_type, dim=tuple(range(1, x.ndim)), ke... | 43 | 507 | 115 | 622 | """ Adaptive Gradient Clipping
An impl of AGC, as per (https://arxiv.org/abs/2102.06171):
@article{brock2021high,
author={Andrew Brock and Soham De and Samuel L. Smith and Karen Simonyan},
title={High-Performance Large-Scale Image Recognition Without Normalization},
journal={arXiv preprint arXiv:},
year={2021... | """ Adaptive Gradient Clipping
An impl of AGC, as per (https://arxiv.org/abs/2102.06171):
@article{brock2021high,
author={Andrew Brock and Soham De and Samuel L. Smith and Karen Simonyan},
title={High-Performance Large-Scale Image Recognition Without Normalization},
journal={arXiv preprint arXiv:},
year={2021... | |
17,978 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/utils/clip_grad.py | 6,7 | 1,24 | dispatch_clip_grad | 15,24 | 7,24 | import torch
from timm.utils.agc import adaptive_clip_grad
def dispatch_clip_grad(parameters, value: float, mode: str = 'norm', norm_type: float = 2.0):
FILL_FUNC_BODY | import torch
from timm.utils.agc import adaptive_clip_grad
def dispatch_clip_grad(parameters, value: float, mode: str = 'norm', norm_type: float = 2.0):
""" Dispatch to gradient clipping method
Args:
parameters (Iterable): model parameters to clip
value (float): clipping value/factor/norm, m... | if mode == 'norm':
torch.nn.utils.clip_grad_norm_(parameters, value, norm_type=norm_type)
elif mode == 'value':
torch.nn.utils.clip_grad_value_(parameters, value)
elif mode == 'agc':
adaptive_clip_grad(parameters, value, norm_type=norm_type)
else:
assert False, f"Unknown ... | """ Dispatch to gradient clipping method
Args:
parameters (Iterable): model parameters to clip
value (float): clipping value/factor/norm, mode dependant
mode (str): clipping mode, one of 'norm', 'value', 'agc'
norm_type (float): p-norm, default 2.0
"""
if mode == 'norm':... | 27 | 150 | 121 | 271 | import torch
from timm.utils.agc import adaptive_clip_grad
def dispatch_clip_grad(parameters, value: float, mode: str = 'norm', norm_type: float = 2.0):
FILL_FUNC_BODY | import torch
from timm.utils.agc import adaptive_clip_grad
def dispatch_clip_grad(parameters, value: float, mode: str = 'norm', norm_type: float = 2.0):
""" Dispatch to gradient clipping method
Args:
parameters (Iterable): model parameters to clip
value (float): clipping value/factor/norm, m... | |
17,979 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/utils/cuda.py | 37,38 | 17,46 | state_dict | ApexScaler | 38,41 | 38,41 | class ApexScaler:
state_dict_key = "amp"
def __call__(
self,
loss,
optimizer,
clip_grad=None,
clip_mode='norm',
parameters=None,
create_graph=False,
need_update=True,
):
with amp.scale_loss(loss, optimiz... | class ApexScaler:
state_dict_key = "amp"
def __call__(
self,
loss,
optimizer,
clip_grad=None,
clip_mode='norm',
parameters=None,
create_graph=False,
need_update=True,
):
with amp.scale_loss(loss, optimiz... | if 'state_dict' in amp.__dict__:
return amp.state_dict()
| if 'state_dict' in amp.__dict__:
return amp.state_dict()
| 6 | 236 | 27 | 263 | """ CUDA / AMP utils
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
try:
from apex import amp
has_apex = True
except ImportError:
amp = None
has_apex = False
from .clip_grad import dispatch_clip_grad
class ApexScaler:
state_dict_key = "amp"
def __call__(
sel... | """ CUDA / AMP utils
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
try:
from apex import amp
has_apex = True
except ImportError:
amp = None
has_apex = False
from .clip_grad import dispatch_clip_grad
class ApexScaler:
state_dict_key = "amp"
def __call__(
sel... |
17,980 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/utils/cuda.py | 71,72 | 46,76 | state_dict | NativeScaler | 72,74 | 72,74 | class NativeScaler:
state_dict_key = "amp_scaler"
def __init__(self):
self._scaler = torch.cuda.amp.GradScaler()
def __call__(
self,
loss,
optimizer,
clip_grad=None,
clip_mode='norm',
parameters=None,
create_graph=... | class NativeScaler:
state_dict_key = "amp_scaler"
def __init__(self):
self._scaler = torch.cuda.amp.GradScaler()
def __call__(
self,
loss,
optimizer,
clip_grad=None,
clip_mode='norm',
parameters=None,
create_graph=... | return self._scaler.state_dict()
| return self._scaler.state_dict()
| 2 | 280 | 15 | 295 | """ CUDA / AMP utils
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
try:
from apex import amp
has_apex = True
except ImportError:
amp = None
has_apex = False
from .clip_grad import dispatch_clip_grad
class ApexScaler:
state_dict_key = "amp"
def __call__(
sel... | """ CUDA / AMP utils
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
try:
from apex import amp
has_apex = True
except ImportError:
amp = None
has_apex = False
from .clip_grad import dispatch_clip_grad
class ApexScaler:
state_dict_key = "amp"
def __call__(
sel... |
17,981 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/utils/decay_batch.py | 32,33 | 1,44 | check_batch_size_retry | 35,44 | 33,44 | """ Batch size decay and retry helpers.
Copyright 2022 Ross Wightman
"""
import math
def decay_batch_step(batch_size, num_intra_steps=2, no_odd=False):
""" power of two batch-size decay with intra steps
Decay by stepping between powers of 2:
* determine power-of-2 floor of current batch size (base batch... | """ Batch size decay and retry helpers.
Copyright 2022 Ross Wightman
"""
import math
def decay_batch_step(batch_size, num_intra_steps=2, no_odd=False):
""" power of two batch-size decay with intra steps
Decay by stepping between powers of 2:
* determine power-of-2 floor of current batch size (base batch... | error_str = error_str.lower()
if 'required rank' in error_str:
# Errors involving phrase 'required rank' typically happen when a conv is used that's
# not compatible with channels_last memory format.
return False
if 'illegal' in error_str:
# 'Illegal memory access' errors in ... | """ check failure error string for conditions where batch decay retry should not be attempted
"""
error_str = error_str.lower()
if 'required rank' in error_str:
# Errors involving phrase 'required rank' typically happen when a conv is used that's
# not compatible with channels_last memor... | 52 | 577 | 110 | 687 | """ Batch size decay and retry helpers.
Copyright 2022 Ross Wightman
"""
import math
def decay_batch_step(batch_size, num_intra_steps=2, no_odd=False):
""" power of two batch-size decay with intra steps
Decay by stepping between powers of 2:
* determine power-of-2 floor of current batch size (base batch... | """ Batch size decay and retry helpers.
Copyright 2022 Ross Wightman
"""
import math
def decay_batch_step(batch_size, num_intra_steps=2, no_odd=False):
""" power of two batch-size decay with intra steps
Decay by stepping between powers of 2:
* determine power-of-2 floor of current batch size (base batch... | |
17,982 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/utils/distributed.py | 24,25 | 1,176 | distribute_bn | 25,37 | 25,37 | """ Distributed training/validation utils
Hacked together by / Copyright 2020 Ross Wightman
"""
import logging
import os
from typing import Optional
import torch
from torch import distributed as dist
from .model import unwrap_model
_logger = logging.getLogger(__name__)
def reduce_tensor(tensor, n):
rt = tenso... | """ Distributed training/validation utils
Hacked together by / Copyright 2020 Ross Wightman
"""
import logging
import os
from typing import Optional
import torch
from torch import distributed as dist
from .model import unwrap_model
_logger = logging.getLogger(__name__)
def reduce_tensor(tensor, n):
rt = tenso... | # ensure every node has the same running bn stats
for bn_name, bn_buf in unwrap_model(model).named_buffers(recurse=True):
if ('running_mean' in bn_name) or ('running_var' in bn_name):
if reduce:
# average bn stats across whole group
torch.distributed.all_reduc... | # ensure every node has the same running bn stats
for bn_name, bn_buf in unwrap_model(model).named_buffers(recurse=True):
if ('running_mean' in bn_name) or ('running_var' in bn_name):
if reduce:
# average bn stats across whole group
torch.distributed.all_reduc... | 50 | 1,706 | 178 | 1,884 | """ Distributed training/validation utils
Hacked together by / Copyright 2020 Ross Wightman
"""
import logging
import os
from typing import Optional
import torch
from torch import distributed as dist
from .model import unwrap_model
_logger = logging.getLogger(__name__)
def reduce_tensor(tensor, n):
rt = tenso... | """ Distributed training/validation utils
Hacked together by / Copyright 2020 Ross Wightman
"""
import logging
import os
from typing import Optional
import torch
from torch import distributed as dist
from .model import unwrap_model
_logger = logging.getLogger(__name__)
def reduce_tensor(tensor, n):
rt = tenso... | |
17,983 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/utils/jit.py | 23,24 | 1,59 | set_jit_fuser | 24,59 | 24,59 | """ JIT scripting/tracing utils
Hacked together by / Copyright 2020 Ross Wightman
"""
import os
import torch
def set_jit_legacy():
""" Set JIT executor to legacy w/ support for op fusion
This is hopefully a temporary need in 1.5/1.5.1/1.6 to restore performance due to changes
in the JIT exectutor. These... | """ JIT scripting/tracing utils
Hacked together by / Copyright 2020 Ross Wightman
"""
import os
import torch
def set_jit_legacy():
""" Set JIT executor to legacy w/ support for op fusion
This is hopefully a temporary need in 1.5/1.5.1/1.6 to restore performance due to changes
in the JIT exectutor. These... | if fuser == "te":
# default fuser should be == 'te'
torch._C._jit_set_profiling_executor(True)
torch._C._jit_set_profiling_mode(True)
torch._C._jit_override_can_fuse_on_cpu(False)
torch._C._jit_override_can_fuse_on_gpu(True)
torch._C._jit_set_texpr_fuser_enabled(True)... | if fuser == "te":
# default fuser should be == 'te'
torch._C._jit_set_profiling_executor(True)
torch._C._jit_set_profiling_mode(True)
torch._C._jit_override_can_fuse_on_cpu(False)
torch._C._jit_override_can_fuse_on_gpu(True)
torch._C._jit_set_texpr_fuser_enabled(True)... | 71 | 269 | 650 | 919 | """ JIT scripting/tracing utils
Hacked together by / Copyright 2020 Ross Wightman
"""
import os
import torch
def set_jit_legacy():
""" Set JIT executor to legacy w/ support for op fusion
This is hopefully a temporary need in 1.5/1.5.1/1.6 to restore performance due to changes
in the JIT exectutor. These... | """ JIT scripting/tracing utils
Hacked together by / Copyright 2020 Ross Wightman
"""
import os
import torch
def set_jit_legacy():
""" Set JIT executor to legacy w/ support for op fusion
This is hopefully a temporary need in 1.5/1.5.1/1.6 to restore performance due to changes
in the JIT exectutor. These... | |
17,984 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/utils/log.py | 19,20 | 1,29 | setup_default_logging | 20,29 | 20,29 | """ Logging helpers
Hacked together by / Copyright 2020 Ross Wightman
"""
import logging
import logging.handlers
class FormatterNoInfo(logging.Formatter):
def __init__(self, fmt='%(levelname)s: %(message)s'):
logging.Formatter.__init__(self, fmt)
def format(self, record):
if record.levelno =... | """ Logging helpers
Hacked together by / Copyright 2020 Ross Wightman
"""
import logging
import logging.handlers
class FormatterNoInfo(logging.Formatter):
def __init__(self, fmt='%(levelname)s: %(message)s'):
logging.Formatter.__init__(self, fmt)
def format(self, record):
if record.levelno =... | console_handler = logging.StreamHandler()
console_handler.setFormatter(FormatterNoInfo())
logging.root.addHandler(console_handler)
logging.root.setLevel(default_level)
if log_path:
file_handler = logging.handlers.RotatingFileHandler(log_path, maxBytes=(1024 ** 2 * 2), backupCount=3)
... | console_handler = logging.StreamHandler()
console_handler.setFormatter(FormatterNoInfo())
logging.root.addHandler(console_handler)
logging.root.setLevel(default_level)
if log_path:
file_handler = logging.handlers.RotatingFileHandler(log_path, maxBytes=(1024 ** 2 * 2), backupCount=3)
... | 27 | 166 | 179 | 345 | """ Logging helpers
Hacked together by / Copyright 2020 Ross Wightman
"""
import logging
import logging.handlers
class FormatterNoInfo(logging.Formatter):
def __init__(self, fmt='%(levelname)s: %(message)s'):
logging.Formatter.__init__(self, fmt)
def format(self, record):
if record.levelno =... | """ Logging helpers
Hacked together by / Copyright 2020 Ross Wightman
"""
import logging
import logging.handlers
class FormatterNoInfo(logging.Formatter):
def __init__(self, fmt='%(levelname)s: %(message)s'):
logging.Formatter.__init__(self, fmt)
def format(self, record):
if record.levelno =... | |
17,985 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/utils/log.py | 13,14 | 9,19 | format | FormatterNoInfo | 14,19 | 14,19 | class FormatterNoInfo(logging.Formatter):
def __init__(self, fmt='%(levelname)s: %(message)s'):
logging.Formatter.__init__(self, fmt)
def format(self, record):
FILL_FUNC_BODY | class FormatterNoInfo(logging.Formatter):
def __init__(self, fmt='%(levelname)s: %(message)s'):
logging.Formatter.__init__(self, fmt)
def format(self, record):
FILL_FUNC_BODY | if record.levelno == logging.INFO:
return str(record.getMessage())
return logging.Formatter.format(self, record)
| if record.levelno == logging.INFO:
return str(record.getMessage())
return logging.Formatter.format(self, record)
| 9 | 70 | 42 | 112 | """ Logging helpers
Hacked together by / Copyright 2020 Ross Wightman
"""
import logging
import logging.handlers
class FormatterNoInfo(logging.Formatter):
def __init__(self, fmt='%(levelname)s: %(message)s'):
logging.Formatter.__init__(self, fmt)
def format(self, record):
FILL_FUNC_BODYdef setup_def... | """ Logging helpers
Hacked together by / Copyright 2020 Ross Wightman
"""
import logging
import logging.handlers
class FormatterNoInfo(logging.Formatter):
def __init__(self, fmt='%(levelname)s: %(message)s'):
logging.Formatter.__init__(self, fmt)
def format(self, record):
FILL_FUNC_BODYdef setup_def... |
17,986 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/utils/metrics.py | 25,26 | 1,33 | accuracy | 27,33 | 26,33 | """ Eval metrics and related
Hacked together by / Copyright 2020 Ross Wightman
"""
class AverageMeter:
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
... | """ Eval metrics and related
Hacked together by / Copyright 2020 Ross Wightman
"""
class AverageMeter:
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
... | maxk = min(max(topk), output.size()[1])
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.reshape(1, -1).expand_as(pred))
return [correct[:min(k, maxk)].reshape(-1).float().sum(0) * 100. / batch_size for k in topk]
| """Computes the accuracy over the k top predictions for the specified values of k"""
maxk = min(max(topk), output.size()[1])
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.reshape(1, -1).expand_as(pred))
return [correct[:min(k,... | 32 | 198 | 130 | 328 | """ Eval metrics and related
Hacked together by / Copyright 2020 Ross Wightman
"""
class AverageMeter:
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
... | """ Eval metrics and related
Hacked together by / Copyright 2020 Ross Wightman
"""
class AverageMeter:
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
... | |
17,987 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/utils/metrics.py | 12,13 | 7,25 | reset | AverageMeter | 13,18 | 13,18 | class AverageMeter:
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
FILL_FUNC_BODY def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
| class AverageMeter:
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
FILL_FUNC_BODY def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
| self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
| self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
| 12 | 104 | 35 | 139 | """ Eval metrics and related
Hacked together by / Copyright 2020 Ross Wightman
"""
class AverageMeter:
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
FILL_FUNC_BODY def update(self, val, n=1):
self.val = val
self.sum +... | """ Eval metrics and related
Hacked together by / Copyright 2020 Ross Wightman
"""
class AverageMeter:
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
FILL_FUNC_BODY def update(self, val, n=1):
self.val = val
self.sum +... |
17,988 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/utils/misc.py | 15,16 | 1,33 | add_bool_arg | 16,23 | 16,23 | """ Misc utils
Hacked together by / Copyright 2020 Ross Wightman
"""
import argparse
import ast
import re
def natural_key(string_):
"""See http://www.codinghorror.com/blog/archives/001018.html"""
return [int(s) if s.isdigit() else s for s in re.split(r'(\d+)', string_.lower())]
def add_bool_arg(parser, nam... | """ Misc utils
Hacked together by / Copyright 2020 Ross Wightman
"""
import argparse
import ast
import re
def natural_key(string_):
"""See http://www.codinghorror.com/blog/archives/001018.html"""
return [int(s) if s.isdigit() else s for s in re.split(r'(\d+)', string_.lower())]
def add_bool_arg(parser, nam... | dest_name = name.replace('-', '_')
group = parser.add_mutually_exclusive_group(required=False)
group.add_argument('--' + name, dest=dest_name, action='store_true', help=help)
group.add_argument('--no-' + name, dest=dest_name, action='store_false', help=help)
parser.set_defaults(**{dest_name: default... | dest_name = name.replace('-', '_')
group = parser.add_mutually_exclusive_group(required=False)
group.add_argument('--' + name, dest=dest_name, action='store_true', help=help)
group.add_argument('--no-' + name, dest=dest_name, action='store_false', help=help)
parser.set_defaults(**{dest_name: default... | 21 | 271 | 121 | 392 | """ Misc utils
Hacked together by / Copyright 2020 Ross Wightman
"""
import argparse
import ast
import re
def natural_key(string_):
"""See http://www.codinghorror.com/blog/archives/001018.html"""
return [int(s) if s.isdigit() else s for s in re.split(r'(\d+)', string_.lower())]
def add_bool_arg(parser, nam... | """ Misc utils
Hacked together by / Copyright 2020 Ross Wightman
"""
import argparse
import ast
import re
def natural_key(string_):
"""See http://www.codinghorror.com/blog/archives/001018.html"""
return [int(s) if s.isdigit() else s for s in re.split(r'(\d+)', string_.lower())]
def add_bool_arg(parser, nam... | |
17,990 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/utils/model.py | 81,82 | 45,88 | register_hook | ActivationStatsHook | 82,88 | 82,88 | class ActivationStatsHook:
"""Iterates through each of `model`'s modules and matches modules using unix pattern
matching based on `hook_fn_locs` and registers `hook_fn` to the module if there is
a match.
Arguments:
model (nn.Module): model from which we will extract the activation stats
... | class ActivationStatsHook:
"""Iterates through each of `model`'s modules and matches modules using unix pattern
matching based on `hook_fn_locs` and registers `hook_fn` to the module if there is
a match.
Arguments:
model (nn.Module): model from which we will extract the activation stats
... | for name, module in self.model.named_modules():
if not fnmatch.fnmatch(name, hook_fn_loc):
continue
module.register_forward_hook(self._create_hook(hook_fn))
| for name, module in self.model.named_modules():
if not fnmatch.fnmatch(name, hook_fn_loc):
continue
module.register_forward_hook(self._create_hook(hook_fn))
| 11 | 546 | 64 | 610 | """ Model / state_dict utils
Hacked together by / Copyright 2020 Ross Wightman
"""
import fnmatch
from copy import deepcopy
import torch
from torchvision.ops.misc import FrozenBatchNorm2d
from timm.layers import BatchNormAct2d, SyncBatchNormAct, FrozenBatchNormAct2d,\
freeze_batch_norm_2d, unfreeze_batch_norm_2d... | """ Model / state_dict utils
Hacked together by / Copyright 2020 Ross Wightman
"""
import fnmatch
from copy import deepcopy
import torch
from torchvision.ops.misc import FrozenBatchNorm2d
from timm.layers import BatchNormAct2d, SyncBatchNormAct, FrozenBatchNormAct2d,\
freeze_batch_norm_2d, unfreeze_batch_norm_2d... |
17,991 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/utils/model_ema.py | 52,53 | 16,83 | _load_checkpoint | ModelEma | 53,69 | 53,69 | class ModelEma:
""" Model Exponential Moving Average (DEPRECATED)
Keep a moving average of everything in the model state_dict (parameters and buffers).
This version is deprecated, it does not work with scripted models. Will be removed eventually.
This is intended to allow functionality like
https:... | class ModelEma:
""" Model Exponential Moving Average (DEPRECATED)
Keep a moving average of everything in the model state_dict (parameters and buffers).
This version is deprecated, it does not work with scripted models. Will be removed eventually.
This is intended to allow functionality like
https:... | checkpoint = torch.load(checkpoint_path, map_location='cpu')
assert isinstance(checkpoint, dict)
if 'state_dict_ema' in checkpoint:
new_state_dict = OrderedDict()
for k, v in checkpoint['state_dict_ema'].items():
# ema model may have been wrapped by DataPa... | checkpoint = torch.load(checkpoint_path, map_location='cpu')
assert isinstance(checkpoint, dict)
if 'state_dict_ema' in checkpoint:
new_state_dict = OrderedDict()
for k, v in checkpoint['state_dict_ema'].items():
# ema model may have been wrapped by DataPa... | 64 | 672 | 226 | 898 | """ Exponential Moving Average (EMA) of model updates
Hacked together by / Copyright 2020 Ross Wightman
"""
import logging
from collections import OrderedDict
from copy import deepcopy
from typing import Optional
import torch
import torch.nn as nn
_logger = logging.getLogger(__name__)
class ModelEma:
""" Model... | """ Exponential Moving Average (EMA) of model updates
Hacked together by / Copyright 2020 Ross Wightman
"""
import logging
from collections import OrderedDict
from copy import deepcopy
from typing import Optional
import torch
import torch.nn as nn
_logger = logging.getLogger(__name__)
class ModelEma:
""" Model... |
17,992 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/utils/model_ema.py | 129,130 | 83,133 | forward | ModelEmaV2 | 130,133 | 130,133 | class ModelEmaV2(nn.Module):
""" Model Exponential Moving Average V2
Keep a moving average of everything in the model state_dict (parameters and buffers).
V2 of this module is simpler, it does not match params/buffers based on name but simply
iterates in order. It works with torchscript (JIT of full mo... | class ModelEmaV2(nn.Module):
""" Model Exponential Moving Average V2
Keep a moving average of everything in the model state_dict (parameters and buffers).
V2 of this module is simpler, it does not match params/buffers based on name but simply
iterates in order. It works with torchscript (JIT of full mo... | return self.module(*args, **kwargs)
| return self.module(*args, **kwargs)
| 3 | 652 | 17 | 669 | """ Exponential Moving Average (EMA) of model updates
Hacked together by / Copyright 2020 Ross Wightman
"""
import logging
from collections import OrderedDict
from copy import deepcopy
from typing import Optional
import torch
import torch.nn as nn
_logger = logging.getLogger(__name__)
class ModelEma:
""" Model... | """ Exponential Moving Average (EMA) of model updates
Hacked together by / Copyright 2020 Ross Wightman
"""
import logging
from collections import OrderedDict
from copy import deepcopy
from typing import Optional
import torch
import torch.nn as nn
_logger = logging.getLogger(__name__)
class ModelEma:
""" Model... |
17,993 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/utils/model_ema.py | 254,256 | 133,261 | set | ModelEmaV3 | 256,259 | 256,259 | class ModelEmaV3(nn.Module):
""" Model Exponential Moving Average V3
Keep a moving average of everything in the model state_dict (parameters and buffers).
V3 of this module leverages for_each and in-place operations for faster performance.
Decay warmup based on code by @crowsonkb, her comments:
... | class ModelEmaV3(nn.Module):
""" Model Exponential Moving Average V3
Keep a moving average of everything in the model state_dict (parameters and buffers).
V3 of this module leverages for_each and in-place operations for faster performance.
Decay warmup based on code by @crowsonkb, her comments:
... | for ema_v, model_v in zip(self.module.state_dict().values(), model.state_dict().values()):
ema_v.copy_(model_v.to(device=self.device))
| for ema_v, model_v in zip(self.module.state_dict().values(), model.state_dict().values()):
ema_v.copy_(model_v.to(device=self.device))
| 7 | 1,620 | 58 | 1,678 | """ Exponential Moving Average (EMA) of model updates
Hacked together by / Copyright 2020 Ross Wightman
"""
import logging
from collections import OrderedDict
from copy import deepcopy
from typing import Optional
import torch
import torch.nn as nn
_logger = logging.getLogger(__name__)
class ModelEma:
""" Model... | """ Exponential Moving Average (EMA) of model updates
Hacked together by / Copyright 2020 Ross Wightman
"""
import logging
from collections import OrderedDict
from copy import deepcopy
from typing import Optional
import torch
import torch.nn as nn
_logger = logging.getLogger(__name__)
class ModelEma:
""" Model... |
17,994 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/utils/random.py | 6,7 | 1,10 | random_seed | 7,10 | 7,10 | import random
import numpy as np
import torch
def random_seed(seed=42, rank=0):
FILL_FUNC_BODY | import random
import numpy as np
import torch
def random_seed(seed=42, rank=0):
FILL_FUNC_BODY | torch.manual_seed(seed + rank)
np.random.seed(seed + rank)
random.seed(seed + rank)
| torch.manual_seed(seed + rank)
np.random.seed(seed + rank)
random.seed(seed + rank)
| 9 | 40 | 39 | 79 | import random
import numpy as np
import torch
def random_seed(seed=42, rank=0):
FILL_FUNC_BODY | import random
import numpy as np
import torch
def random_seed(seed=42, rank=0):
FILL_FUNC_BODY | |
17,995 | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models | /home/hieuvd/dung_repo/raw/stars_01/huggingface_pytorch-image-models/pytorch-image-models/timm/utils/summary.py | 30,39 | 1,52 | update_summary | 39,52 | 39,52 | """ Summary utilities
Hacked together by / Copyright 2020 Ross Wightman
"""
import csv
import os
from collections import OrderedDict
try:
import wandb
except ImportError:
pass
def get_outdir(path, *paths, inc=False):
outdir = os.path.join(path, *paths)
if not os.path.exists(outdir):
os.maked... | """ Summary utilities
Hacked together by / Copyright 2020 Ross Wightman
"""
import csv
import os
from collections import OrderedDict
try:
import wandb
except ImportError:
pass
def get_outdir(path, *paths, inc=False):
outdir = os.path.join(path, *paths)
if not os.path.exists(outdir):
os.maked... | rowd = OrderedDict(epoch=epoch)
rowd.update([('train_' + k, v) for k, v in train_metrics.items()])
if eval_metrics:
rowd.update([('eval_' + k, v) for k, v in eval_metrics.items()])
if lr is not None:
rowd['lr'] = lr
if log_wandb:
wandb.log(rowd)
with open(filename, mode='... | rowd = OrderedDict(epoch=epoch)
rowd.update([('train_' + k, v) for k, v in train_metrics.items()])
if eval_metrics:
rowd.update([('eval_' + k, v) for k, v in eval_metrics.items()])
if lr is not None:
rowd['lr'] = lr
if log_wandb:
wandb.log(rowd)
with open(filename, mode='... | 56 | 281 | 211 | 492 | """ Summary utilities
Hacked together by / Copyright 2020 Ross Wightman
"""
import csv
import os
from collections import OrderedDict
try:
import wandb
except ImportError:
pass
def get_outdir(path, *paths, inc=False):
outdir = os.path.join(path, *paths)
if not os.path.exists(outdir):
os.maked... | """ Summary utilities
Hacked together by / Copyright 2020 Ross Wightman
"""
import csv
import os
from collections import OrderedDict
try:
import wandb
except ImportError:
pass
def get_outdir(path, *paths, inc=False):
outdir = os.path.join(path, *paths)
if not os.path.exists(outdir):
os.maked... | |
18,015 | /home/hieuvd/dung_repo/raw/stars_01/idank_explainshell/explainshell | /home/hieuvd/dung_repo/raw/stars_01/idank_explainshell/explainshell/explainshell/algo/features.py | 3,4 | 1,64 | extract_first_line | 14,26 | 4,26 | import re
def extract_first_line(paragraph):
FILL_FUNC_BODYdef starts_with_hyphen(paragraph):
return paragraph.lstrip()[0] == '-'
def is_indented(paragraph):
return paragraph != paragraph.lstrip()
def par_length(paragraph):
return round(len(paragraph.strip()), -1) / 2
def first_line_contains(paragraph, ... | import re
def extract_first_line(paragraph):
'''
>>> extract_first_line('a b cd')
'a b'
>>> extract_first_line('a b cd')
'a b cd'
>>> extract_first_line(' a b cd')
'a b cd'
>>> extract_first_line(' a b cd')
'a b'
'''
FILL_FUNC_BODYdef starts_with_hyphen(paragraph):
retu... | lines = paragraph.splitlines()
first = lines[0].strip()
spaces = list(re.finditer(r'(\s+)', first))
# handle options that have their description in the first line by trying
# to treat it as two lines (looking at spaces between option and the rest
# of the text)
if spaces:
longest = m... | '''
>>> extract_first_line('a b cd')
'a b'
>>> extract_first_line('a b cd')
'a b cd'
>>> extract_first_line(' a b cd')
'a b cd'
>>> extract_first_line(' a b cd')
'a b'
'''
lines = paragraph.splitlines()
first = lines[0].strip()
spaces = list(re.finditer(r'(\s+)',... | 69 | 431 | 166 | 597 | import re
def extract_first_line(paragraph):
FILL_FUNC_BODYdef starts_with_hyphen(paragraph):
return paragraph.lstrip()[0] == '-'
def is_indented(paragraph):
return paragraph != paragraph.lstrip()
def par_length(paragraph):
return round(len(paragraph.strip()), -1) / 2
def first_line_contains(paragraph, ... | import re
def extract_first_line(paragraph):
'''
>>> extract_first_line('a b cd')
'a b'
>>> extract_first_line('a b cd')
'a b cd'
>>> extract_first_line(' a b cd')
'a b cd'
>>> extract_first_line(' a b cd')
'a b'
'''
FILL_FUNC_BODYdef starts_with_hyphen(paragraph):
retu... | |
18,360 | /home/hieuvd/dung_repo/raw/stars_01/jofpin_trape/trape | /home/hieuvd/dung_repo/raw/stars_01/jofpin_trape/trape/core/colorama/ansi.py | 21,22 | 1,103 | clear_line | 22,25 | 22,25 | # Copyright Jonathan Hartley 2013. BSD 3-Clause license, see LICENSE file.
'''
This module generates ANSI character codes to printing colors to terminals.
See: http://en.wikipedia.org/wiki/ANSI_escape_code
'''
CSI = '\033['
OSC = '\033]'
BEL = '\007'
def code_to_chars(code):
return CSI + str(code) + 'm'
def set... | # Copyright Jonathan Hartley 2013. BSD 3-Clause license, see LICENSE file.
'''
This module generates ANSI character codes to printing colors to terminals.
See: http://en.wikipedia.org/wiki/ANSI_escape_code
'''
CSI = '\033['
OSC = '\033]'
BEL = '\007'
def code_to_chars(code):
return CSI + str(code) + 'm'
def set... | return CSI + str(mode) + 'K'
| return CSI + str(mode) + 'K'
| 6 | 954 | 19 | 973 | # Copyright Jonathan Hartley 2013. BSD 3-Clause license, see LICENSE file.
'''
This module generates ANSI character codes to printing colors to terminals.
See: http://en.wikipedia.org/wiki/ANSI_escape_code
'''
CSI = '\033['
OSC = '\033]'
BEL = '\007'
def code_to_chars(code):
return CSI + str(code) + 'm'
def set... | # Copyright Jonathan Hartley 2013. BSD 3-Clause license, see LICENSE file.
'''
This module generates ANSI character codes to printing colors to terminals.
See: http://en.wikipedia.org/wiki/ANSI_escape_code
'''
CSI = '\033['
OSC = '\033]'
BEL = '\007'
def code_to_chars(code):
return CSI + str(code) + 'm'
def set... | |
18,361 | /home/hieuvd/dung_repo/raw/stars_01/jofpin_trape/trape | /home/hieuvd/dung_repo/raw/stars_01/jofpin_trape/trape/core/colorama/ansi.py | 39,40 | 36,49 | DOWN | AnsiCursor | 40,41 | 40,41 | class AnsiCursor(object):
def UP(self, n=1):
return CSI + str(n) + 'A'
def DOWN(self, n=1):
FILL_FUNC_BODY def FORWARD(self, n=1):
return CSI + str(n) + 'C'
def BACK(self, n=1):
return CSI + str(n) + 'D'
def POS(self, x=1, y=1):
return CSI + str(y) + ';' + str(x) + 'H'... | class AnsiCursor(object):
def UP(self, n=1):
return CSI + str(n) + 'A'
def DOWN(self, n=1):
FILL_FUNC_BODY def FORWARD(self, n=1):
return CSI + str(n) + 'C'
def BACK(self, n=1):
return CSI + str(n) + 'D'
def POS(self, x=1, y=1):
return CSI + str(y) + ';' + str(x) + 'H'... | return CSI + str(n) + 'B'
| return CSI + str(n) + 'B'
| 6 | 149 | 15 | 164 | # Copyright Jonathan Hartley 2013. BSD 3-Clause license, see LICENSE file.
'''
This module generates ANSI character codes to printing colors to terminals.
See: http://en.wikipedia.org/wiki/ANSI_escape_code
'''
CSI = '\033['
OSC = '\033]'
BEL = '\007'
def code_to_chars(code):
return CSI + str(code) + 'm'
def set... | # Copyright Jonathan Hartley 2013. BSD 3-Clause license, see LICENSE file.
'''
This module generates ANSI character codes to printing colors to terminals.
See: http://en.wikipedia.org/wiki/ANSI_escape_code
'''
CSI = '\033['
OSC = '\033]'
BEL = '\007'
def code_to_chars(code):
return CSI + str(code) + 'm'
def set... |
18,364 | /home/hieuvd/dung_repo/raw/stars_01/jofpin_trape/trape | /home/hieuvd/dung_repo/raw/stars_01/jofpin_trape/trape/core/colorama/ansitowin32.py | 39,40 | 24,43 | write | StreamWrapper | 40,43 | 40,43 | class StreamWrapper(object):
'''
Wraps a stream (such as stdout), acting as a transparent proxy for all
attribute access apart from method 'write()', which is delegated to our
Converter instance.
'''
def __init__(self, wrapped, converter):
# double-underscore everything to prevent clashe... | class StreamWrapper(object):
'''
Wraps a stream (such as stdout), acting as a transparent proxy for all
attribute access apart from method 'write()', which is delegated to our
Converter instance.
'''
def __init__(self, wrapped, converter):
# double-underscore everything to prevent clashe... | self.__convertor.write(text)
| self.__convertor.write(text)
| 1 | 154 | 16 | 170 | # Copyright Jonathan Hartley 2013. BSD 3-Clause license, see LICENSE file.
import re
import sys
import os
from .ansi import AnsiFore, AnsiBack, AnsiStyle, Style
from .winterm import WinTerm, WinColor, WinStyle
from .win32 import windll, winapi_test
winterm = None
if windll is not None:
winterm = WinTerm()
def ... | # Copyright Jonathan Hartley 2013. BSD 3-Clause license, see LICENSE file.
import re
import sys
import os
from .ansi import AnsiFore, AnsiBack, AnsiStyle, Style
from .winterm import WinTerm, WinColor, WinStyle
from .win32 import windll, winapi_test
winterm = None
if windll is not None:
winterm = WinTerm()
def ... |
18,365 | /home/hieuvd/dung_repo/raw/stars_01/jofpin_trape/trape | /home/hieuvd/dung_repo/raw/stars_01/jofpin_trape/trape/core/colorama/initialise.py | 74,75 | 1,83 | wrap_stream | 75,83 | 75,83 | # Copyright Jonathan Hartley 2013. BSD 3-Clause license, see LICENSE file.
import atexit
import contextlib
import sys
from .ansitowin32 import AnsiToWin32
orig_stdout = None
orig_stderr = None
wrapped_stdout = None
wrapped_stderr = None
atexit_done = False
def reset_all():
if AnsiToWin32 is not None: # Is... | # Copyright Jonathan Hartley 2013. BSD 3-Clause license, see LICENSE file.
import atexit
import contextlib
import sys
from .ansitowin32 import AnsiToWin32
orig_stdout = None
orig_stderr = None
wrapped_stdout = None
wrapped_stderr = None
atexit_done = False
def reset_all():
if AnsiToWin32 is not None: # Is... | if wrap:
wrapper = AnsiToWin32(stream,
convert=convert, strip=strip, autoreset=autoreset)
if wrapper.should_wrap():
stream = wrapper.stream
return stream
| if wrap:
wrapper = AnsiToWin32(stream,
convert=convert, strip=strip, autoreset=autoreset)
if wrapper.should_wrap():
stream = wrapper.stream
return stream
| 15 | 583 | 66 | 649 | # Copyright Jonathan Hartley 2013. BSD 3-Clause license, see LICENSE file.
import atexit
import contextlib
import sys
from .ansitowin32 import AnsiToWin32
orig_stdout = None
orig_stderr = None
wrapped_stdout = None
wrapped_stderr = None
atexit_done = False
def reset_all():
if AnsiToWin32 is not None: # Is... | # Copyright Jonathan Hartley 2013. BSD 3-Clause license, see LICENSE file.
import atexit
import contextlib
import sys
from .ansitowin32 import AnsiToWin32
orig_stdout = None
orig_stderr = None
wrapped_stdout = None
wrapped_stderr = None
atexit_done = False
def reset_all():
if AnsiToWin32 is not None: # Is... | |
18,367 | /home/hieuvd/dung_repo/raw/stars_01/jofpin_trape/trape | /home/hieuvd/dung_repo/raw/stars_01/jofpin_trape/trape/core/user_objects.py | 67,68 | 1,112 | victim_headers | 68,78 | 68,78 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# **
#
#########
# trape #
#########
#
# trape depends of this file
# For full copyright information this visit: https://github.com/jofpin/trape
#
# Copyright 2018 by Jose Pino (@jofpin) / <jofpin@gmail.com>
# **
class victim(object):
def __init__(self, vId, ip, device,... | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# **
#
#########
# trape #
#########
#
# trape depends of this file
# For full copyright information this visit: https://github.com/jofpin/trape
#
# Copyright 2018 by Jose Pino (@jofpin) / <jofpin@gmail.com>
# **
class victim(object):
def __init__(self, vId, ip, device,... | return [("User-Agent", ua),
("Content-Type", "text/html; charset=utf-8"),
("Accept", "text/html, application/xml;q=0.9, application/xhtml+xml, image/png, image/webp, image/jpeg, image/gif, image/x-xbitmap, */*;q=0.8"),
("Connection", "keep-alive"),
# Do Not Track (inf... | return [("User-Agent", ua),
("Content-Type", "text/html; charset=utf-8"),
("Accept", "text/html, application/xml;q=0.9, application/xhtml+xml, image/png, image/webp, image/jpeg, image/gif, image/x-xbitmap, */*;q=0.8"),
("Connection", "keep-alive"),
# Do Not Track (inf... | 30 | 1,418 | 166 | 1,584 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# **
#
#########
# trape #
#########
#
# trape depends of this file
# For full copyright information this visit: https://github.com/jofpin/trape
#
# Copyright 2018 by Jose Pino (@jofpin) / <jofpin@gmail.com>
# **
class victim(object):
def __init__(self, vId, ip, device,... | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# **
#
#########
# trape #
#########
#
# trape depends of this file
# For full copyright information this visit: https://github.com/jofpin/trape
#
# Copyright 2018 by Jose Pino (@jofpin) / <jofpin@gmail.com>
# **
class victim(object):
def __init__(self, vId, ip, device,... | |
18,385 | /home/hieuvd/dung_repo/raw/stars_01/jupyterhub_jupyterhub/jupyterhub | /home/hieuvd/dung_repo/raw/stars_01/jupyterhub_jupyterhub/jupyterhub/examples/custom-scopes/grades.py | 18,19 | 1,133 | require_scope | 26,43 | 19,43 | import os
from functools import wraps
from html import escape
from urllib.parse import urlparse
from tornado.httpserver import HTTPServer
from tornado.ioloop import IOLoop
from tornado.web import Application, RequestHandler, authenticated
from jupyterhub.services.auth import HubOAuthCallbackHandler, HubOAuthenticated... | import os
from functools import wraps
from html import escape
from urllib.parse import urlparse
from tornado.httpserver import HTTPServer
from tornado.ioloop import IOLoop
from tornado.web import Application, RequestHandler, authenticated
from jupyterhub.services.auth import HubOAuthCallbackHandler, HubOAuthenticated... | if isinstance(scopes, str):
scopes = [scopes]
def wrap(method):
"""The actual decorator"""
@wraps(method)
@authenticated
def wrapped(self, *args, **kwargs):
self.hub_scopes = scopes
return method(self, *args, **kwargs)
return wrapped
... | """Decorator to require scopes
For use if multiple methods on one Handler
may want different scopes,
so class-level .hub_scopes is insufficient
(e.g. read for GET, write for POST).
"""
if isinstance(scopes, str):
scopes = [scopes]
def wrap(method):
"""The actual decorat... | 28 | 1,127 | 118 | 1,245 | import os
from functools import wraps
from html import escape
from urllib.parse import urlparse
from tornado.httpserver import HTTPServer
from tornado.ioloop import IOLoop
from tornado.web import Application, RequestHandler, authenticated
from jupyterhub.services.auth import HubOAuthCallbackHandler, HubOAuthenticated... | import os
from functools import wraps
from html import escape
from urllib.parse import urlparse
from tornado.httpserver import HTTPServer
from tornado.ioloop import IOLoop
from tornado.web import Application, RequestHandler, authenticated
from jupyterhub.services.auth import HubOAuthCallbackHandler, HubOAuthenticated... | |
18,386 | /home/hieuvd/dung_repo/raw/stars_01/jupyterhub_jupyterhub/jupyterhub | /home/hieuvd/dung_repo/raw/stars_01/jupyterhub_jupyterhub/jupyterhub/examples/custom-scopes/grades.py | 65,66 | 61,105 | _render | GradesHandler | 66,92 | 66,92 | class GradesHandler(HubOAuthenticated, RequestHandler):
# default scope for this Handler: read-only
hub_scopes = [READ_SCOPE]
def _render(self):
FILL_FUNC_BODY @require_scope([READ_SCOPE])
async def get(self):
self._render()
# POST requires WRITE_SCOPE instead of READ_SCOPE
@require... | class GradesHandler(HubOAuthenticated, RequestHandler):
# default scope for this Handler: read-only
hub_scopes = [READ_SCOPE]
def _render(self):
FILL_FUNC_BODY @require_scope([READ_SCOPE])
async def get(self):
self._render()
# POST requires WRITE_SCOPE instead of READ_SCOPE
@require... | grades = self.settings["grades"]
self.write("<h1>All grades</h1>")
self.write("<table>")
self.write("<tr><th>Student</th><th>Grade</th></tr>")
for student, grade in grades.items():
qstudent = escape(student)
qgrade = escape(str(grade))
self.wri... | grades = self.settings["grades"]
self.write("<h1>All grades</h1>")
self.write("<table>")
self.write("<tr><th>Student</th><th>Grade</th></tr>")
for student, grade in grades.items():
qstudent = escape(student)
qgrade = escape(str(grade))
self.wri... | 51 | 175 | 291 | 466 | import os
from functools import wraps
from html import escape
from urllib.parse import urlparse
from tornado.httpserver import HTTPServer
from tornado.ioloop import IOLoop
from tornado.web import Application, RequestHandler, authenticated
from jupyterhub.services.auth import HubOAuthCallbackHandler, HubOAuthenticated... | import os
from functools import wraps
from html import escape
from urllib.parse import urlparse
from tornado.httpserver import HTTPServer
from tornado.ioloop import IOLoop
from tornado.web import Application, RequestHandler, authenticated
from jupyterhub.services.auth import HubOAuthCallbackHandler, HubOAuthenticated... |
18,387 | /home/hieuvd/dung_repo/raw/stars_01/jupyterhub_jupyterhub/jupyterhub | /home/hieuvd/dung_repo/raw/stars_01/jupyterhub_jupyterhub/jupyterhub/examples/custom-scopes/grades.py | 47,49 | 43,61 | get | MyGradesHandler | 49,61 | 49,61 | class MyGradesHandler(HubOAuthenticated, RequestHandler):
# no hub_scopes, anyone with access to this service
# will be able to visit this URL
@authenticated
def get(self):
FILL_FUNC_BODY | class MyGradesHandler(HubOAuthenticated, RequestHandler):
# no hub_scopes, anyone with access to this service
# will be able to visit this URL
@authenticated
def get(self):
FILL_FUNC_BODY | self.write("<h1>My grade</h1>")
name = self.current_user["name"]
grades = self.settings["grades"]
self.write(f"<p>My name is: {escape(name)}</p>")
if name in grades:
self.write(f"<p>My grade is: {escape(str(grades[name]))}</p>")
else:
self.write("<... | self.write("<h1>My grade</h1>")
name = self.current_user["name"]
grades = self.settings["grades"]
self.write(f"<p>My name is: {escape(name)}</p>")
if name in grades:
self.write(f"<p>My grade is: {escape(str(grades[name]))}</p>")
else:
self.write("<... | 31 | 65 | 177 | 242 | import os
from functools import wraps
from html import escape
from urllib.parse import urlparse
from tornado.httpserver import HTTPServer
from tornado.ioloop import IOLoop
from tornado.web import Application, RequestHandler, authenticated
from jupyterhub.services.auth import HubOAuthCallbackHandler, HubOAuthenticated... | import os
from functools import wraps
from html import escape
from urllib.parse import urlparse
from tornado.httpserver import HTTPServer
from tornado.ioloop import IOLoop
from tornado.web import Application, RequestHandler, authenticated
from jupyterhub.services.auth import HubOAuthCallbackHandler, HubOAuthenticated... |
18,388 | /home/hieuvd/dung_repo/raw/stars_01/jupyterhub_jupyterhub/jupyterhub | /home/hieuvd/dung_repo/raw/stars_01/jupyterhub_jupyterhub/jupyterhub/examples/external-oauth/whoami-oauth-basic.py | 100,101 | 1,132 | main | 101,130 | 101,130 | """Basic implementation of OAuth without any inheritance
Implements OAuth handshake manually
so all URLs and requests necessary for OAuth with JupyterHub should be in one place
"""
import json
import os
from urllib.parse import urlencode, urlparse
from tornado import log, web
from tornado.httpclient import AsyncHTTP... | """Basic implementation of OAuth without any inheritance
Implements OAuth handshake manually
so all URLs and requests necessary for OAuth with JupyterHub should be in one place
"""
import json
import os
from urllib.parse import urlencode, urlparse
from tornado import log, web
from tornado.httpclient import AsyncHTTP... | log.enable_pretty_logging()
# construct OAuth URLs from jupyterhub base URL
hub_api = os.environ['JUPYTERHUB_URL'].rstrip('/') + '/hub/api'
authorize_url = hub_api + '/oauth2/authorize'
token_url = hub_api + '/oauth2/token'
user_url = hub_api + '/user'
app = web.Application(
[('/oa... | log.enable_pretty_logging()
# construct OAuth URLs from jupyterhub base URL
hub_api = os.environ['JUPYTERHUB_URL'].rstrip('/') + '/hub/api'
authorize_url = hub_api + '/oauth2/authorize'
token_url = hub_api + '/oauth2/token'
user_url = hub_api + '/user'
app = web.Application(
[('/oa... | 62 | 971 | 411 | 1,382 | """Basic implementation of OAuth without any inheritance
Implements OAuth handshake manually
so all URLs and requests necessary for OAuth with JupyterHub should be in one place
"""
import json
import os
from urllib.parse import urlencode, urlparse
from tornado import log, web
from tornado.httpclient import AsyncHTTP... | """Basic implementation of OAuth without any inheritance
Implements OAuth handshake manually
so all URLs and requests necessary for OAuth with JupyterHub should be in one place
"""
import json
import os
from urllib.parse import urlencode, urlparse
from tornado import log, web
from tornado.httpclient import AsyncHTTP... | |
18,389 | /home/hieuvd/dung_repo/raw/stars_01/jupyterhub_jupyterhub/jupyterhub | /home/hieuvd/dung_repo/raw/stars_01/jupyterhub_jupyterhub/jupyterhub/examples/external-oauth/whoami-oauth-basic.py | 42,43 | 17,67 | get | JupyterHubLoginHandler | 43,67 | 43,67 | class JupyterHubLoginHandler(web.RequestHandler):
"""Login Handler
this handler both begins and ends the OAuth process
"""
async def token_for_code(self, code):
"""Complete OAuth by requesting an access token for an oauth code"""
params = dict(
client_id=self.settings['clie... | class JupyterHubLoginHandler(web.RequestHandler):
"""Login Handler
this handler both begins and ends the OAuth process
"""
async def token_for_code(self, code):
"""Complete OAuth by requesting an access token for an oauth code"""
params = dict(
client_id=self.settings['clie... | code = self.get_argument('code', None)
if code:
# code is set, we are the oauth callback
# complete oauth
token = await self.token_for_code(code)
# login successful, set cookie and redirect back to home
self.set_secure_cookie('whoami-oauth-toke... | code = self.get_argument('code', None)
if code:
# code is set, we are the oauth callback
# complete oauth
token = await self.token_for_code(code)
# login successful, set cookie and redirect back to home
self.set_secure_cookie('whoami-oauth-toke... | 65 | 266 | 230 | 496 | """Basic implementation of OAuth without any inheritance
Implements OAuth handshake manually
so all URLs and requests necessary for OAuth with JupyterHub should be in one place
"""
import json
import os
from urllib.parse import urlencode, urlparse
from tornado import log, web
from tornado.httpclient import AsyncHTTP... | """Basic implementation of OAuth without any inheritance
Implements OAuth handshake manually
so all URLs and requests necessary for OAuth with JupyterHub should be in one place
"""
import json
import os
from urllib.parse import urlencode, urlparse
from tornado import log, web
from tornado.httpclient import AsyncHTTP... |
18,390 | /home/hieuvd/dung_repo/raw/stars_01/jupyterhub_jupyterhub/jupyterhub | /home/hieuvd/dung_repo/raw/stars_01/jupyterhub_jupyterhub/jupyterhub/examples/external-oauth/whoami-oauth-basic.py | 83,84 | 67,100 | user_for_token | WhoAmIHandler | 85,92 | 84,92 | class WhoAmIHandler(web.RequestHandler):
"""Serve the JSON model for the authenticated user"""
def get_current_user(self):
"""The login handler stored a JupyterHub API token in a cookie
@web.authenticated calls this method.
If a Falsy value is returned, the request is redirected to `lo... | class WhoAmIHandler(web.RequestHandler):
"""Serve the JSON model for the authenticated user"""
def get_current_user(self):
"""The login handler stored a JupyterHub API token in a cookie
@web.authenticated calls this method.
If a Falsy value is returned, the request is redirected to `lo... |
req = HTTPRequest(
self.settings['user_url'], headers={'Authorization': f'token {token}'}
)
response = await AsyncHTTPClient().fetch(req)
return json.loads(response.body.decode('utf8', 'replace'))
| """Retrieve the user for a given token, via /hub/api/user"""
req = HTTPRequest(
self.settings['user_url'], headers={'Authorization': f'token {token}'}
)
response = await AsyncHTTPClient().fetch(req)
return json.loads(response.body.decode('utf8', 'replace'))
| 15 | 305 | 76 | 381 | """Basic implementation of OAuth without any inheritance
Implements OAuth handshake manually
so all URLs and requests necessary for OAuth with JupyterHub should be in one place
"""
import json
import os
from urllib.parse import urlencode, urlparse
from tornado import log, web
from tornado.httpclient import AsyncHTTP... | """Basic implementation of OAuth without any inheritance
Implements OAuth handshake manually
so all URLs and requests necessary for OAuth with JupyterHub should be in one place
"""
import json
import os
from urllib.parse import urlencode, urlparse
from tornado import log, web
from tornado.httpclient import AsyncHTTP... |
18,391 | /home/hieuvd/dung_repo/raw/stars_01/jupyterhub_jupyterhub/jupyterhub | /home/hieuvd/dung_repo/raw/stars_01/jupyterhub_jupyterhub/jupyterhub/examples/server-api/start-stop-server.py | 114,115 | 1,174 | stop_server | 119,154 | 115,154 | #!/usr/bin/env python3
"""
Example of starting/stopping a server via the JupyterHub API
1. get user status
2. start server
3. wait for server to be ready via progress api
4. make a request to the server itself
5. stop server via API
6. wait for server to finish stopping
"""
import json
import logging
import pathlib
i... | #!/usr/bin/env python3
"""
Example of starting/stopping a server via the JupyterHub API
1. get user status
2. start server
3. wait for server to be ready via progress api
4. make a request to the server itself
5. stop server via API
6. wait for server to finish stopping
"""
import json
import logging
import pathlib
i... | # step 1: get user status
user_url = f"{hub_url}/hub/api/users/{user}"
server_url = f"{user_url}/servers/{server_name}"
log_name = f"{user}/{server_name}".rstrip("/")
log.info(f"Stopping server {log_name}")
r = session.delete(server_url)
if r.status_code == 404:
log.info(f"Server {l... | """Stop a server via the JupyterHub API
Returns when the server has finished stopping
"""
# step 1: get user status
user_url = f"{hub_url}/hub/api/users/{user}"
server_url = f"{user_url}/servers/{server_name}"
log_name = f"{user}/{server_name}".rstrip("/")
log.info(f"Stopping server {l... | 106 | 1,305 | 424 | 1,729 | #!/usr/bin/env python3
"""
Example of starting/stopping a server via the JupyterHub API
1. get user status
2. start server
3. wait for server to be ready via progress api
4. make a request to the server itself
5. stop server via API
6. wait for server to finish stopping
"""
import json
import logging
import pathlib
i... | #!/usr/bin/env python3
"""
Example of starting/stopping a server via the JupyterHub API
1. get user status
2. start server
3. wait for server to be ready via progress api
4. make a request to the server itself
5. stop server via API
6. wait for server to finish stopping
"""
import json
import logging
import pathlib
i... | |
18,392 | /home/hieuvd/dung_repo/raw/stars_01/jupyterhub_jupyterhub/jupyterhub | /home/hieuvd/dung_repo/raw/stars_01/jupyterhub_jupyterhub/jupyterhub/examples/service-announcement/announcement.py | 51,52 | 1,72 | parse_arguments | 52,65 | 52,65 | import argparse
import datetime
import json
import os
from tornado import escape, ioloop, web
from jupyterhub.services.auth import HubAuthenticated
class AnnouncementRequestHandler(HubAuthenticated, web.RequestHandler):
"""Dynamically manage page announcements"""
def initialize(self, storage):
"""C... | import argparse
import datetime
import json
import os
from tornado import escape, ioloop, web
from jupyterhub.services.auth import HubAuthenticated
class AnnouncementRequestHandler(HubAuthenticated, web.RequestHandler):
"""Dynamically manage page announcements"""
def initialize(self, storage):
"""C... | parser = argparse.ArgumentParser()
parser.add_argument(
"--api-prefix",
"-a",
default=os.environ.get("JUPYTERHUB_SERVICE_PREFIX", "/"),
help="application API prefix",
)
parser.add_argument(
"--port", "-p", default=8888, help="port for API to listen on", type=int
... | parser = argparse.ArgumentParser()
parser.add_argument(
"--api-prefix",
"-a",
default=os.environ.get("JUPYTERHUB_SERVICE_PREFIX", "/"),
help="application API prefix",
)
parser.add_argument(
"--port", "-p", default=8888, help="port for API to listen on", type=int
... | 26 | 505 | 133 | 638 | import argparse
import datetime
import json
import os
from tornado import escape, ioloop, web
from jupyterhub.services.auth import HubAuthenticated
class AnnouncementRequestHandler(HubAuthenticated, web.RequestHandler):
"""Dynamically manage page announcements"""
def initialize(self, storage):
"""C... | import argparse
import datetime
import json
import os
from tornado import escape, ioloop, web
from jupyterhub.services.auth import HubAuthenticated
class AnnouncementRequestHandler(HubAuthenticated, web.RequestHandler):
"""Dynamically manage page announcements"""
def initialize(self, storage):
"""C... | |
18,393 | /home/hieuvd/dung_repo/raw/stars_01/jupyterhub_jupyterhub/jupyterhub | /home/hieuvd/dung_repo/raw/stars_01/jupyterhub_jupyterhub/jupyterhub/examples/service-announcement/announcement.py | 32,34 | 11,44 | delete | AnnouncementRequestHandler | 35,38 | 34,38 | class AnnouncementRequestHandler(HubAuthenticated, web.RequestHandler):
"""Dynamically manage page announcements"""
def initialize(self, storage):
"""Create storage for announcement text"""
self.storage = storage
@web.authenticated
def post(self):
"""Update announcement"""
... | class AnnouncementRequestHandler(HubAuthenticated, web.RequestHandler):
"""Dynamically manage page announcements"""
def initialize(self, storage):
"""Create storage for announcement text"""
self.storage = storage
@web.authenticated
def post(self):
"""Update announcement"""
... | self.storage["announcement"] = ""
self.write_to_json(self.storage)
| """Clear announcement"""
self.storage["announcement"] = ""
self.write_to_json(self.storage)
| 4 | 293 | 30 | 323 | import argparse
import datetime
import json
import os
from tornado import escape, ioloop, web
from jupyterhub.services.auth import HubAuthenticated
class AnnouncementRequestHandler(HubAuthenticated, web.RequestHandler):
"""Dynamically manage page announcements"""
def initialize(self, storage):
"""C... | import argparse
import datetime
import json
import os
from tornado import escape, ioloop, web
from jupyterhub.services.auth import HubAuthenticated
class AnnouncementRequestHandler(HubAuthenticated, web.RequestHandler):
"""Dynamically manage page announcements"""
def initialize(self, storage):
"""C... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.