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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
17,752 | /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/mobilevit.py | 505,506 | 449,543 | forward | MobileVitV2Block | 506,543 | 506,543 | @register_notrace_module
class MobileVitV2Block(nn.Module):
"""
This class defines the `MobileViTv2 block <>`_
"""
def __init__(
self,
in_chs: int,
out_chs: Optional[int] = None,
kernel_size: int = 3,
bottle_ratio: float = 1.0,
group_size: Optional[int] =... | @register_notrace_module
class MobileVitV2Block(nn.Module):
"""
This class defines the `MobileViTv2 block <>`_
"""
def __init__(
self,
in_chs: int,
out_chs: Optional[int] = None,
kernel_size: int = 3,
bottle_ratio: float = 1.0,
group_size: Optional[int] =... | B, C, H, W = x.shape
patch_h, patch_w = self.patch_size
new_h, new_w = math.ceil(H / patch_h) * patch_h, math.ceil(W / patch_w) * patch_w
num_patch_h, num_patch_w = new_h // patch_h, new_w // patch_w # n_h, n_w
num_patches = num_patch_h * num_patch_w # N
if new_h != H o... | B, C, H, W = x.shape
patch_h, patch_w = self.patch_size
new_h, new_w = math.ceil(H / patch_h) * patch_h, math.ceil(W / patch_w) * patch_w
num_patch_h, num_patch_w = new_h // patch_h, new_w // patch_w # n_h, n_w
num_patches = num_patch_h * num_patch_w # N
if new_h != H o... | 186 | 697 | 705 | 1,402 | """ MobileViT
Paper:
V1: `MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer` - https://arxiv.org/abs/2110.02178
V2: `Separable Self-attention for Mobile Vision Transformers` - https://arxiv.org/abs/2206.02680
MobileVitBlock and checkpoints adapted from https://github.com/apple/ml-cvnets... | """ MobileViT
Paper:
V1: `MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer` - https://arxiv.org/abs/2110.02178
V2: `Separable Self-attention for Mobile Vision Transformers` - https://arxiv.org/abs/2206.02680
MobileVitBlock and checkpoints adapted from https://github.com/apple/ml-cvnets... |
17,754 | /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/mvitv2.py | 467,468 | 368,521 | forward | MultiScaleAttention | 468,521 | 468,521 | class MultiScaleAttention(nn.Module):
def __init__(
self,
dim,
dim_out,
feat_size,
num_heads=8,
qkv_bias=True,
mode="conv",
kernel_q=(1, 1),
kernel_kv=(1, 1),
stride_q=(1, 1),
stride_k... | class MultiScaleAttention(nn.Module):
def __init__(
self,
dim,
dim_out,
feat_size,
num_heads=8,
qkv_bias=True,
mode="conv",
kernel_q=(1, 1),
kernel_kv=(1, 1),
stride_q=(1, 1),
stride_k... | B, N, _ = x.shape
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
q, k, v = qkv.unbind(dim=0)
if self.pool_q is not None:
q, q_tok = reshape_pre_pool(q, feat_size, self.has_cls_token)
q = self.pool_q(q)
q, q_size = resha... | B, N, _ = x.shape
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
q, k, v = qkv.unbind(dim=0)
if self.pool_q is not None:
q, q_tok = reshape_pre_pool(q, feat_size, self.has_cls_token)
q = self.pool_q(q)
q, q_size = resha... | 163 | 1,274 | 683 | 1,957 | """ Multi-Scale Vision Transformer v2
@inproceedings{li2021improved,
title={MViTv2: Improved multiscale vision transformers for classification and detection},
author={Li, Yanghao and Wu, Chao-Yuan and Fan, Haoqi and Mangalam, Karttikeya and Xiong, Bo and Malik, Jitendra and Feichtenhofer, Christoph},
booktitle={... | """ Multi-Scale Vision Transformer v2
@inproceedings{li2021improved,
title={MViTv2: Improved multiscale vision transformers for classification and detection},
author={Li, Yanghao and Wu, Chao-Yuan and Fan, Haoqi and Mangalam, Karttikeya and Xiong, Bo and Malik, Jitendra and Feichtenhofer, Christoph},
booktitle={... |
17,756 | /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/mvitv2.py | 590,591 | 521,620 | _shortcut_pool | MultiScaleBlock | 591,606 | 591,606 | class MultiScaleBlock(nn.Module):
def __init__(
self,
dim,
dim_out,
num_heads,
feat_size,
mlp_ratio=4.0,
qkv_bias=True,
drop_path=0.0,
norm_layer=nn.LayerNorm,
kernel_q=(1, 1),
kernel_... | class MultiScaleBlock(nn.Module):
def __init__(
self,
dim,
dim_out,
num_heads,
feat_size,
mlp_ratio=4.0,
qkv_bias=True,
drop_path=0.0,
norm_layer=nn.LayerNorm,
kernel_q=(1, 1),
kernel_... | if self.shortcut_pool_attn is None:
return x
if self.has_cls_token:
cls_tok, x = x[:, :1, :], x[:, 1:, :]
else:
cls_tok = None
B, L, C = x.shape
H, W = feat_size
x = x.reshape(B, H, W, C).permute(0, 3, 1, 2).contiguous()
x = sel... | if self.shortcut_pool_attn is None:
return x
if self.has_cls_token:
cls_tok, x = x[:, :1, :], x[:, 1:, :]
else:
cls_tok = None
B, L, C = x.shape
H, W = feat_size
x = x.reshape(B, H, W, C).permute(0, 3, 1, 2).contiguous()
x = sel... | 60 | 1,089 | 214 | 1,303 | """ Multi-Scale Vision Transformer v2
@inproceedings{li2021improved,
title={MViTv2: Improved multiscale vision transformers for classification and detection},
author={Li, Yanghao and Wu, Chao-Yuan and Fan, Haoqi and Mangalam, Karttikeya and Xiong, Bo and Malik, Jitendra and Feichtenhofer, Christoph},
booktitle={... | """ Multi-Scale Vision Transformer v2
@inproceedings{li2021improved,
title={MViTv2: Improved multiscale vision transformers for classification and detection},
author={Li, Yanghao and Wu, Chao-Yuan and Fan, Haoqi and Mangalam, Karttikeya and Xiong, Bo and Malik, Jitendra and Feichtenhofer, Christoph},
booktitle={... |
17,758 | /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/mvitv2.py | 681,682 | 620,690 | forward | MultiScaleVitStage | 682,690 | 682,690 | class MultiScaleVitStage(nn.Module):
def __init__(
self,
dim,
dim_out,
depth,
num_heads,
feat_size,
mlp_ratio=4.0,
qkv_bias=True,
mode="conv",
kernel_q=(1, 1),
kernel_kv=(1, 1),
... | class MultiScaleVitStage(nn.Module):
def __init__(
self,
dim,
dim_out,
depth,
num_heads,
feat_size,
mlp_ratio=4.0,
qkv_bias=True,
mode="conv",
kernel_q=(1, 1),
kernel_kv=(1, 1),
... | for blk in self.blocks:
if self.grad_checkpointing and not torch.jit.is_scripting():
x, feat_size = checkpoint.checkpoint(blk, x, feat_size)
else:
x, feat_size = blk(x, feat_size)
return x, feat_size
| for blk in self.blocks:
if self.grad_checkpointing and not torch.jit.is_scripting():
x, feat_size = checkpoint.checkpoint(blk, x, feat_size)
else:
x, feat_size = blk(x, feat_size)
return x, feat_size
| 24 | 649 | 95 | 744 | """ Multi-Scale Vision Transformer v2
@inproceedings{li2021improved,
title={MViTv2: Improved multiscale vision transformers for classification and detection},
author={Li, Yanghao and Wu, Chao-Yuan and Fan, Haoqi and Mangalam, Karttikeya and Xiong, Bo and Malik, Jitendra and Feichtenhofer, Christoph},
booktitle={... | """ Multi-Scale Vision Transformer v2
@inproceedings{li2021improved,
title={MViTv2: Improved multiscale vision transformers for classification and detection},
author={Li, Yanghao and Wu, Chao-Yuan and Fan, Haoqi and Mangalam, Karttikeya and Xiong, Bo and Malik, Jitendra and Feichtenhofer, Christoph},
booktitle={... |
17,759 | /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/mvitv2.py | 112,113 | 89,118 | forward | PatchEmbed | 113,118 | 113,118 | class PatchEmbed(nn.Module):
"""
PatchEmbed.
"""
def __init__(
self,
dim_in=3,
dim_out=768,
kernel=(7, 7),
stride=(4, 4),
padding=(3, 3),
):
super().__init__()
self.proj = nn.Conv2d(
dim_in,
... | class PatchEmbed(nn.Module):
"""
PatchEmbed.
"""
def __init__(
self,
dim_in=3,
dim_out=768,
kernel=(7, 7),
stride=(4, 4),
padding=(3, 3),
):
super().__init__()
self.proj = nn.Conv2d(
dim_in,
... | x = self.proj(x)
# B C H W -> B HW C
return x.flatten(2).transpose(1, 2), x.shape[-2:]
| x = self.proj(x)
# B C H W -> B HW C
return x.flatten(2).transpose(1, 2), x.shape[-2:]
| 16 | 186 | 54 | 240 | """ Multi-Scale Vision Transformer v2
@inproceedings{li2021improved,
title={MViTv2: Improved multiscale vision transformers for classification and detection},
author={Li, Yanghao and Wu, Chao-Yuan and Fan, Haoqi and Mangalam, Karttikeya and Xiong, Bo and Malik, Jitendra and Feichtenhofer, Christoph},
booktitle={... | """ Multi-Scale Vision Transformer v2
@inproceedings{li2021improved,
title={MViTv2: Improved multiscale vision transformers for classification and detection},
author={Li, Yanghao and Wu, Chao-Yuan and Fan, Haoqi and Mangalam, Karttikeya and Xiong, Bo and Malik, Jitendra and Feichtenhofer, Christoph},
booktitle={... |
17,761 | /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/nasnet.py | 28,29 | 19,35 | forward | ActConvBn | 29,35 | 29,35 | class ActConvBn(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=''):
super(ActConvBn, self).__init__()
self.act = nn.ReLU()
self.conv = create_conv2d(
in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding)
... | class ActConvBn(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=''):
super(ActConvBn, self).__init__()
self.act = nn.ReLU()
self.conv = create_conv2d(
in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding)
... | x = self.act(x)
x = self.conv(x)
x = self.bn(x)
return x
| x = self.act(x)
x = self.conv(x)
x = self.bn(x)
return x
| 11 | 169 | 42 | 211 | """ NasNet-A (Large)
nasnetalarge implementation grabbed from Cadene's pretrained models
https://github.com/Cadene/pretrained-models.pytorch
"""
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.layers import ConvNormAct, create_conv2d, create_pool2d, create_... | """ NasNet-A (Large)
nasnetalarge implementation grabbed from Cadene's pretrained models
https://github.com/Cadene/pretrained-models.pytorch
"""
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.layers import ConvNormAct, create_conv2d, create_pool2d, create_... |
17,762 | /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/nasnet.py | 65,66 | 51,75 | forward | BranchSeparables | 66,75 | 66,75 | class BranchSeparables(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1, pad_type='', stem_cell=False):
super(BranchSeparables, self).__init__()
middle_channels = out_channels if stem_cell else in_channels
self.act_1 = nn.ReLU()
self.separable_1 = Sepa... | class BranchSeparables(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1, pad_type='', stem_cell=False):
super(BranchSeparables, self).__init__()
middle_channels = out_channels if stem_cell else in_channels
self.act_1 = nn.ReLU()
self.separable_1 = Sepa... | x = self.act_1(x)
x = self.separable_1(x)
x = self.bn_sep_1(x)
x = self.act_2(x)
x = self.separable_2(x)
x = self.bn_sep_2(x)
return x
| x = self.act_1(x)
x = self.separable_1(x)
x = self.bn_sep_1(x)
x = self.act_2(x)
x = self.separable_2(x)
x = self.bn_sep_2(x)
return x
| 20 | 305 | 93 | 398 | """ NasNet-A (Large)
nasnetalarge implementation grabbed from Cadene's pretrained models
https://github.com/Cadene/pretrained-models.pytorch
"""
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.layers import ConvNormAct, create_conv2d, create_pool2d, create_... | """ NasNet-A (Large)
nasnetalarge implementation grabbed from Cadene's pretrained models
https://github.com/Cadene/pretrained-models.pytorch
"""
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.layers import ConvNormAct, create_conv2d, create_pool2d, create_... |
17,763 | /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/nasnet.py | 96,97 | 75,122 | forward | CellStem0 | 97,122 | 97,122 | class CellStem0(nn.Module):
def __init__(self, stem_size, num_channels=42, pad_type=''):
super(CellStem0, self).__init__()
self.num_channels = num_channels
self.stem_size = stem_size
self.conv_1x1 = ActConvBn(self.stem_size, self.num_channels, 1, stride=1)
self.comb_iter_0_l... | class CellStem0(nn.Module):
def __init__(self, stem_size, num_channels=42, pad_type=''):
super(CellStem0, self).__init__()
self.num_channels = num_channels
self.stem_size = stem_size
self.conv_1x1 = ActConvBn(self.stem_size, self.num_channels, 1, stride=1)
self.comb_iter_0_l... | x1 = self.conv_1x1(x)
x_comb_iter_0_left = self.comb_iter_0_left(x1)
x_comb_iter_0_right = self.comb_iter_0_right(x)
x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right
x_comb_iter_1_left = self.comb_iter_1_left(x1)
x_comb_iter_1_right = self.comb_iter_1_right(x)
... | x1 = self.conv_1x1(x)
x_comb_iter_0_left = self.comb_iter_0_left(x1)
x_comb_iter_0_right = self.comb_iter_0_right(x)
x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right
x_comb_iter_1_left = self.comb_iter_1_left(x1)
x_comb_iter_1_right = self.comb_iter_1_right(x)
... | 64 | 509 | 475 | 984 | """ NasNet-A (Large)
nasnetalarge implementation grabbed from Cadene's pretrained models
https://github.com/Cadene/pretrained-models.pytorch
"""
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.layers import ConvNormAct, create_conv2d, create_pool2d, create_... | """ NasNet-A (Large)
nasnetalarge implementation grabbed from Cadene's pretrained models
https://github.com/Cadene/pretrained-models.pytorch
"""
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.layers import ConvNormAct, create_conv2d, create_pool2d, create_... |
17,764 | /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/nasnet.py | 156,157 | 122,190 | forward | CellStem1 | 157,190 | 157,190 | class CellStem1(nn.Module):
def __init__(self, stem_size, num_channels, pad_type=''):
super(CellStem1, self).__init__()
self.num_channels = num_channels
self.stem_size = stem_size
self.conv_1x1 = ActConvBn(2 * self.num_channels, self.num_channels, 1, stride=1)
self.act = nn... | class CellStem1(nn.Module):
def __init__(self, stem_size, num_channels, pad_type=''):
super(CellStem1, self).__init__()
self.num_channels = num_channels
self.stem_size = stem_size
self.conv_1x1 = ActConvBn(2 * self.num_channels, self.num_channels, 1, stride=1)
self.act = nn... | x_left = self.conv_1x1(x_stem_0)
x_relu = self.act(x_conv0)
# path 1
x_path1 = self.path_1(x_relu)
# path 2
x_path2 = self.path_2(x_relu)
# final path
x_right = self.final_path_bn(torch.cat([x_path1, x_path2], 1))
x_comb_iter_0_left = self.comb_i... | x_left = self.conv_1x1(x_stem_0)
x_relu = self.act(x_conv0)
# path 1
x_path1 = self.path_1(x_relu)
# path 2
x_path2 = self.path_2(x_relu)
# final path
x_right = self.final_path_bn(torch.cat([x_path1, x_path2], 1))
x_comb_iter_0_left = self.comb_i... | 87 | 804 | 601 | 1,405 | """ NasNet-A (Large)
nasnetalarge implementation grabbed from Cadene's pretrained models
https://github.com/Cadene/pretrained-models.pytorch
"""
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.layers import ConvNormAct, create_conv2d, create_pool2d, create_... | """ NasNet-A (Large)
nasnetalarge implementation grabbed from Cadene's pretrained models
https://github.com/Cadene/pretrained-models.pytorch
"""
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.layers import ConvNormAct, create_conv2d, create_pool2d, create_... |
17,765 | /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/nasnet.py | 221,222 | 190,250 | forward | FirstCell | 222,250 | 222,250 | class FirstCell(nn.Module):
def __init__(self, in_chs_left, out_chs_left, in_chs_right, out_chs_right, pad_type=''):
super(FirstCell, self).__init__()
self.conv_1x1 = ActConvBn(in_chs_right, out_chs_right, 1, stride=1)
self.act = nn.ReLU()
self.path_1 = nn.Sequential()
self... | class FirstCell(nn.Module):
def __init__(self, in_chs_left, out_chs_left, in_chs_right, out_chs_right, pad_type=''):
super(FirstCell, self).__init__()
self.conv_1x1 = ActConvBn(in_chs_right, out_chs_right, 1, stride=1)
self.act = nn.ReLU()
self.path_1 = nn.Sequential()
self... | x_relu = self.act(x_prev)
x_path1 = self.path_1(x_relu)
x_path2 = self.path_2(x_relu)
x_left = self.final_path_bn(torch.cat([x_path1, x_path2], 1))
x_right = self.conv_1x1(x)
x_comb_iter_0_left = self.comb_iter_0_left(x_right)
x_comb_iter_0_right = self.comb_iter... | x_relu = self.act(x_prev)
x_path1 = self.path_1(x_relu)
x_path2 = self.path_2(x_relu)
x_left = self.final_path_bn(torch.cat([x_path1, x_path2], 1))
x_right = self.conv_1x1(x)
x_comb_iter_0_left = self.comb_iter_0_left(x_right)
x_comb_iter_0_right = self.comb_iter... | 77 | 759 | 540 | 1,299 | """ NasNet-A (Large)
nasnetalarge implementation grabbed from Cadene's pretrained models
https://github.com/Cadene/pretrained-models.pytorch
"""
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.layers import ConvNormAct, create_conv2d, create_pool2d, create_... | """ NasNet-A (Large)
nasnetalarge implementation grabbed from Cadene's pretrained models
https://github.com/Cadene/pretrained-models.pytorch
"""
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.layers import ConvNormAct, create_conv2d, create_pool2d, create_... |
17,767 | /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/nasnet.py | 270,271 | 250,296 | forward | NormalCell | 271,296 | 271,296 | class NormalCell(nn.Module):
def __init__(self, in_chs_left, out_chs_left, in_chs_right, out_chs_right, pad_type=''):
super(NormalCell, self).__init__()
self.conv_prev_1x1 = ActConvBn(in_chs_left, out_chs_left, 1, stride=1, padding=pad_type)
self.conv_1x1 = ActConvBn(in_chs_right, out_chs_r... | class NormalCell(nn.Module):
def __init__(self, in_chs_left, out_chs_left, in_chs_right, out_chs_right, pad_type=''):
super(NormalCell, self).__init__()
self.conv_prev_1x1 = ActConvBn(in_chs_left, out_chs_left, 1, stride=1, padding=pad_type)
self.conv_1x1 = ActConvBn(in_chs_right, out_chs_r... | x_left = self.conv_prev_1x1(x_prev)
x_right = self.conv_1x1(x)
x_comb_iter_0_left = self.comb_iter_0_left(x_right)
x_comb_iter_0_right = self.comb_iter_0_right(x_left)
x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right
x_comb_iter_1_left = self.comb_iter_1_left(x_... | x_left = self.conv_prev_1x1(x_prev)
x_right = self.conv_1x1(x)
x_comb_iter_0_left = self.comb_iter_0_left(x_right)
x_comb_iter_0_right = self.comb_iter_0_right(x_left)
x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right
x_comb_iter_1_left = self.comb_iter_1_left(x_... | 66 | 512 | 474 | 986 | """ NasNet-A (Large)
nasnetalarge implementation grabbed from Cadene's pretrained models
https://github.com/Cadene/pretrained-models.pytorch
"""
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.layers import ConvNormAct, create_conv2d, create_pool2d, create_... | """ NasNet-A (Large)
nasnetalarge implementation grabbed from Cadene's pretrained models
https://github.com/Cadene/pretrained-models.pytorch
"""
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.layers import ConvNormAct, create_conv2d, create_pool2d, create_... |
17,768 | /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/nasnet.py | 317,318 | 296,344 | forward | ReductionCell0 | 318,344 | 318,344 | class ReductionCell0(nn.Module):
def __init__(self, in_chs_left, out_chs_left, in_chs_right, out_chs_right, pad_type=''):
super(ReductionCell0, self).__init__()
self.conv_prev_1x1 = ActConvBn(in_chs_left, out_chs_left, 1, stride=1, padding=pad_type)
self.conv_1x1 = ActConvBn(in_chs_right, o... | class ReductionCell0(nn.Module):
def __init__(self, in_chs_left, out_chs_left, in_chs_right, out_chs_right, pad_type=''):
super(ReductionCell0, self).__init__()
self.conv_prev_1x1 = ActConvBn(in_chs_left, out_chs_left, 1, stride=1, padding=pad_type)
self.conv_1x1 = ActConvBn(in_chs_right, o... | x_left = self.conv_prev_1x1(x_prev)
x_right = self.conv_1x1(x)
x_comb_iter_0_left = self.comb_iter_0_left(x_right)
x_comb_iter_0_right = self.comb_iter_0_right(x_left)
x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right
x_comb_iter_1_left = self.comb_iter_1_left(x_... | x_left = self.conv_prev_1x1(x_prev)
x_right = self.conv_1x1(x)
x_comb_iter_0_left = self.comb_iter_0_left(x_right)
x_comb_iter_0_right = self.comb_iter_0_right(x_left)
x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right
x_comb_iter_1_left = self.comb_iter_1_left(x_... | 67 | 539 | 507 | 1,046 | """ NasNet-A (Large)
nasnetalarge implementation grabbed from Cadene's pretrained models
https://github.com/Cadene/pretrained-models.pytorch
"""
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.layers import ConvNormAct, create_conv2d, create_pool2d, create_... | """ NasNet-A (Large)
nasnetalarge implementation grabbed from Cadene's pretrained models
https://github.com/Cadene/pretrained-models.pytorch
"""
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.layers import ConvNormAct, create_conv2d, create_pool2d, create_... |
17,769 | /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/nasnet.py | 365,366 | 344,392 | forward | ReductionCell1 | 366,392 | 366,392 | class ReductionCell1(nn.Module):
def __init__(self, in_chs_left, out_chs_left, in_chs_right, out_chs_right, pad_type=''):
super(ReductionCell1, self).__init__()
self.conv_prev_1x1 = ActConvBn(in_chs_left, out_chs_left, 1, stride=1, padding=pad_type)
self.conv_1x1 = ActConvBn(in_chs_right, o... | class ReductionCell1(nn.Module):
def __init__(self, in_chs_left, out_chs_left, in_chs_right, out_chs_right, pad_type=''):
super(ReductionCell1, self).__init__()
self.conv_prev_1x1 = ActConvBn(in_chs_left, out_chs_left, 1, stride=1, padding=pad_type)
self.conv_1x1 = ActConvBn(in_chs_right, o... | x_left = self.conv_prev_1x1(x_prev)
x_right = self.conv_1x1(x)
x_comb_iter_0_left = self.comb_iter_0_left(x_right)
x_comb_iter_0_right = self.comb_iter_0_right(x_left)
x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right
x_comb_iter_1_left = self.comb_iter_1_left(x_... | x_left = self.conv_prev_1x1(x_prev)
x_right = self.conv_1x1(x)
x_comb_iter_0_left = self.comb_iter_0_left(x_right)
x_comb_iter_0_right = self.comb_iter_0_right(x_left)
x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right
x_comb_iter_1_left = self.comb_iter_1_left(x_... | 67 | 539 | 507 | 1,046 | """ NasNet-A (Large)
nasnetalarge implementation grabbed from Cadene's pretrained models
https://github.com/Cadene/pretrained-models.pytorch
"""
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.layers import ConvNormAct, create_conv2d, create_pool2d, create_... | """ NasNet-A (Large)
nasnetalarge implementation grabbed from Cadene's pretrained models
https://github.com/Cadene/pretrained-models.pytorch
"""
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.layers import ConvNormAct, create_conv2d, create_pool2d, create_... |
17,770 | /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/nasnet.py | 45,46 | 35,51 | forward | SeparableConv2d | 46,51 | 46,51 | class SeparableConv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride, padding=''):
super(SeparableConv2d, self).__init__()
self.depthwise_conv2d = create_conv2d(
in_channels, in_channels, kernel_size=kernel_size,
stride=stride, padding=padding,... | class SeparableConv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride, padding=''):
super(SeparableConv2d, self).__init__()
self.depthwise_conv2d = create_conv2d(
in_channels, in_channels, kernel_size=kernel_size,
stride=stride, padding=padding,... | x = self.depthwise_conv2d(x)
x = self.pointwise_conv2d(x)
return x
| x = self.depthwise_conv2d(x)
x = self.pointwise_conv2d(x)
return x
| 8 | 176 | 41 | 217 | """ NasNet-A (Large)
nasnetalarge implementation grabbed from Cadene's pretrained models
https://github.com/Cadene/pretrained-models.pytorch
"""
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.layers import ConvNormAct, create_conv2d, create_pool2d, create_... | """ NasNet-A (Large)
nasnetalarge implementation grabbed from Cadene's pretrained models
https://github.com/Cadene/pretrained-models.pytorch
"""
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.layers import ConvNormAct, create_conv2d, create_pool2d, create_... |
17,772 | /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/nest.py | 59,60 | 40,84 | forward | Attention | 63,84 | 60,84 | class Attention(nn.Module):
"""
This is much like `.vision_transformer.Attention` but uses *localised* self attention by accepting an input with
an extra "image block" dim
"""
fused_attn: torch.jit.Final[bool]
def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.):
... | class Attention(nn.Module):
"""
This is much like `.vision_transformer.Attention` but uses *localised* self attention by accepting an input with
an extra "image block" dim
"""
fused_attn: torch.jit.Final[bool]
def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.):
... | B, T, N, C = x.shape
# result of next line is (qkv, B, num (H)eads, T, N, (C')hannels per head)
qkv = self.qkv(x).reshape(B, T, N, 3, self.num_heads, C // self.num_heads).permute(3, 0, 4, 1, 2, 5)
q, k, v = qkv.unbind(0) # make torchscript happy (cannot use tensor as tuple)
if ... | """
x is shape: B (batch_size), T (image blocks), N (seq length per image block), C (embed dim)
"""
B, T, N, C = x.shape
# result of next line is (qkv, B, num (H)eads, T, N, (C')hannels per head)
qkv = self.qkv(x).reshape(B, T, N, 3, self.num_heads, C // self.num_heads).p... | 127 | 317 | 401 | 718 | """ Nested Transformer (NesT) in PyTorch
A PyTorch implement of Aggregating Nested Transformers as described in:
'Aggregating Nested Transformers'
- https://arxiv.org/abs/2105.12723
The official Jax code is released and available at https://github.com/google-research/nested-transformer. The weights
have been con... | """ Nested Transformer (NesT) in PyTorch
A PyTorch implement of Aggregating Nested Transformers as described in:
'Aggregating Nested Transformers'
- https://arxiv.org/abs/2105.12723
The official Jax code is released and available at https://github.com/google-research/nested-transformer. The weights
have been con... |
17,773 | /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/nest.py | 135,136 | 128,148 | forward | ConvPool | 139,148 | 136,148 | class ConvPool(nn.Module):
def __init__(self, in_channels, out_channels, norm_layer, pad_type=''):
super().__init__()
self.conv = create_conv2d(in_channels, out_channels, kernel_size=3, padding=pad_type, bias=True)
self.norm = norm_layer(out_channels)
self.pool = create_pool2d('max',... | class ConvPool(nn.Module):
def __init__(self, in_channels, out_channels, norm_layer, pad_type=''):
super().__init__()
self.conv = create_conv2d(in_channels, out_channels, kernel_size=3, padding=pad_type, bias=True)
self.norm = norm_layer(out_channels)
self.pool = create_pool2d('max',... | _assert(x.shape[-2] % 2 == 0, 'BlockAggregation requires even input spatial dims')
_assert(x.shape[-1] % 2 == 0, 'BlockAggregation requires even input spatial dims')
x = self.conv(x)
# Layer norm done over channel dim only
x = self.norm(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
... | """
x is expected to have shape (B, C, H, W)
"""
_assert(x.shape[-2] % 2 == 0, 'BlockAggregation requires even input spatial dims')
_assert(x.shape[-1] % 2 == 0, 'BlockAggregation requires even input spatial dims')
x = self.conv(x)
# Layer norm done over channel d... | 52 | 173 | 157 | 330 | """ Nested Transformer (NesT) in PyTorch
A PyTorch implement of Aggregating Nested Transformers as described in:
'Aggregating Nested Transformers'
- https://arxiv.org/abs/2105.12723
The official Jax code is released and available at https://github.com/google-research/nested-transformer. The weights
have been con... | """ Nested Transformer (NesT) in PyTorch
A PyTorch implement of Aggregating Nested Transformers as described in:
'Aggregating Nested Transformers'
- https://arxiv.org/abs/2105.12723
The official Jax code is released and available at https://github.com/google-research/nested-transformer. The weights
have been con... |
17,775 | /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/nest.py | 228,229 | 179,245 | forward | NestLevel | 232,245 | 229,245 | class NestLevel(nn.Module):
""" Single hierarchical level of a Nested Transformer
"""
def __init__(
self,
num_blocks,
block_size,
seq_length,
num_heads,
depth,
embed_dim,
prev_embed_dim=None,
mlp_rati... | class NestLevel(nn.Module):
""" Single hierarchical level of a Nested Transformer
"""
def __init__(
self,
num_blocks,
block_size,
seq_length,
num_heads,
depth,
embed_dim,
prev_embed_dim=None,
mlp_rati... | x = self.pool(x)
x = x.permute(0, 2, 3, 1) # (B, H', W', C), switch to channels last for transformer
x = blockify(x, self.block_size) # (B, T, N, C')
x = x + self.pos_embed
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint_seq(self.transfor... | """
expects x as (B, C, H, W)
"""
x = self.pool(x)
x = x.permute(0, 2, 3, 1) # (B, H', W', C), switch to channels last for transformer
x = blockify(x, self.block_size) # (B, T, N, C')
x = x + self.pos_embed
if self.grad_checkpointing and not torch.jit.is... | 86 | 506 | 252 | 758 | """ Nested Transformer (NesT) in PyTorch
A PyTorch implement of Aggregating Nested Transformers as described in:
'Aggregating Nested Transformers'
- https://arxiv.org/abs/2105.12723
The official Jax code is released and available at https://github.com/google-research/nested-transformer. The weights
have been con... | """ Nested Transformer (NesT) in PyTorch
A PyTorch implement of Aggregating Nested Transformers as described in:
'Aggregating Nested Transformers'
- https://arxiv.org/abs/2105.12723
The official Jax code is released and available at https://github.com/google-research/nested-transformer. The weights
have been con... |
17,776 | /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/nest.py | 121,122 | 84,128 | forward | TransformerLayer | 122,128 | 122,128 | class TransformerLayer(nn.Module):
"""
This is much like `.vision_transformer.Block` but:
- Called TransformerLayer here to allow for "block" as defined in the paper ("non-overlapping image blocks")
- Uses modified Attention layer that handles the "block" dimension
"""
def __init__(
... | class TransformerLayer(nn.Module):
"""
This is much like `.vision_transformer.Block` but:
- Called TransformerLayer here to allow for "block" as defined in the paper ("non-overlapping image blocks")
- Uses modified Attention layer that handles the "block" dimension
"""
def __init__(
... | y = self.norm1(x)
x = x + self.drop_path(self.attn(y))
x = x + self.drop_path(self.mlp(self.norm2(x)))
return x
| y = self.norm1(x)
x = x + self.drop_path(self.attn(y))
x = x + self.drop_path(self.mlp(self.norm2(x)))
return x
| 15 | 392 | 66 | 458 | """ Nested Transformer (NesT) in PyTorch
A PyTorch implement of Aggregating Nested Transformers as described in:
'Aggregating Nested Transformers'
- https://arxiv.org/abs/2105.12723
The official Jax code is released and available at https://github.com/google-research/nested-transformer. The weights
have been con... | """ Nested Transformer (NesT) in PyTorch
A PyTorch implement of Aggregating Nested Transformers as described in:
'Aggregating Nested Transformers'
- https://arxiv.org/abs/2105.12723
The official Jax code is released and available at https://github.com/google-research/nested-transformer. The weights
have been con... |
17,778 | /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/nextvit.py | 146,147 | 131,153 | forward | ConvAttention | 147,153 | 147,153 | class ConvAttention(nn.Module):
"""
Multi-Head Convolutional Attention
"""
def __init__(self, out_chs, head_dim, norm_layer = nn.BatchNorm2d, act_layer = nn.ReLU):
super(ConvAttention, self).__init__()
self.group_conv3x3 = nn.Conv2d(
out_chs, out_chs,
kernel_size... | class ConvAttention(nn.Module):
"""
Multi-Head Convolutional Attention
"""
def __init__(self, out_chs, head_dim, norm_layer = nn.BatchNorm2d, act_layer = nn.ReLU):
super(ConvAttention, self).__init__()
self.group_conv3x3 = nn.Conv2d(
out_chs, out_chs,
kernel_size... | out = self.group_conv3x3(x)
out = self.norm(out)
out = self.act(out)
out = self.projection(out)
return out
| out = self.group_conv3x3(x)
out = self.norm(out)
out = self.act(out)
out = self.projection(out)
return out
| 14 | 228 | 57 | 285 | """ Next-ViT
As described in https://arxiv.org/abs/2207.05501
Next-ViT model defs and weights adapted from https://github.com/bytedance/Next-ViT, original copyright below
"""
# Copyright (c) ByteDance Inc. All rights reserved.
from functools import partial
import torch
import torch.nn.functional as F
from torch impo... | """ Next-ViT
As described in https://arxiv.org/abs/2207.05501
Next-ViT model defs and weights adapted from https://github.com/bytedance/Next-ViT, original copyright below
"""
# Copyright (c) ByteDance Inc. All rights reserved.
from functools import partial
import torch
import torch.nn.functional as F
from torch impo... |
17,779 | /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/nextvit.py | 88,89 | 70,95 | forward | ConvNormAct | 89,95 | 89,95 | class ConvNormAct(nn.Module):
def __init__(
self,
in_chs,
out_chs,
kernel_size=3,
stride=1,
groups=1,
norm_layer=nn.BatchNorm2d,
act_layer=nn.ReLU,
):
super(ConvNormAct, self).__init__()
self.conv = n... | class ConvNormAct(nn.Module):
def __init__(
self,
in_chs,
out_chs,
kernel_size=3,
stride=1,
groups=1,
norm_layer=nn.BatchNorm2d,
act_layer=nn.ReLU,
):
super(ConvNormAct, self).__init__()
self.conv = n... | x = self.conv(x)
x = self.norm(x)
x = self.act(x)
return x
| x = self.conv(x)
x = self.norm(x)
x = self.act(x)
return x
| 11 | 199 | 42 | 241 | """ Next-ViT
As described in https://arxiv.org/abs/2207.05501
Next-ViT model defs and weights adapted from https://github.com/bytedance/Next-ViT, original copyright below
"""
# Copyright (c) ByteDance Inc. All rights reserved.
from functools import partial
import torch
import torch.nn.functional as F
from torch impo... | """ Next-ViT
As described in https://arxiv.org/abs/2207.05501
Next-ViT model defs and weights adapted from https://github.com/bytedance/Next-ViT, original copyright below
"""
# Copyright (c) ByteDance Inc. All rights reserved.
from functools import partial
import torch
import torch.nn.functional as F
from torch impo... |
17,780 | /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/nextvit.py | 252,253 | 211,281 | forward | EfficientAttention | 253,281 | 253,281 | class EfficientAttention(nn.Module):
"""
Efficient Multi-Head Self Attention
"""
fused_attn: torch.jit.Final[bool]
def __init__(
self,
dim,
out_dim=None,
head_dim=32,
qkv_bias=True,
attn_drop=0.,
proj_drop=0.,
... | class EfficientAttention(nn.Module):
"""
Efficient Multi-Head Self Attention
"""
fused_attn: torch.jit.Final[bool]
def __init__(
self,
dim,
out_dim=None,
head_dim=32,
qkv_bias=True,
attn_drop=0.,
proj_drop=0.,
... | B, N, C = x.shape
q = self.q(x).reshape(B, N, self.num_heads, self.head_dim).permute(0, 2, 1, 3)
if self.sr is not None:
x = self.sr(x.transpose(1, 2))
x = self.norm(x).transpose(1, 2)
k = self.k(x).reshape(B, -1, self.num_heads, self.head_dim).transpose(1, 2)
... | B, N, C = x.shape
q = self.q(x).reshape(B, N, self.num_heads, self.head_dim).permute(0, 2, 1, 3)
if self.sr is not None:
x = self.sr(x.transpose(1, 2))
x = self.norm(x).transpose(1, 2)
k = self.k(x).reshape(B, -1, self.num_heads, self.head_dim).transpose(1, 2)
... | 92 | 486 | 392 | 878 | """ Next-ViT
As described in https://arxiv.org/abs/2207.05501
Next-ViT model defs and weights adapted from https://github.com/bytedance/Next-ViT, original copyright below
"""
# Copyright (c) ByteDance Inc. All rights reserved.
from functools import partial
import torch
import torch.nn.functional as F
from torch impo... | """ Next-ViT
As described in https://arxiv.org/abs/2207.05501
Next-ViT model defs and weights adapted from https://github.com/bytedance/Next-ViT, original copyright below
"""
# Copyright (c) ByteDance Inc. All rights reserved.
from functools import partial
import torch
import torch.nn.functional as F
from torch impo... |
17,781 | /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/nextvit.py | 195,197 | 153,211 | reparameterize | NextConvBlock | 197,202 | 197,202 | class NextConvBlock(nn.Module):
"""
Next Convolution Block
"""
def __init__(
self,
in_chs,
out_chs,
stride=1,
drop_path=0.,
drop=0.,
head_dim=32,
mlp_ratio=3.,
norm_layer=nn.BatchNorm2d,
... | class NextConvBlock(nn.Module):
"""
Next Convolution Block
"""
def __init__(
self,
in_chs,
out_chs,
stride=1,
drop_path=0.,
drop=0.,
head_dim=32,
mlp_ratio=3.,
norm_layer=nn.BatchNorm2d,
... | if not self.is_fused:
merge_pre_bn(self.mlp.fc1, self.norm)
self.norm = None
self.is_fused = True
| if not self.is_fused:
merge_pre_bn(self.mlp.fc1, self.norm)
self.norm = None
self.is_fused = True
| 11 | 491 | 54 | 545 | """ Next-ViT
As described in https://arxiv.org/abs/2207.05501
Next-ViT model defs and weights adapted from https://github.com/bytedance/Next-ViT, original copyright below
"""
# Copyright (c) ByteDance Inc. All rights reserved.
from functools import partial
import torch
import torch.nn.functional as F
from torch impo... | """ Next-ViT
As described in https://arxiv.org/abs/2207.05501
Next-ViT model defs and weights adapted from https://github.com/bytedance/Next-ViT, original copyright below
"""
# Copyright (c) ByteDance Inc. All rights reserved.
from functools import partial
import torch
import torch.nn.functional as F
from torch impo... |
17,782 | /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/nextvit.py | 431,433 | 374,443 | set_grad_checkpointing | NextStage | 433,435 | 433,435 | class NextStage(nn.Module):
def __init__(
self,
in_chs,
block_chs,
block_types,
stride=2,
sr_ratio=1,
mix_block_ratio=1.0,
drop=0.,
attn_drop=0.,
drop_path=0.,
head_dim=32,
... | class NextStage(nn.Module):
def __init__(
self,
in_chs,
block_chs,
block_types,
stride=2,
sr_ratio=1,
mix_block_ratio=1.0,
drop=0.,
attn_drop=0.,
drop_path=0.,
head_dim=32,
... | self.grad_checkpointing = enable
| self.grad_checkpointing = enable
| 3 | 616 | 14 | 630 | """ Next-ViT
As described in https://arxiv.org/abs/2207.05501
Next-ViT model defs and weights adapted from https://github.com/bytedance/Next-ViT, original copyright below
"""
# Copyright (c) ByteDance Inc. All rights reserved.
from functools import partial
import torch
import torch.nn.functional as F
from torch impo... | """ Next-ViT
As described in https://arxiv.org/abs/2207.05501
Next-ViT model defs and weights adapted from https://github.com/bytedance/Next-ViT, original copyright below
"""
# Copyright (c) ByteDance Inc. All rights reserved.
from functools import partial
import torch
import torch.nn.functional as F
from torch impo... |
17,783 | /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/nextvit.py | 339,341 | 281,374 | reparameterize | NextTransformerBlock | 341,356 | 341,356 | class NextTransformerBlock(nn.Module):
"""
Next Transformer Block
"""
def __init__(
self,
in_chs,
out_chs,
drop_path,
stride=1,
sr_ratio=1,
mlp_ratio=2,
head_dim=32,
mix_block_ratio=0.75,
... | class NextTransformerBlock(nn.Module):
"""
Next Transformer Block
"""
def __init__(
self,
in_chs,
out_chs,
drop_path,
stride=1,
sr_ratio=1,
mlp_ratio=2,
head_dim=32,
mix_block_ratio=0.75,
... | if not self.is_fused:
merge_pre_bn(self.e_mhsa.q, self.norm1)
if self.e_mhsa.norm is not None:
merge_pre_bn(self.e_mhsa.k, self.norm1, self.e_mhsa.norm)
merge_pre_bn(self.e_mhsa.v, self.norm1, self.e_mhsa.norm)
self.e_mhsa.norm = nn.Identit... | if not self.is_fused:
merge_pre_bn(self.e_mhsa.q, self.norm1)
if self.e_mhsa.norm is not None:
merge_pre_bn(self.e_mhsa.k, self.norm1, self.e_mhsa.norm)
merge_pre_bn(self.e_mhsa.v, self.norm1, self.e_mhsa.norm)
self.e_mhsa.norm = nn.Identit... | 35 | 901 | 255 | 1,156 | """ Next-ViT
As described in https://arxiv.org/abs/2207.05501
Next-ViT model defs and weights adapted from https://github.com/bytedance/Next-ViT, original copyright below
"""
# Copyright (c) ByteDance Inc. All rights reserved.
from functools import partial
import torch
import torch.nn.functional as F
from torch impo... | """ Next-ViT
As described in https://arxiv.org/abs/2207.05501
Next-ViT model defs and weights adapted from https://github.com/bytedance/Next-ViT, original copyright below
"""
# Copyright (c) ByteDance Inc. All rights reserved.
from functools import partial
import torch
import torch.nn.functional as F
from torch impo... |
17,784 | /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/nextvit.py | 525,526 | 443,577 | _initialize_weights | NextViT | 526,536 | 526,536 | class NextViT(nn.Module):
def __init__(
self,
in_chans,
num_classes=1000,
global_pool='avg',
stem_chs=(64, 32, 64),
depths=(3, 4, 10, 3),
strides=(1, 2, 2, 2),
sr_ratios=(8, 4, 2, 1),
drop_path_rate=0.1,
... | class NextViT(nn.Module):
def __init__(
self,
in_chans,
num_classes=1000,
global_pool='avg',
stem_chs=(64, 32, 64),
depths=(3, 4, 10, 3),
strides=(1, 2, 2, 2),
sr_ratios=(8, 4, 2, 1),
drop_path_rate=0.1,
... | for n, m in self.named_modules():
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if hasattr(m, 'bias') and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Conv2d):
trunc_normal_(m.we... | for n, m in self.named_modules():
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if hasattr(m, 'bias') and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Conv2d):
trunc_normal_(m.we... | 35 | 1,689 | 161 | 1,850 | """ Next-ViT
As described in https://arxiv.org/abs/2207.05501
Next-ViT model defs and weights adapted from https://github.com/bytedance/Next-ViT, original copyright below
"""
# Copyright (c) ByteDance Inc. All rights reserved.
from functools import partial
import torch
import torch.nn.functional as F
from torch impo... | """ Next-ViT
As described in https://arxiv.org/abs/2207.05501
Next-ViT model defs and weights adapted from https://github.com/bytedance/Next-ViT, original copyright below
"""
# Copyright (c) ByteDance Inc. All rights reserved.
from functools import partial
import torch
import torch.nn.functional as F
from torch impo... |
17,785 | /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/nextvit.py | 127,128 | 105,131 | forward | PatchEmbed | 128,131 | 128,131 | class PatchEmbed(nn.Module):
def __init__(self,
in_chs,
out_chs,
stride=1,
norm_layer = nn.BatchNorm2d,
):
super(PatchEmbed, self).__init__()
if stride == 2:
self.pool = nn.AvgPool2d((2, 2), stride=2, ceil_mode=True, count_include_pad=... | class PatchEmbed(nn.Module):
def __init__(self,
in_chs,
out_chs,
stride=1,
norm_layer = nn.BatchNorm2d,
):
super(PatchEmbed, self).__init__()
if stride == 2:
self.pool = nn.AvgPool2d((2, 2), stride=2, ceil_mode=True, count_include_pad=... | return self.norm(self.conv(self.pool(x)))
| return self.norm(self.conv(self.pool(x)))
| 2 | 314 | 22 | 336 | """ Next-ViT
As described in https://arxiv.org/abs/2207.05501
Next-ViT model defs and weights adapted from https://github.com/bytedance/Next-ViT, original copyright below
"""
# Copyright (c) ByteDance Inc. All rights reserved.
from functools import partial
import torch
import torch.nn.functional as F
from torch impo... | """ Next-ViT
As described in https://arxiv.org/abs/2207.05501
Next-ViT model defs and weights adapted from https://github.com/bytedance/Next-ViT, original copyright below
"""
# Copyright (c) ByteDance Inc. All rights reserved.
from functools import partial
import torch
import torch.nn.functional as F
from torch impo... |
17,787 | /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/nfnet.py | 100,101 | 80,104 | forward | DownsampleAvg | 101,104 | 101,104 | class DownsampleAvg(nn.Module):
def __init__(
self,
in_chs: int,
out_chs: int,
stride: int = 1,
dilation: int = 1,
first_dilation: Optional[int] = None,
conv_layer: Callable = ScaledStdConv2d,
):
""" AvgPool Downsampling... | class DownsampleAvg(nn.Module):
def __init__(
self,
in_chs: int,
out_chs: int,
stride: int = 1,
dilation: int = 1,
first_dilation: Optional[int] = None,
conv_layer: Callable = ScaledStdConv2d,
):
""" AvgPool Downsampling... | return self.conv(self.pool(x))
| return self.conv(self.pool(x))
| 2 | 298 | 18 | 316 | """ Normalization Free Nets. NFNet, NF-RegNet, NF-ResNet (pre-activation) Models
Paper: `Characterizing signal propagation to close the performance gap in unnormalized ResNets`
- https://arxiv.org/abs/2101.08692
Paper: `High-Performance Large-Scale Image Recognition Without Normalization`
- https://arxiv.org/... | """ Normalization Free Nets. NFNet, NF-RegNet, NF-ResNet (pre-activation) Models
Paper: `Characterizing signal propagation to close the performance gap in unnormalized ResNets`
- https://arxiv.org/abs/2101.08692
Paper: `High-Performance Large-Scale Image Recognition Without Normalization`
- https://arxiv.org/... |
17,788 | /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/nfnet.py | 70,71 | 63,74 | forward | GammaAct | 71,74 | 71,74 | class GammaAct(nn.Module):
def __init__(self, act_type='relu', gamma: float = 1.0, inplace=False):
super().__init__()
self.act_fn = get_act_fn(act_type)
self.gamma = gamma
self.inplace = inplace
def forward(self, x):
FILL_FUNC_BODY | class GammaAct(nn.Module):
def __init__(self, act_type='relu', gamma: float = 1.0, inplace=False):
super().__init__()
self.act_fn = get_act_fn(act_type)
self.gamma = gamma
self.inplace = inplace
def forward(self, x):
FILL_FUNC_BODY | return self.act_fn(x, inplace=self.inplace).mul_(self.gamma)
| return self.act_fn(x, inplace=self.inplace).mul_(self.gamma)
| 3 | 101 | 31 | 132 | """ Normalization Free Nets. NFNet, NF-RegNet, NF-ResNet (pre-activation) Models
Paper: `Characterizing signal propagation to close the performance gap in unnormalized ResNets`
- https://arxiv.org/abs/2101.08692
Paper: `High-Performance Large-Scale Image Recognition Without Normalization`
- https://arxiv.org/... | """ Normalization Free Nets. NFNet, NF-RegNet, NF-ResNet (pre-activation) Models
Paper: `Characterizing signal propagation to close the performance gap in unnormalized ResNets`
- https://arxiv.org/abs/2101.08692
Paper: `High-Performance Large-Scale Image Recognition Without Normalization`
- https://arxiv.org/... |
17,789 | /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/nfnet.py | 177,178 | 104,203 | forward | NormFreeBlock | 178,203 | 178,203 | @register_notrace_module # reason: mul_ causes FX to drop a relevant node. https://github.com/pytorch/pytorch/issues/68301
class NormFreeBlock(nn.Module):
"""Normalization-Free pre-activation block.
"""
def __init__(
self,
in_chs: int,
out_chs: Optional[int] = None,
... | @register_notrace_module # reason: mul_ causes FX to drop a relevant node. https://github.com/pytorch/pytorch/issues/68301
class NormFreeBlock(nn.Module):
"""Normalization-Free pre-activation block.
"""
def __init__(
self,
in_chs: int,
out_chs: Optional[int] = None,
... | out = self.act1(x) * self.beta
# shortcut branch
shortcut = x
if self.downsample is not None:
shortcut = self.downsample(out)
# residual branch
out = self.conv1(out)
out = self.conv2(self.act2(out))
if self.conv2b is not None:
out... | out = self.act1(x) * self.beta
# shortcut branch
shortcut = x
if self.downsample is not None:
shortcut = self.downsample(out)
# residual branch
out = self.conv1(out)
out = self.conv2(self.act2(out))
if self.conv2b is not None:
out... | 77 | 1,027 | 275 | 1,302 | """ Normalization Free Nets. NFNet, NF-RegNet, NF-ResNet (pre-activation) Models
Paper: `Characterizing signal propagation to close the performance gap in unnormalized ResNets`
- https://arxiv.org/abs/2101.08692
Paper: `High-Performance Large-Scale Image Recognition Without Normalization`
- https://arxiv.org/... | """ Normalization Free Nets. NFNet, NF-RegNet, NF-ResNet (pre-activation) Models
Paper: `Characterizing signal propagation to close the performance gap in unnormalized ResNets`
- https://arxiv.org/abs/2101.08692
Paper: `High-Performance Large-Scale Image Recognition Without Normalization`
- https://arxiv.org/... |
17,792 | /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/pit.py | 137,138 | 115,142 | forward | ConvEmbedding | 138,142 | 138,142 | class ConvEmbedding(nn.Module):
def __init__(
self,
in_channels,
out_channels,
img_size: int = 224,
patch_size: int = 16,
stride: int = 8,
padding: int = 0,
):
super(ConvEmbedding, self).__init__()
padding = padd... | class ConvEmbedding(nn.Module):
def __init__(
self,
in_channels,
out_channels,
img_size: int = 224,
patch_size: int = 16,
stride: int = 8,
padding: int = 0,
):
super(ConvEmbedding, self).__init__()
padding = padd... | x = self.conv(x)
return x
| x = self.conv(x)
return x
| 5 | 299 | 20 | 319 | """ Pooling-based Vision Transformer (PiT) in PyTorch
A PyTorch implement of Pooling-based Vision Transformers as described in
'Rethinking Spatial Dimensions of Vision Transformers' - https://arxiv.org/abs/2103.16302
This code was adapted from the original version at https://github.com/naver-ai/pit, original copyrigh... | """ Pooling-based Vision Transformer (PiT) in PyTorch
A PyTorch implement of Pooling-based Vision Transformers as described in
'Rethinking Spatial Dimensions of Vision Transformers' - https://arxiv.org/abs/2103.16302
This code was adapted from the original version at https://github.com/naver-ai/pit, original copyrigh... |
17,793 | /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/pit.py | 109,110 | 94,115 | forward | Pooling | 110,115 | 110,115 | class Pooling(nn.Module):
def __init__(self, in_feature, out_feature, stride, padding_mode='zeros'):
super(Pooling, self).__init__()
self.conv = nn.Conv2d(
in_feature,
out_feature,
kernel_size=stride + 1,
padding=stride // 2,
stride=stride... | class Pooling(nn.Module):
def __init__(self, in_feature, out_feature, stride, padding_mode='zeros'):
super(Pooling, self).__init__()
self.conv = nn.Conv2d(
in_feature,
out_feature,
kernel_size=stride + 1,
padding=stride // 2,
stride=stride... | x = self.conv(x)
cls_token = self.fc(cls_token)
return x, cls_token
| x = self.conv(x)
cls_token = self.fc(cls_token)
return x, cls_token
| 9 | 188 | 41 | 229 | """ Pooling-based Vision Transformer (PiT) in PyTorch
A PyTorch implement of Pooling-based Vision Transformers as described in
'Rethinking Spatial Dimensions of Vision Transformers' - https://arxiv.org/abs/2103.16302
This code was adapted from the original version at https://github.com/naver-ai/pit, original copyrigh... | """ Pooling-based Vision Transformer (PiT) in PyTorch
A PyTorch implement of Pooling-based Vision Transformers as described in
'Rethinking Spatial Dimensions of Vision Transformers' - https://arxiv.org/abs/2103.16302
This code was adapted from the original version at https://github.com/naver-ai/pit, original copyrigh... |
17,794 | /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/pit.py | 249,250 | 142,292 | reset_classifier | PoolingVisionTransformer | 250,255 | 250,255 | class PoolingVisionTransformer(nn.Module):
""" Pooling-based Vision Transformer
A PyTorch implement of 'Rethinking Spatial Dimensions of Vision Transformers'
- https://arxiv.org/abs/2103.16302
"""
def __init__(
self,
img_size: int = 224,
patch_size: int = 16,... | class PoolingVisionTransformer(nn.Module):
""" Pooling-based Vision Transformer
A PyTorch implement of 'Rethinking Spatial Dimensions of Vision Transformers'
- https://arxiv.org/abs/2103.16302
"""
def __init__(
self,
img_size: int = 224,
patch_size: int = 16,... | self.num_classes = num_classes
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
if self.head_dist is not None:
self.head_dist = nn.Linear(self.embed_dim, self.num_classes) if num_classes > 0 else nn.Identity()
| self.num_classes = num_classes
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
if self.head_dist is not None:
self.head_dist = nn.Linear(self.embed_dim, self.num_classes) if num_classes > 0 else nn.Identity()
| 28 | 1,751 | 99 | 1,850 | """ Pooling-based Vision Transformer (PiT) in PyTorch
A PyTorch implement of Pooling-based Vision Transformers as described in
'Rethinking Spatial Dimensions of Vision Transformers' - https://arxiv.org/abs/2103.16302
This code was adapted from the original version at https://github.com/naver-ai/pit, original copyrigh... | """ Pooling-based Vision Transformer (PiT) in PyTorch
A PyTorch implement of Pooling-based Vision Transformers as described in
'Rethinking Spatial Dimensions of Vision Transformers' - https://arxiv.org/abs/2103.16302
This code was adapted from the original version at https://github.com/naver-ai/pit, original copyrigh... |
17,795 | /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/pit.py | 37,38 | 32,43 | forward | SequentialTuple | 38,43 | 38,43 | class SequentialTuple(nn.Sequential):
""" This module exists to work around torchscript typing issues list -> list"""
def __init__(self, *args):
super(SequentialTuple, self).__init__(*args)
def forward(self, x: Tuple[torch.Tensor, torch.Tensor]) -> Tuple[torch.Tensor, torch.Tensor]:
FILL_FUNC_BODY | class SequentialTuple(nn.Sequential):
""" This module exists to work around torchscript typing issues list -> list"""
def __init__(self, *args):
super(SequentialTuple, self).__init__(*args)
def forward(self, x: Tuple[torch.Tensor, torch.Tensor]) -> Tuple[torch.Tensor, torch.Tensor]:
FILL_FUNC_BODY | for module in self:
x = module(x)
return x
| for module in self:
x = module(x)
return x
| 9 | 114 | 26 | 140 | """ Pooling-based Vision Transformer (PiT) in PyTorch
A PyTorch implement of Pooling-based Vision Transformers as described in
'Rethinking Spatial Dimensions of Vision Transformers' - https://arxiv.org/abs/2103.16302
This code was adapted from the original version at https://github.com/naver-ai/pit, original copyrigh... | """ Pooling-based Vision Transformer (PiT) in PyTorch
A PyTorch implement of Pooling-based Vision Transformers as described in
'Rethinking Spatial Dimensions of Vision Transformers' - https://arxiv.org/abs/2103.16302
This code was adapted from the original version at https://github.com/naver-ai/pit, original copyrigh... |
17,796 | /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/pit.py | 74,75 | 43,94 | forward | Transformer | 75,94 | 75,94 | class Transformer(nn.Module):
def __init__(
self,
base_dim,
depth,
heads,
mlp_ratio,
pool=None,
proj_drop=.0,
attn_drop=.0,
drop_path_prob=None,
norm_layer=None,
):
super(Transformer, ... | class Transformer(nn.Module):
def __init__(
self,
base_dim,
depth,
heads,
mlp_ratio,
pool=None,
proj_drop=.0,
attn_drop=.0,
drop_path_prob=None,
norm_layer=None,
):
super(Transformer, ... | x, cls_tokens = x
token_length = cls_tokens.shape[1]
if self.pool is not None:
x, cls_tokens = self.pool(x, cls_tokens)
B, C, H, W = x.shape
x = x.flatten(2).transpose(1, 2)
x = torch.cat((cls_tokens, x), dim=1)
x = self.norm(x)
x = self.bloc... | x, cls_tokens = x
token_length = cls_tokens.shape[1]
if self.pool is not None:
x, cls_tokens = self.pool(x, cls_tokens)
B, C, H, W = x.shape
x = x.flatten(2).transpose(1, 2)
x = torch.cat((cls_tokens, x), dim=1)
x = self.norm(x)
x = self.bloc... | 56 | 326 | 218 | 544 | """ Pooling-based Vision Transformer (PiT) in PyTorch
A PyTorch implement of Pooling-based Vision Transformers as described in
'Rethinking Spatial Dimensions of Vision Transformers' - https://arxiv.org/abs/2103.16302
This code was adapted from the original version at https://github.com/naver-ai/pit, original copyrigh... | """ Pooling-based Vision Transformer (PiT) in PyTorch
A PyTorch implement of Pooling-based Vision Transformers as described in
'Rethinking Spatial Dimensions of Vision Transformers' - https://arxiv.org/abs/2103.16302
This code was adapted from the original version at https://github.com/naver-ai/pit, original copyrigh... |
17,798 | /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/pnasnet.py | 71,72 | 62,78 | forward | ActConvBn | 72,78 | 72,78 | class ActConvBn(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=''):
super(ActConvBn, self).__init__()
self.act = nn.ReLU()
self.conv = create_conv2d(
in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding)
... | class ActConvBn(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=''):
super(ActConvBn, self).__init__()
self.act = nn.ReLU()
self.conv = create_conv2d(
in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding)
... | x = self.act(x)
x = self.conv(x)
x = self.bn(x)
return x
| x = self.act(x)
x = self.conv(x)
x = self.bn(x)
return x
| 11 | 163 | 42 | 205 | """
pnasnet5large implementation grabbed from Cadene's pretrained models
Additional credit to https://github.com/creafz
https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/pnasnet.py
"""
from collections import OrderedDict
from functools import partial
import torch
import torch... | """
pnasnet5large implementation grabbed from Cadene's pretrained models
Additional credit to https://github.com/creafz
https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/pnasnet.py
"""
from collections import OrderedDict
from functools import partial
import torch
import torch... |
17,799 | /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/pnasnet.py | 52,53 | 38,62 | forward | BranchSeparables | 53,62 | 53,62 | class BranchSeparables(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1, stem_cell=False, padding=''):
super(BranchSeparables, self).__init__()
middle_channels = out_channels if stem_cell else in_channels
self.act_1 = nn.ReLU()
self.separable_1 = Separ... | class BranchSeparables(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1, stem_cell=False, padding=''):
super(BranchSeparables, self).__init__()
middle_channels = out_channels if stem_cell else in_channels
self.act_1 = nn.ReLU()
self.separable_1 = Separ... | x = self.act_1(x)
x = self.separable_1(x)
x = self.bn_sep_1(x)
x = self.act_2(x)
x = self.separable_2(x)
x = self.bn_sep_2(x)
return x
| x = self.act_1(x)
x = self.separable_1(x)
x = self.bn_sep_1(x)
x = self.act_2(x)
x = self.separable_2(x)
x = self.bn_sep_2(x)
return x
| 20 | 283 | 93 | 376 | """
pnasnet5large implementation grabbed from Cadene's pretrained models
Additional credit to https://github.com/creafz
https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/pnasnet.py
"""
from collections import OrderedDict
from functools import partial
import torch
import torch... | """
pnasnet5large implementation grabbed from Cadene's pretrained models
Additional credit to https://github.com/creafz
https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/pnasnet.py
"""
from collections import OrderedDict
from functools import partial
import torch
import torch... |
17,800 | /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/pnasnet.py | 223,224 | 170,230 | forward | Cell | 224,230 | 224,230 | class Cell(CellBase):
def __init__(
self,
in_chs_left,
out_chs_left,
in_chs_right,
out_chs_right,
pad_type='',
is_reduction=False,
match_prev_layer_dims=False,
):
super(Cell, self).__init__()
# If `... | class Cell(CellBase):
def __init__(
self,
in_chs_left,
out_chs_left,
in_chs_right,
out_chs_right,
pad_type='',
is_reduction=False,
match_prev_layer_dims=False,
):
super(Cell, self).__init__()
# If `... | x_left = self.conv_prev_1x1(x_left)
x_right = self.conv_1x1(x_right)
x_out = self.cell_forward(x_left, x_right)
return x_out
| x_left = self.conv_prev_1x1(x_left)
x_right = self.conv_1x1(x_right)
x_out = self.cell_forward(x_left, x_right)
return x_out
| 12 | 889 | 72 | 961 | """
pnasnet5large implementation grabbed from Cadene's pretrained models
Additional credit to https://github.com/creafz
https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/pnasnet.py
"""
from collections import OrderedDict
from functools import partial
import torch
import torch... | """
pnasnet5large implementation grabbed from Cadene's pretrained models
Additional credit to https://github.com/creafz
https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/pnasnet.py
"""
from collections import OrderedDict
from functools import partial
import torch
import torch... |
17,801 | /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/pnasnet.py | 104,105 | 102,132 | cell_forward | CellBase | 105,132 | 105,132 | class CellBase(nn.Module):
def cell_forward(self, x_left, x_right):
FILL_FUNC_BODY | class CellBase(nn.Module):
def cell_forward(self, x_left, x_right):
FILL_FUNC_BODY | x_comb_iter_0_left = self.comb_iter_0_left(x_left)
x_comb_iter_0_right = self.comb_iter_0_right(x_left)
x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right
x_comb_iter_1_left = self.comb_iter_1_left(x_right)
x_comb_iter_1_right = self.comb_iter_1_right(x_right)
x_co... | x_comb_iter_0_left = self.comb_iter_0_left(x_left)
x_comb_iter_0_right = self.comb_iter_0_right(x_left)
x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right
x_comb_iter_1_left = self.comb_iter_1_left(x_right)
x_comb_iter_1_right = self.comb_iter_1_right(x_right)
x_co... | 74 | 37 | 538 | 575 | """
pnasnet5large implementation grabbed from Cadene's pretrained models
Additional credit to https://github.com/creafz
https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/pnasnet.py
"""
from collections import OrderedDict
from functools import partial
import torch
import torch... | """
pnasnet5large implementation grabbed from Cadene's pretrained models
Additional credit to https://github.com/creafz
https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/pnasnet.py
"""
from collections import OrderedDict
from functools import partial
import torch
import torch... |
17,802 | /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/pnasnet.py | 164,165 | 132,170 | forward | CellStem0 | 165,170 | 165,170 | class CellStem0(CellBase):
def __init__(self, in_chs_left, out_chs_left, in_chs_right, out_chs_right, pad_type=''):
super(CellStem0, self).__init__()
self.conv_1x1 = ActConvBn(in_chs_right, out_chs_right, kernel_size=1, padding=pad_type)
self.comb_iter_0_left = BranchSeparables(
... | class CellStem0(CellBase):
def __init__(self, in_chs_left, out_chs_left, in_chs_right, out_chs_right, pad_type=''):
super(CellStem0, self).__init__()
self.conv_1x1 = ActConvBn(in_chs_right, out_chs_right, kernel_size=1, padding=pad_type)
self.comb_iter_0_left = BranchSeparables(
... | x_right = self.conv_1x1(x_left)
x_out = self.cell_forward(x_left, x_right)
return x_out
| x_right = self.conv_1x1(x_left)
x_out = self.cell_forward(x_left, x_right)
return x_out
| 9 | 686 | 51 | 737 | """
pnasnet5large implementation grabbed from Cadene's pretrained models
Additional credit to https://github.com/creafz
https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/pnasnet.py
"""
from collections import OrderedDict
from functools import partial
import torch
import torch... | """
pnasnet5large implementation grabbed from Cadene's pretrained models
Additional credit to https://github.com/creafz
https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/pnasnet.py
"""
from collections import OrderedDict
from functools import partial
import torch
import torch... |
17,803 | /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/pnasnet.py | 94,95 | 78,102 | forward | FactorizedReduction | 95,102 | 95,102 | class FactorizedReduction(nn.Module):
def __init__(self, in_channels, out_channels, padding=''):
super(FactorizedReduction, self).__init__()
self.act = nn.ReLU()
self.path_1 = nn.Sequential(OrderedDict([
('avgpool', nn.AvgPool2d(1, stride=2, count_include_pad=False)),
... | class FactorizedReduction(nn.Module):
def __init__(self, in_channels, out_channels, padding=''):
super(FactorizedReduction, self).__init__()
self.act = nn.ReLU()
self.path_1 = nn.Sequential(OrderedDict([
('avgpool', nn.AvgPool2d(1, stride=2, count_include_pad=False)),
... | x = self.act(x)
x_path1 = self.path_1(x)
x_path2 = self.path_2(x)
out = self.final_path_bn(torch.cat([x_path1, x_path2], 1))
return out
| x = self.act(x)
x_path1 = self.path_1(x)
x_path2 = self.path_2(x)
out = self.final_path_bn(torch.cat([x_path1, x_path2], 1))
return out
| 16 | 323 | 83 | 406 | """
pnasnet5large implementation grabbed from Cadene's pretrained models
Additional credit to https://github.com/creafz
https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/pnasnet.py
"""
from collections import OrderedDict
from functools import partial
import torch
import torch... | """
pnasnet5large implementation grabbed from Cadene's pretrained models
Additional credit to https://github.com/creafz
https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/pnasnet.py
"""
from collections import OrderedDict
from functools import partial
import torch
import torch... |
17,805 | /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/pnasnet.py | 32,33 | 22,38 | forward | SeparableConv2d | 33,38 | 33,38 | class SeparableConv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride, padding=''):
super(SeparableConv2d, self).__init__()
self.depthwise_conv2d = create_conv2d(
in_channels, in_channels, kernel_size=kernel_size,
stride=stride, padding=padding,... | class SeparableConv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride, padding=''):
super(SeparableConv2d, self).__init__()
self.depthwise_conv2d = create_conv2d(
in_channels, in_channels, kernel_size=kernel_size,
stride=stride, padding=padding,... | x = self.depthwise_conv2d(x)
x = self.pointwise_conv2d(x)
return x
| x = self.depthwise_conv2d(x)
x = self.pointwise_conv2d(x)
return x
| 8 | 176 | 41 | 217 | """
pnasnet5large implementation grabbed from Cadene's pretrained models
Additional credit to https://github.com/creafz
https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/pnasnet.py
"""
from collections import OrderedDict
from functools import partial
import torch
import torch... | """
pnasnet5large implementation grabbed from Cadene's pretrained models
Additional credit to https://github.com/creafz
https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/pnasnet.py
"""
from collections import OrderedDict
from functools import partial
import torch
import torch... |
17,807 | /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/pvt_v2.py | 111,112 | 68,147 | forward | Attention | 112,147 | 112,147 | class Attention(nn.Module):
fused_attn: torch.jit.Final[bool]
def __init__(
self,
dim,
num_heads=8,
sr_ratio=1,
linear_attn=False,
qkv_bias=True,
attn_drop=0.,
proj_drop=0.
):
super().__init__()
... | class Attention(nn.Module):
fused_attn: torch.jit.Final[bool]
def __init__(
self,
dim,
num_heads=8,
sr_ratio=1,
linear_attn=False,
qkv_bias=True,
attn_drop=0.,
proj_drop=0.
):
super().__init__()
... | B, N, C = x.shape
H, W = feat_size
q = self.q(x).reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3)
if self.pool is not None:
x = x.permute(0, 2, 1).reshape(B, C, H, W)
x = self.sr(self.pool(x)).reshape(B, C, -1).permute(0, 2, 1)
x = self.norm(x)
... | B, N, C = x.shape
H, W = feat_size
q = self.q(x).reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3)
if self.pool is not None:
x = x.permute(0, 2, 1).reshape(B, C, H, W)
x = self.sr(self.pool(x)).reshape(B, C, -1).permute(0, 2, 1)
x = self.norm(x)
... | 155 | 526 | 633 | 1,159 | """ Pyramid Vision Transformer v2
@misc{wang2021pvtv2,
title={PVTv2: Improved Baselines with Pyramid Vision Transformer},
author={Wenhai Wang and Enze Xie and Xiang Li and Deng-Ping Fan and Kaitao Song and Ding Liang and
Tong Lu and Ping Luo and Ling Shao},
year={2021},
eprint={2106.137... | """ Pyramid Vision Transformer v2
@misc{wang2021pvtv2,
title={PVTv2: Improved Baselines with Pyramid Vision Transformer},
author={Wenhai Wang and Enze Xie and Xiang Li and Deng-Ping Fan and Kaitao Song and Ding Liang and
Tong Lu and Ping Luo and Ling Shao},
year={2021},
eprint={2106.137... |
17,808 | /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/pvt_v2.py | 186,187 | 147,193 | forward | Block | 187,193 | 187,193 | class Block(nn.Module):
def __init__(
self,
dim,
num_heads,
mlp_ratio=4.,
sr_ratio=1,
linear_attn=False,
qkv_bias=False,
proj_drop=0.,
attn_drop=0.,
drop_path=0.,
act_layer=nn.GELU,
... | class Block(nn.Module):
def __init__(
self,
dim,
num_heads,
mlp_ratio=4.,
sr_ratio=1,
linear_attn=False,
qkv_bias=False,
proj_drop=0.,
attn_drop=0.,
drop_path=0.,
act_layer=nn.GELU,
... | x = x + self.drop_path1(self.attn(self.norm1(x), feat_size))
x = x + self.drop_path2(self.mlp(self.norm2(x), feat_size))
return x
| x = x + self.drop_path1(self.attn(self.norm1(x), feat_size))
x = x + self.drop_path2(self.mlp(self.norm2(x), feat_size))
return x
| 14 | 403 | 71 | 474 | """ Pyramid Vision Transformer v2
@misc{wang2021pvtv2,
title={PVTv2: Improved Baselines with Pyramid Vision Transformer},
author={Wenhai Wang and Enze Xie and Xiang Li and Deng-Ping Fan and Kaitao Song and Ding Liang and
Tong Lu and Ping Luo and Ling Shao},
year={2021},
eprint={2106.137... | """ Pyramid Vision Transformer v2
@misc{wang2021pvtv2,
title={PVTv2: Improved Baselines with Pyramid Vision Transformer},
author={Wenhai Wang and Enze Xie and Xiang Li and Deng-Ping Fan and Kaitao Song and Ding Liang and
Tong Lu and Ping Luo and Ling Shao},
year={2021},
eprint={2106.137... |
17,809 | /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/pvt_v2.py | 54,55 | 34,68 | forward | MlpWithDepthwiseConv | 55,68 | 55,68 | class MlpWithDepthwiseConv(nn.Module):
def __init__(
self,
in_features,
hidden_features=None,
out_features=None,
act_layer=nn.GELU,
drop=0.,
extra_relu=False,
):
super().__init__()
out_features = out_features or ... | class MlpWithDepthwiseConv(nn.Module):
def __init__(
self,
in_features,
hidden_features=None,
out_features=None,
act_layer=nn.GELU,
drop=0.,
extra_relu=False,
):
super().__init__()
out_features = out_features or ... | x = self.fc1(x)
B, N, C = x.shape
x = x.transpose(1, 2).view(B, C, feat_size[0], feat_size[1])
x = self.relu(x)
x = self.dwconv(x)
x = x.flatten(2).transpose(1, 2)
x = self.act(x)
x = self.drop(x)
x = self.fc2(x)
x = self.drop(x)
re... | x = self.fc1(x)
B, N, C = x.shape
x = x.transpose(1, 2).view(B, C, feat_size[0], feat_size[1])
x = self.relu(x)
x = self.dwconv(x)
x = x.flatten(2).transpose(1, 2)
x = self.act(x)
x = self.drop(x)
x = self.fc2(x)
x = self.drop(x)
re... | 39 | 272 | 156 | 428 | """ Pyramid Vision Transformer v2
@misc{wang2021pvtv2,
title={PVTv2: Improved Baselines with Pyramid Vision Transformer},
author={Wenhai Wang and Enze Xie and Xiang Li and Deng-Ping Fan and Kaitao Song and Ding Liang and
Tong Lu and Ping Luo and Ling Shao},
year={2021},
eprint={2106.137... | """ Pyramid Vision Transformer v2
@misc{wang2021pvtv2,
title={PVTv2: Improved Baselines with Pyramid Vision Transformer},
author={Wenhai Wang and Enze Xie and Xiang Li and Deng-Ping Fan and Kaitao Song and Ding Liang and
Tong Lu and Ping Luo and Ling Shao},
year={2021},
eprint={2106.137... |
17,810 | /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/pvt_v2.py | 206,207 | 193,213 | forward | OverlapPatchEmbed | 207,213 | 207,213 | class OverlapPatchEmbed(nn.Module):
""" Image to Patch Embedding
"""
def __init__(self, patch_size=7, stride=4, in_chans=3, embed_dim=768):
super().__init__()
patch_size = to_2tuple(patch_size)
assert max(patch_size) > stride, "Set larger patch_size than stride"
self.patch_si... | class OverlapPatchEmbed(nn.Module):
""" Image to Patch Embedding
"""
def __init__(self, patch_size=7, stride=4, in_chans=3, embed_dim=768):
super().__init__()
patch_size = to_2tuple(patch_size)
assert max(patch_size) > stride, "Set larger patch_size than stride"
self.patch_si... | x = self.proj(x)
x = x.permute(0, 2, 3, 1)
x = self.norm(x)
return x
| x = self.proj(x)
x = x.permute(0, 2, 3, 1)
x = self.norm(x)
return x
| 14 | 214 | 53 | 267 | """ Pyramid Vision Transformer v2
@misc{wang2021pvtv2,
title={PVTv2: Improved Baselines with Pyramid Vision Transformer},
author={Wenhai Wang and Enze Xie and Xiang Li and Deng-Ping Fan and Kaitao Song and Ding Liang and
Tong Lu and Ping Luo and Ling Shao},
year={2021},
eprint={2106.137... | """ Pyramid Vision Transformer v2
@misc{wang2021pvtv2,
title={PVTv2: Improved Baselines with Pyramid Vision Transformer},
author={Wenhai Wang and Enze Xie and Xiang Li and Deng-Ping Fan and Kaitao Song and Ding Liang and
Tong Lu and Ping Luo and Ling Shao},
year={2021},
eprint={2106.137... |
17,811 | /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/pvt_v2.py | 259,260 | 213,277 | forward | PyramidVisionTransformerStage | 260,277 | 260,277 | class PyramidVisionTransformerStage(nn.Module):
def __init__(
self,
dim: int,
dim_out: int,
depth: int,
downsample: bool = True,
num_heads: int = 8,
sr_ratio: int = 1,
linear_attn: bool = False,
mlp_ratio: fl... | class PyramidVisionTransformerStage(nn.Module):
def __init__(
self,
dim: int,
dim_out: int,
depth: int,
downsample: bool = True,
num_heads: int = 8,
sr_ratio: int = 1,
linear_attn: bool = False,
mlp_ratio: fl... | # x is either B, C, H, W (if downsample) or B, H, W, C if not
if self.downsample is not None:
# input to downsample is B, C, H, W
x = self.downsample(x) # output B, H, W, C
B, H, W, C = x.shape
feat_size = (H, W)
x = x.reshape(B, -1, C)
for blk in... | # x is either B, C, H, W (if downsample) or B, H, W, C if not
if self.downsample is not None:
# input to downsample is B, C, H, W
x = self.downsample(x) # output B, H, W, C
B, H, W, C = x.shape
feat_size = (H, W)
x = x.reshape(B, -1, C)
for blk in... | 88 | 462 | 262 | 724 | """ Pyramid Vision Transformer v2
@misc{wang2021pvtv2,
title={PVTv2: Improved Baselines with Pyramid Vision Transformer},
author={Wenhai Wang and Enze Xie and Xiang Li and Deng-Ping Fan and Kaitao Song and Ding Liang and
Tong Lu and Ping Luo and Ling Shao},
year={2021},
eprint={2106.137... | """ Pyramid Vision Transformer v2
@misc{wang2021pvtv2,
title={PVTv2: Improved Baselines with Pyramid Vision Transformer},
author={Wenhai Wang and Enze Xie and Xiang Li and Deng-Ping Fan and Kaitao Song and Ding Liang and
Tong Lu and Ping Luo and Ling Shao},
year={2021},
eprint={2106.137... |
17,812 | /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/pvt_v2.py | 382,383 | 277,406 | reset_classifier | PyramidVisionTransformerV2 | 383,389 | 383,389 | class PyramidVisionTransformerV2(nn.Module):
def __init__(
self,
in_chans=3,
num_classes=1000,
global_pool='avg',
depths=(3, 4, 6, 3),
embed_dims=(64, 128, 256, 512),
num_heads=(1, 2, 4, 8),
sr_ratios=(8, 4, 2, 1),
... | class PyramidVisionTransformerV2(nn.Module):
def __init__(
self,
in_chans=3,
num_classes=1000,
global_pool='avg',
depths=(3, 4, 6, 3),
embed_dims=(64, 128, 256, 512),
num_heads=(1, 2, 4, 8),
sr_ratios=(8, 4, 2, 1),
... | self.num_classes = num_classes
if global_pool is not None:
assert global_pool in ('avg', '')
self.global_pool = global_pool
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
| self.num_classes = num_classes
if global_pool is not None:
assert global_pool in ('avg', '')
self.global_pool = global_pool
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
| 26 | 1,417 | 85 | 1,502 | """ Pyramid Vision Transformer v2
@misc{wang2021pvtv2,
title={PVTv2: Improved Baselines with Pyramid Vision Transformer},
author={Wenhai Wang and Enze Xie and Xiang Li and Deng-Ping Fan and Kaitao Song and Ding Liang and
Tong Lu and Ping Luo and Ling Shao},
year={2021},
eprint={2106.137... | """ Pyramid Vision Transformer v2
@misc{wang2021pvtv2,
title={PVTv2: Improved Baselines with Pyramid Vision Transformer},
author={Wenhai Wang and Enze Xie and Xiang Li and Deng-Ping Fan and Kaitao Song and Ding Liang and
Tong Lu and Ping Luo and Ling Shao},
year={2021},
eprint={2106.137... |
17,814 | /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/regnet.py | 235,236 | 174,249 | forward | Bottleneck | 236,249 | 236,249 | class Bottleneck(nn.Module):
""" RegNet Bottleneck
This is almost exactly the same as a ResNet Bottlneck. The main difference is the SE block is moved from
after conv3 to after conv2. Otherwise, it's just redefining the arguments for groups/bottleneck channels.
"""
def __init__(
self,
... | class Bottleneck(nn.Module):
""" RegNet Bottleneck
This is almost exactly the same as a ResNet Bottlneck. The main difference is the SE block is moved from
after conv3 to after conv2. Otherwise, it's just redefining the arguments for groups/bottleneck channels.
"""
def __init__(
self,
... | shortcut = x
x = self.conv1(x)
x = self.conv2(x)
x = self.se(x)
x = self.conv3(x)
if self.downsample is not None:
# NOTE stuck with downsample as the attr name due to weight compatibility
# now represents the shortcut, no shortcut if None, and non-... | shortcut = x
x = self.conv1(x)
x = self.conv2(x)
x = self.se(x)
x = self.conv3(x)
if self.downsample is not None:
# NOTE stuck with downsample as the attr name due to weight compatibility
# now represents the shortcut, no shortcut if None, and non-... | 57 | 725 | 152 | 877 | """RegNet X, Y, Z, and more
Paper: `Designing Network Design Spaces` - https://arxiv.org/abs/2003.13678
Original Impl: https://github.com/facebookresearch/pycls/blob/master/pycls/models/regnet.py
Paper: `Fast and Accurate Model Scaling` - https://arxiv.org/abs/2103.06877
Original Impl: None
Based on original PyTorch... | """RegNet X, Y, Z, and more
Paper: `Designing Network Design Spaces` - https://arxiv.org/abs/2003.13678
Original Impl: https://github.com/facebookresearch/pycls/blob/master/pycls/models/regnet.py
Paper: `Fast and Accurate Model Scaling` - https://arxiv.org/abs/2103.06877
Original Impl: None
Based on original PyTorch... |
17,815 | /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/regnet.py | 309,310 | 249,325 | forward | PreBottleneck | 310,325 | 310,325 | class PreBottleneck(nn.Module):
""" RegNet Bottleneck
This is almost exactly the same as a ResNet Bottlneck. The main difference is the SE block is moved from
after conv3 to after conv2. Otherwise, it's just redefining the arguments for groups/bottleneck channels.
"""
def __init__(
sel... | class PreBottleneck(nn.Module):
""" RegNet Bottleneck
This is almost exactly the same as a ResNet Bottlneck. The main difference is the SE block is moved from
after conv3 to after conv2. Otherwise, it's just redefining the arguments for groups/bottleneck channels.
"""
def __init__(
sel... | x = self.norm1(x)
shortcut = x
x = self.conv1(x)
x = self.norm2(x)
x = self.conv2(x)
x = self.se(x)
x = self.norm3(x)
x = self.conv3(x)
if self.downsample is not None:
# NOTE stuck with downsample as the attr name due to weight compatib... | x = self.norm1(x)
shortcut = x
x = self.conv1(x)
x = self.norm2(x)
x = self.conv2(x)
x = self.se(x)
x = self.norm3(x)
x = self.conv3(x)
if self.downsample is not None:
# NOTE stuck with downsample as the attr name due to weight compatib... | 63 | 707 | 176 | 883 | """RegNet X, Y, Z, and more
Paper: `Designing Network Design Spaces` - https://arxiv.org/abs/2003.13678
Original Impl: https://github.com/facebookresearch/pycls/blob/master/pycls/models/regnet.py
Paper: `Fast and Accurate Model Scaling` - https://arxiv.org/abs/2103.06877
Original Impl: None
Based on original PyTorch... | """RegNet X, Y, Z, and more
Paper: `Designing Network Design Spaces` - https://arxiv.org/abs/2003.13678
Original Impl: https://github.com/facebookresearch/pycls/blob/master/pycls/models/regnet.py
Paper: `Fast and Accurate Model Scaling` - https://arxiv.org/abs/2103.06877
Original Impl: None
Based on original PyTorch... |
17,817 | /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/regnet.py | 362,363 | 325,371 | forward | RegStage | 363,371 | 363,371 | class RegStage(nn.Module):
"""Stage (sequence of blocks w/ the same output shape)."""
def __init__(
self,
depth,
in_chs,
out_chs,
stride,
dilation,
drop_path_rates=None,
block_fn=Bottleneck,
**block_kwar... | class RegStage(nn.Module):
"""Stage (sequence of blocks w/ the same output shape)."""
def __init__(
self,
depth,
in_chs,
out_chs,
stride,
dilation,
drop_path_rates=None,
block_fn=Bottleneck,
**block_kwar... | if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint_seq(self.children(), x)
else:
for block in self.children():
x = block(x)
return x
| if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint_seq(self.children(), x)
else:
for block in self.children():
x = block(x)
return x
| 19 | 356 | 73 | 429 | """RegNet X, Y, Z, and more
Paper: `Designing Network Design Spaces` - https://arxiv.org/abs/2003.13678
Original Impl: https://github.com/facebookresearch/pycls/blob/master/pycls/models/regnet.py
Paper: `Fast and Accurate Model Scaling` - https://arxiv.org/abs/2103.06877
Original Impl: None
Based on original PyTorch... | """RegNet X, Y, Z, and more
Paper: `Designing Network Design Spaces` - https://arxiv.org/abs/2003.13678
Original Impl: https://github.com/facebookresearch/pycls/blob/master/pycls/models/regnet.py
Paper: `Fast and Accurate Model Scaling` - https://arxiv.org/abs/2103.06877
Original Impl: None
Based on original PyTorch... |
17,819 | /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/repghost.py | 183,184 | 133,205 | forward | RepGhostBottleneck | 184,205 | 184,205 | class RepGhostBottleneck(nn.Module):
""" RepGhost bottleneck w/ optional SE"""
def __init__(
self,
in_chs,
mid_chs,
out_chs,
dw_kernel_size=3,
stride=1,
act_layer=nn.ReLU,
se_ratio=0.,
reparam=True,
... | class RepGhostBottleneck(nn.Module):
""" RepGhost bottleneck w/ optional SE"""
def __init__(
self,
in_chs,
mid_chs,
out_chs,
dw_kernel_size=3,
stride=1,
act_layer=nn.ReLU,
se_ratio=0.,
reparam=True,
... | shortcut = x
# 1st ghost bottleneck
x = self.ghost1(x)
# Depth-wise convolution
if self.conv_dw is not None:
x = self.conv_dw(x)
x = self.bn_dw(x)
# Squeeze-and-excitation
if self.se is not None:
x = self.se(x)
# 2nd... | shortcut = x
# 1st ghost bottleneck
x = self.ghost1(x)
# Depth-wise convolution
if self.conv_dw is not None:
x = self.conv_dw(x)
x = self.bn_dw(x)
# Squeeze-and-excitation
if self.se is not None:
x = self.se(x)
# 2nd... | 46 | 630 | 175 | 805 | """
An implementation of RepGhostNet Model as defined in:
RepGhost: A Hardware-Efficient Ghost Module via Re-parameterization. https://arxiv.org/abs/2211.06088
Original implementation: https://github.com/ChengpengChen/RepGhost
"""
import copy
from functools import partial
import torch
import torch.nn as nn
import tor... | """
An implementation of RepGhostNet Model as defined in:
RepGhost: A Hardware-Efficient Ghost Module via Re-parameterization. https://arxiv.org/abs/2211.06088
Original implementation: https://github.com/ChengpengChen/RepGhost
"""
import copy
from functools import partial
import torch
import torch.nn as nn
import tor... |
17,820 | /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/repghost.py | 110,111 | 27,133 | switch_to_deploy | RepGhostModule | 111,129 | 111,129 | class RepGhostModule(nn.Module):
def __init__(
self,
in_chs,
out_chs,
kernel_size=1,
dw_size=3,
stride=1,
relu=True,
reparam=True,
):
super(RepGhostModule, self).__init__()
self.out_chs = out_chs
... | class RepGhostModule(nn.Module):
def __init__(
self,
in_chs,
out_chs,
kernel_size=1,
dw_size=3,
stride=1,
relu=True,
reparam=True,
):
super(RepGhostModule, self).__init__()
self.out_chs = out_chs
... | if len(self.fusion_conv) == 0 and len(self.fusion_bn) == 0:
return
kernel, bias = self.get_equivalent_kernel_bias()
self.cheap_operation = nn.Conv2d(
in_channels=self.cheap_operation[0].in_channels,
out_channels=self.cheap_operation[0].out_channels,
... | if len(self.fusion_conv) == 0 and len(self.fusion_bn) == 0:
return
kernel, bias = self.get_equivalent_kernel_bias()
self.cheap_operation = nn.Conv2d(
in_channels=self.cheap_operation[0].in_channels,
out_channels=self.cheap_operation[0].out_channels,
... | 37 | 1,126 | 273 | 1,399 | """
An implementation of RepGhostNet Model as defined in:
RepGhost: A Hardware-Efficient Ghost Module via Re-parameterization. https://arxiv.org/abs/2211.06088
Original implementation: https://github.com/ChengpengChen/RepGhost
"""
import copy
from functools import partial
import torch
import torch.nn as nn
import tor... | """
An implementation of RepGhostNet Model as defined in:
RepGhost: A Hardware-Efficient Ghost Module via Re-parameterization. https://arxiv.org/abs/2211.06088
Original implementation: https://github.com/ChengpengChen/RepGhost
"""
import copy
from functools import partial
import torch
import torch.nn as nn
import tor... |
17,821 | /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/repghost.py | 304,305 | 205,323 | forward_head | RepGhostNet | 305,314 | 305,314 | class RepGhostNet(nn.Module):
def __init__(
self,
cfgs,
num_classes=1000,
width=1.0,
in_chans=3,
output_stride=32,
global_pool='avg',
drop_rate=0.2,
reparam=True,
):
super(RepGhostNet, self).__ini... | class RepGhostNet(nn.Module):
def __init__(
self,
cfgs,
num_classes=1000,
width=1.0,
in_chans=3,
output_stride=32,
global_pool='avg',
drop_rate=0.2,
reparam=True,
):
super(RepGhostNet, self).__ini... | x = self.global_pool(x)
x = self.conv_head(x)
x = self.act2(x)
x = self.flatten(x)
if self.drop_rate > 0.:
x = F.dropout(x, p=self.drop_rate, training=self.training)
x = self.classifier(x)
return x
| x = self.global_pool(x)
x = self.conv_head(x)
x = self.act2(x)
x = self.flatten(x)
if self.drop_rate > 0.:
x = F.dropout(x, p=self.drop_rate, training=self.training)
x = self.classifier(x)
return x
| 26 | 1,403 | 107 | 1,510 | """
An implementation of RepGhostNet Model as defined in:
RepGhost: A Hardware-Efficient Ghost Module via Re-parameterization. https://arxiv.org/abs/2211.06088
Original implementation: https://github.com/ChengpengChen/RepGhost
"""
import copy
from functools import partial
import torch
import torch.nn as nn
import tor... | """
An implementation of RepGhostNet Model as defined in:
RepGhost: A Hardware-Efficient Ghost Module via Re-parameterization. https://arxiv.org/abs/2211.06088
Original implementation: https://github.com/ChengpengChen/RepGhost
"""
import copy
from functools import partial
import torch
import torch.nn as nn
import tor... |
17,823 | /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/repvit.py | 38,40 | 30,59 | fuse | ConvNorm | 40,59 | 40,59 | class ConvNorm(nn.Sequential):
def __init__(self, in_dim, out_dim, ks=1, stride=1, pad=0, dilation=1, groups=1, bn_weight_init=1):
super().__init__()
self.add_module('c', nn.Conv2d(in_dim, out_dim, ks, stride, pad, dilation, groups, bias=False))
self.add_module('bn', nn.BatchNorm2d(out_dim))... | class ConvNorm(nn.Sequential):
def __init__(self, in_dim, out_dim, ks=1, stride=1, pad=0, dilation=1, groups=1, bn_weight_init=1):
super().__init__()
self.add_module('c', nn.Conv2d(in_dim, out_dim, ks, stride, pad, dilation, groups, bias=False))
self.add_module('bn', nn.BatchNorm2d(out_dim))... | c, bn = self._modules.values()
w = bn.weight / (bn.running_var + bn.eps) ** 0.5
w = c.weight * w[:, None, None, None]
b = bn.bias - bn.running_mean * bn.weight / (bn.running_var + bn.eps) ** 0.5
m = nn.Conv2d(
w.size(1) * self.c.groups,
w.size(0),
... | c, bn = self._modules.values()
w = bn.weight / (bn.running_var + bn.eps) ** 0.5
w = c.weight * w[:, None, None, None]
b = bn.bias - bn.running_mean * bn.weight / (bn.running_var + bn.eps) ** 0.5
m = nn.Conv2d(
w.size(1) * self.c.groups,
w.size(0),
... | 52 | 202 | 246 | 448 | """ RepViT
Paper: `RepViT: Revisiting Mobile CNN From ViT Perspective`
- https://arxiv.org/abs/2307.09283
@misc{wang2023repvit,
title={RepViT: Revisiting Mobile CNN From ViT Perspective},
author={Ao Wang and Hui Chen and Zijia Lin and Hengjun Pu and Guiguang Ding},
year={2023},
eprint={23... | """ RepViT
Paper: `RepViT: Revisiting Mobile CNN From ViT Perspective`
- https://arxiv.org/abs/2307.09283
@misc{wang2023repvit,
title={RepViT: Revisiting Mobile CNN From ViT Perspective},
author={Ao Wang and Hui Chen and Zijia Lin and Hengjun Pu and Guiguang Ding},
year={2023},
eprint={23... |
17,824 | /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/repvit.py | 68,70 | 59,84 | fuse | NormLinear | 70,84 | 70,84 | class NormLinear(nn.Sequential):
def __init__(self, in_dim, out_dim, bias=True, std=0.02):
super().__init__()
self.add_module('bn', nn.BatchNorm1d(in_dim))
self.add_module('l', nn.Linear(in_dim, out_dim, bias=bias))
trunc_normal_(self.l.weight, std=std)
if bias:
n... | class NormLinear(nn.Sequential):
def __init__(self, in_dim, out_dim, bias=True, std=0.02):
super().__init__()
self.add_module('bn', nn.BatchNorm1d(in_dim))
self.add_module('l', nn.Linear(in_dim, out_dim, bias=bias))
trunc_normal_(self.l.weight, std=std)
if bias:
n... | bn, l = self._modules.values()
w = bn.weight / (bn.running_var + bn.eps) ** 0.5
b = bn.bias - self.bn.running_mean * self.bn.weight / (bn.running_var + bn.eps) ** 0.5
w = l.weight * w[None, :]
if l.bias is None:
b = b @ self.l.weight.T
else:
b = (l... | bn, l = self._modules.values()
w = bn.weight / (bn.running_var + bn.eps) ** 0.5
b = bn.bias - self.bn.running_mean * self.bn.weight / (bn.running_var + bn.eps) ** 0.5
w = l.weight * w[None, :]
if l.bias is None:
b = b @ self.l.weight.T
else:
b = (l... | 59 | 164 | 221 | 385 | """ RepViT
Paper: `RepViT: Revisiting Mobile CNN From ViT Perspective`
- https://arxiv.org/abs/2307.09283
@misc{wang2023repvit,
title={RepViT: Revisiting Mobile CNN From ViT Perspective},
author={Ao Wang and Hui Chen and Zijia Lin and Hengjun Pu and Guiguang Ding},
year={2023},
eprint={23... | """ RepViT
Paper: `RepViT: Revisiting Mobile CNN From ViT Perspective`
- https://arxiv.org/abs/2307.09283
@misc{wang2023repvit,
title={RepViT: Revisiting Mobile CNN From ViT Perspective},
author={Ao Wang and Hui Chen and Zijia Lin and Hengjun Pu and Guiguang Ding},
year={2023},
eprint={23... |
17,825 | /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/repvit.py | 101,103 | 84,137 | fuse | RepVggDw | 103,137 | 103,137 | class RepVggDw(nn.Module):
def __init__(self, ed, kernel_size, legacy=False):
super().__init__()
self.conv = ConvNorm(ed, ed, kernel_size, 1, (kernel_size - 1) // 2, groups=ed)
if legacy:
self.conv1 = ConvNorm(ed, ed, 1, 1, 0, groups=ed)
# Make torchscript happy.
... | class RepVggDw(nn.Module):
def __init__(self, ed, kernel_size, legacy=False):
super().__init__()
self.conv = ConvNorm(ed, ed, kernel_size, 1, (kernel_size - 1) // 2, groups=ed)
if legacy:
self.conv1 = ConvNorm(ed, ed, 1, 1, 0, groups=ed)
# Make torchscript happy.
... | conv = self.conv.fuse()
if self.legacy:
conv1 = self.conv1.fuse()
else:
conv1 = self.conv1
conv_w = conv.weight
conv_b = conv.bias
conv1_w = conv1.weight
conv1_b = conv1.bias
conv1_w = nn.functional.pad(conv1_w, [1, 1, 1, 1])
... | conv = self.conv.fuse()
if self.legacy:
conv1 = self.conv1.fuse()
else:
conv1 = self.conv1
conv_w = conv.weight
conv_b = conv.bias
conv1_w = conv1.weight
conv1_b = conv1.bias
conv1_w = nn.functional.pad(conv1_w, [1, 1, 1, 1])
... | 100 | 257 | 431 | 688 | """ RepViT
Paper: `RepViT: Revisiting Mobile CNN From ViT Perspective`
- https://arxiv.org/abs/2307.09283
@misc{wang2023repvit,
title={RepViT: Revisiting Mobile CNN From ViT Perspective},
author={Ao Wang and Hui Chen and Zijia Lin and Hengjun Pu and Guiguang Ding},
year={2023},
eprint={23... | """ RepViT
Paper: `RepViT: Revisiting Mobile CNN From ViT Perspective`
- https://arxiv.org/abs/2307.09283
@misc{wang2023repvit,
title={RepViT: Revisiting Mobile CNN From ViT Perspective},
author={Ao Wang and Hui Chen and Zijia Lin and Hengjun Pu and Guiguang Ding},
year={2023},
eprint={23... |
17,826 | /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/repvit.py | 156,157 | 148,164 | forward | RepViTBlock | 157,164 | 157,164 | class RepViTBlock(nn.Module):
def __init__(self, in_dim, mlp_ratio, kernel_size, use_se, act_layer, legacy=False):
super(RepViTBlock, self).__init__()
self.token_mixer = RepVggDw(in_dim, kernel_size, legacy)
self.se = SqueezeExcite(in_dim, 0.25) if use_se else nn.Identity()
self.cha... | class RepViTBlock(nn.Module):
def __init__(self, in_dim, mlp_ratio, kernel_size, use_se, act_layer, legacy=False):
super(RepViTBlock, self).__init__()
self.token_mixer = RepVggDw(in_dim, kernel_size, legacy)
self.se = SqueezeExcite(in_dim, 0.25) if use_se else nn.Identity()
self.cha... | x = self.token_mixer(x)
x = self.se(x)
identity = x
x = self.channel_mixer(x)
return identity + x
| x = self.token_mixer(x)
x = self.se(x)
identity = x
x = self.channel_mixer(x)
return identity + x
| 16 | 172 | 56 | 228 | """ RepViT
Paper: `RepViT: Revisiting Mobile CNN From ViT Perspective`
- https://arxiv.org/abs/2307.09283
@misc{wang2023repvit,
title={RepViT: Revisiting Mobile CNN From ViT Perspective},
author={Ao Wang and Hui Chen and Zijia Lin and Hengjun Pu and Guiguang Ding},
year={2023},
eprint={23... | """ RepViT
Paper: `RepViT: Revisiting Mobile CNN From ViT Perspective`
- https://arxiv.org/abs/2307.09283
@misc{wang2023repvit,
title={RepViT: Revisiting Mobile CNN From ViT Perspective},
author={Ao Wang and Hui Chen and Zijia Lin and Hengjun Pu and Guiguang Ding},
year={2023},
eprint={23... |
17,827 | /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/repvit.py | 345,346 | 255,370 | forward_head | RepVit | 346,351 | 346,351 | class RepVit(nn.Module):
def __init__(
self,
in_chans=3,
img_size=224,
embed_dim=(48,),
depth=(2,),
mlp_ratio=2,
global_pool='avg',
kernel_size=3,
num_classes=1000,
act_layer=nn.GELU,
distillation=True,
drop_rate=0.0,
... | class RepVit(nn.Module):
def __init__(
self,
in_chans=3,
img_size=224,
embed_dim=(48,),
depth=(2,),
mlp_ratio=2,
global_pool='avg',
kernel_size=3,
num_classes=1000,
act_layer=nn.GELU,
distillation=True,
drop_rate=0.0,
... | if self.global_pool == 'avg':
x = x.mean((2, 3), keepdim=False)
x = self.head_drop(x)
return self.head(x)
| if self.global_pool == 'avg':
x = x.mean((2, 3), keepdim=False)
x = self.head_drop(x)
return self.head(x)
| 14 | 1,175 | 58 | 1,233 | """ RepViT
Paper: `RepViT: Revisiting Mobile CNN From ViT Perspective`
- https://arxiv.org/abs/2307.09283
@misc{wang2023repvit,
title={RepViT: Revisiting Mobile CNN From ViT Perspective},
author={Ao Wang and Hui Chen and Zijia Lin and Hengjun Pu and Guiguang Ding},
year={2023},
eprint={23... | """ RepViT
Paper: `RepViT: Revisiting Mobile CNN From ViT Perspective`
- https://arxiv.org/abs/2307.09283
@misc{wang2023repvit,
title={RepViT: Revisiting Mobile CNN From ViT Perspective},
author={Ao Wang and Hui Chen and Zijia Lin and Hengjun Pu and Guiguang Ding},
year={2023},
eprint={23... |
17,828 | /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/repvit.py | 216,218 | 193,232 | fuse | RepVitClassifier | 218,232 | 218,232 | class RepVitClassifier(nn.Module):
def __init__(self, dim, num_classes, distillation=False, drop=0.0):
super().__init__()
self.head_drop = nn.Dropout(drop)
self.head = NormLinear(dim, num_classes) if num_classes > 0 else nn.Identity()
self.distillation = distillation
self.dis... | class RepVitClassifier(nn.Module):
def __init__(self, dim, num_classes, distillation=False, drop=0.0):
super().__init__()
self.head_drop = nn.Dropout(drop)
self.head = NormLinear(dim, num_classes) if num_classes > 0 else nn.Identity()
self.distillation = distillation
self.dis... | if not self.num_classes > 0:
return nn.Identity()
head = self.head.fuse()
if self.distillation:
head_dist = self.head_dist.fuse()
head.weight += head_dist.weight
head.bias += head_dist.bias
head.weight /= 2
head.bias /= 2
... | if not self.num_classes > 0:
return nn.Identity()
head = self.head.fuse()
if self.distillation:
head_dist = self.head_dist.fuse()
head.weight += head_dist.weight
head.bias += head_dist.bias
head.weight /= 2
head.bias /= 2
... | 32 | 307 | 124 | 431 | """ RepViT
Paper: `RepViT: Revisiting Mobile CNN From ViT Perspective`
- https://arxiv.org/abs/2307.09283
@misc{wang2023repvit,
title={RepViT: Revisiting Mobile CNN From ViT Perspective},
author={Ao Wang and Hui Chen and Zijia Lin and Hengjun Pu and Guiguang Ding},
year={2023},
eprint={23... | """ RepViT
Paper: `RepViT: Revisiting Mobile CNN From ViT Perspective`
- https://arxiv.org/abs/2307.09283
@misc{wang2023repvit,
title={RepViT: Revisiting Mobile CNN From ViT Perspective},
author={Ao Wang and Hui Chen and Zijia Lin and Hengjun Pu and Guiguang Ding},
year={2023},
eprint={23... |
17,829 | /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/repvit.py | 184,185 | 176,193 | forward | RepVitDownsample | 185,193 | 185,193 | class RepVitDownsample(nn.Module):
def __init__(self, in_dim, mlp_ratio, out_dim, kernel_size, act_layer, legacy=False):
super().__init__()
self.pre_block = RepViTBlock(in_dim, mlp_ratio, kernel_size, use_se=False, act_layer=act_layer, legacy=legacy)
self.spatial_downsample = ConvNorm(in_dim... | class RepVitDownsample(nn.Module):
def __init__(self, in_dim, mlp_ratio, out_dim, kernel_size, act_layer, legacy=False):
super().__init__()
self.pre_block = RepViTBlock(in_dim, mlp_ratio, kernel_size, use_se=False, act_layer=act_layer, legacy=legacy)
self.spatial_downsample = ConvNorm(in_dim... | x = self.pre_block(x)
x = self.spatial_downsample(x)
x = self.channel_downsample(x)
identity = x
x = self.ffn(x)
return x + identity
| x = self.pre_block(x)
x = self.spatial_downsample(x)
x = self.channel_downsample(x)
identity = x
x = self.ffn(x)
return x + identity
| 19 | 223 | 71 | 294 | """ RepViT
Paper: `RepViT: Revisiting Mobile CNN From ViT Perspective`
- https://arxiv.org/abs/2307.09283
@misc{wang2023repvit,
title={RepViT: Revisiting Mobile CNN From ViT Perspective},
author={Ao Wang and Hui Chen and Zijia Lin and Hengjun Pu and Guiguang Ding},
year={2023},
eprint={23... | """ RepViT
Paper: `RepViT: Revisiting Mobile CNN From ViT Perspective`
- https://arxiv.org/abs/2307.09283
@misc{wang2023repvit,
title={RepViT: Revisiting Mobile CNN From ViT Perspective},
author={Ao Wang and Hui Chen and Zijia Lin and Hengjun Pu and Guiguang Ding},
year={2023},
eprint={23... |
17,830 | /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/repvit.py | 144,145 | 137,148 | forward | RepVitMlp | 145,148 | 145,148 | class RepVitMlp(nn.Module):
def __init__(self, in_dim, hidden_dim, act_layer):
super().__init__()
self.conv1 = ConvNorm(in_dim, hidden_dim, 1, 1, 0)
self.act = act_layer()
self.conv2 = ConvNorm(hidden_dim, in_dim, 1, 1, 0, bn_weight_init=0)
def forward(self, x):
FILL_FUNC_BODY | class RepVitMlp(nn.Module):
def __init__(self, in_dim, hidden_dim, act_layer):
super().__init__()
self.conv1 = ConvNorm(in_dim, hidden_dim, 1, 1, 0)
self.act = act_layer()
self.conv2 = ConvNorm(hidden_dim, in_dim, 1, 1, 0, bn_weight_init=0)
def forward(self, x):
FILL_FUNC_BODY | return self.conv2(self.act(self.conv1(x)))
| return self.conv2(self.act(self.conv1(x)))
| 2 | 135 | 24 | 159 | """ RepViT
Paper: `RepViT: Revisiting Mobile CNN From ViT Perspective`
- https://arxiv.org/abs/2307.09283
@misc{wang2023repvit,
title={RepViT: Revisiting Mobile CNN From ViT Perspective},
author={Ao Wang and Hui Chen and Zijia Lin and Hengjun Pu and Guiguang Ding},
year={2023},
eprint={23... | """ RepViT
Paper: `RepViT: Revisiting Mobile CNN From ViT Perspective`
- https://arxiv.org/abs/2307.09283
@misc{wang2023repvit,
title={RepViT: Revisiting Mobile CNN From ViT Perspective},
author={Ao Wang and Hui Chen and Zijia Lin and Hengjun Pu and Guiguang Ding},
year={2023},
eprint={23... |
17,831 | /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/repvit.py | 249,250 | 232,255 | forward | RepVitStage | 250,255 | 250,255 | class RepVitStage(nn.Module):
def __init__(self, in_dim, out_dim, depth, mlp_ratio, act_layer, kernel_size=3, downsample=True, legacy=False):
super().__init__()
if downsample:
self.downsample = RepVitDownsample(in_dim, mlp_ratio, out_dim, kernel_size, act_layer, legacy)
else:
... | class RepVitStage(nn.Module):
def __init__(self, in_dim, out_dim, depth, mlp_ratio, act_layer, kernel_size=3, downsample=True, legacy=False):
super().__init__()
if downsample:
self.downsample = RepVitDownsample(in_dim, mlp_ratio, out_dim, kernel_size, act_layer, legacy)
else:
... | x = self.downsample(x)
x = self.blocks(x)
return x
| x = self.downsample(x)
x = self.blocks(x)
return x
| 8 | 233 | 32 | 265 | """ RepViT
Paper: `RepViT: Revisiting Mobile CNN From ViT Perspective`
- https://arxiv.org/abs/2307.09283
@misc{wang2023repvit,
title={RepViT: Revisiting Mobile CNN From ViT Perspective},
author={Ao Wang and Hui Chen and Zijia Lin and Hengjun Pu and Guiguang Ding},
year={2023},
eprint={23... | """ RepViT
Paper: `RepViT: Revisiting Mobile CNN From ViT Perspective`
- https://arxiv.org/abs/2307.09283
@misc{wang2023repvit,
title={RepViT: Revisiting Mobile CNN From ViT Perspective},
author={Ao Wang and Hui Chen and Zijia Lin and Hengjun Pu and Guiguang Ding},
year={2023},
eprint={23... |
17,832 | /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/repvit.py | 172,173 | 164,176 | forward | RepVitStem | 173,176 | 173,176 | class RepVitStem(nn.Module):
def __init__(self, in_chs, out_chs, act_layer):
super().__init__()
self.conv1 = ConvNorm(in_chs, out_chs // 2, 3, 2, 1)
self.act1 = act_layer()
self.conv2 = ConvNorm(out_chs // 2, out_chs, 3, 2, 1)
self.stride = 4
def forward(self, x):
FILL_F... | class RepVitStem(nn.Module):
def __init__(self, in_chs, out_chs, act_layer):
super().__init__()
self.conv1 = ConvNorm(in_chs, out_chs // 2, 3, 2, 1)
self.act1 = act_layer()
self.conv2 = ConvNorm(out_chs // 2, out_chs, 3, 2, 1)
self.stride = 4
def forward(self, x):
FILL_F... | return self.conv2(self.act1(self.conv1(x)))
| return self.conv2(self.act1(self.conv1(x)))
| 2 | 147 | 25 | 172 | """ RepViT
Paper: `RepViT: Revisiting Mobile CNN From ViT Perspective`
- https://arxiv.org/abs/2307.09283
@misc{wang2023repvit,
title={RepViT: Revisiting Mobile CNN From ViT Perspective},
author={Ao Wang and Hui Chen and Zijia Lin and Hengjun Pu and Guiguang Ding},
year={2023},
eprint={23... | """ RepViT
Paper: `RepViT: Revisiting Mobile CNN From ViT Perspective`
- https://arxiv.org/abs/2307.09283
@misc{wang2023repvit,
title={RepViT: Revisiting Mobile CNN From ViT Perspective},
author={Ao Wang and Hui Chen and Zijia Lin and Hengjun Pu and Guiguang Ding},
year={2023},
eprint={23... |
17,834 | /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/res2net.py | 78,79 | 18,119 | forward | Bottle2neck | 79,119 | 79,119 | class Bottle2neck(nn.Module):
""" Res2Net/Res2NeXT Bottleneck
Adapted from https://github.com/gasvn/Res2Net/blob/master/res2net.py
"""
expansion = 4
def __init__(
self,
inplanes,
planes,
stride=1,
downsample=None,
cardinality=1... | class Bottle2neck(nn.Module):
""" Res2Net/Res2NeXT Bottleneck
Adapted from https://github.com/gasvn/Res2Net/blob/master/res2net.py
"""
expansion = 4
def __init__(
self,
inplanes,
planes,
stride=1,
downsample=None,
cardinality=1... | shortcut = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
spx = torch.split(out, self.width, 1)
spo = []
sp = spx[0] # redundant, for torchscript
for i, (conv, bn) in enumerate(zip(self.convs, self.bns)):
if i == 0 or self.is... | shortcut = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
spx = torch.split(out, self.width, 1)
spo = []
sp = spx[0] # redundant, for torchscript
for i, (conv, bn) in enumerate(zip(self.convs, self.bns)):
if i == 0 or self.is... | 113 | 719 | 394 | 1,113 | """ Res2Net and Res2NeXt
Adapted from Official Pytorch impl at: https://github.com/gasvn/Res2Net/
Paper: `Res2Net: A New Multi-scale Backbone Architecture` - https://arxiv.org/abs/1904.01169
"""
import math
import torch
import torch.nn as nn
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from ._bui... | """ Res2Net and Res2NeXt
Adapted from Official Pytorch impl at: https://github.com/gasvn/Res2Net/
Paper: `Res2Net: A New Multi-scale Backbone Architecture` - https://arxiv.org/abs/1904.01169
"""
import math
import torch
import torch.nn as nn
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from ._bui... |
17,836 | /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/resnest.py | 91,92 | 18,120 | forward | ResNestBottleneck | 92,120 | 92,120 | class ResNestBottleneck(nn.Module):
"""ResNet Bottleneck
"""
# pylint: disable=unused-argument
expansion = 4
def __init__(
self,
inplanes,
planes,
stride=1,
downsample=None,
radix=1,
cardinality=1,
base_... | class ResNestBottleneck(nn.Module):
"""ResNet Bottleneck
"""
# pylint: disable=unused-argument
expansion = 4
def __init__(
self,
inplanes,
planes,
stride=1,
downsample=None,
radix=1,
cardinality=1,
base_... | shortcut = x
out = self.conv1(x)
out = self.bn1(out)
out = self.act1(out)
if self.avd_first is not None:
out = self.avd_first(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.drop_block(out)
out = self.act2(out)
if ... | shortcut = x
out = self.conv1(x)
out = self.bn1(out)
out = self.act1(out)
if self.avd_first is not None:
out = self.avd_first(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.drop_block(out)
out = self.act2(out)
if ... | 62 | 933 | 236 | 1,169 | """ ResNeSt Models
Paper: `ResNeSt: Split-Attention Networks` - https://arxiv.org/abs/2004.08955
Adapted from original PyTorch impl w/ weights at https://github.com/zhanghang1989/ResNeSt by Hang Zhang
Modified for torchscript compat, and consistency with timm by Ross Wightman
"""
from torch import nn
from timm.data... | """ ResNeSt Models
Paper: `ResNeSt: Split-Attention Networks` - https://arxiv.org/abs/2004.08955
Adapted from original PyTorch impl w/ weights at https://github.com/zhanghang1989/ResNeSt by Hang Zhang
Modified for torchscript compat, and consistency with timm by Ross Wightman
"""
from torch import nn
from timm.data... |
17,838 | /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/resnet.py | 115,116 | 43,141 | forward | BasicBlock | 116,141 | 116,141 | class BasicBlock(nn.Module):
expansion = 1
def __init__(
self,
inplanes: int,
planes: int,
stride: int = 1,
downsample: Optional[nn.Module] = None,
cardinality: int = 1,
base_width: int = 64,
reduce_first: int = 1,
... | class BasicBlock(nn.Module):
expansion = 1
def __init__(
self,
inplanes: int,
planes: int,
stride: int = 1,
downsample: Optional[nn.Module] = None,
cardinality: int = 1,
base_width: int = 64,
reduce_first: int = 1,
... | shortcut = x
x = self.conv1(x)
x = self.bn1(x)
x = self.drop_block(x)
x = self.act1(x)
x = self.aa(x)
x = self.conv2(x)
x = self.bn2(x)
if self.se is not None:
x = self.se(x)
if self.drop_path is not None:
x = se... | shortcut = x
x = self.conv1(x)
x = self.bn1(x)
x = self.drop_block(x)
x = self.act1(x)
x = self.aa(x)
x = self.conv2(x)
x = self.bn2(x)
if self.se is not None:
x = self.se(x)
if self.drop_path is not None:
x = se... | 56 | 923 | 202 | 1,125 | """PyTorch ResNet
This started as a copy of https://github.com/pytorch/vision 'resnet.py' (BSD-3-Clause) with
additional dropout and dynamic global avg/max pool.
ResNeXt, SE-ResNeXt, SENet, and MXNet Gluon stem/downsample variants, tiered stems added by Ross Wightman
Copyright 2019, Ross Wightman
"""
import math
fro... | """PyTorch ResNet
This started as a copy of https://github.com/pytorch/vision 'resnet.py' (BSD-3-Clause) with
additional dropout and dynamic global avg/max pool.
ResNeXt, SE-ResNeXt, SENet, and MXNet Gluon stem/downsample variants, tiered stems added by Ross Wightman
Copyright 2019, Ross Wightman
"""
import math
fro... |
17,839 | /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/resnet.py | 215,216 | 141,245 | forward | Bottleneck | 216,245 | 216,245 | class Bottleneck(nn.Module):
expansion = 4
def __init__(
self,
inplanes: int,
planes: int,
stride: int = 1,
downsample: Optional[nn.Module] = None,
cardinality: int = 1,
base_width: int = 64,
reduce_first: int = 1,
... | class Bottleneck(nn.Module):
expansion = 4
def __init__(
self,
inplanes: int,
planes: int,
stride: int = 1,
downsample: Optional[nn.Module] = None,
cardinality: int = 1,
base_width: int = 64,
reduce_first: int = 1,
... | shortcut = x
x = self.conv1(x)
x = self.bn1(x)
x = self.act1(x)
x = self.conv2(x)
x = self.bn2(x)
x = self.drop_block(x)
x = self.act2(x)
x = self.aa(x)
x = self.conv3(x)
x = self.bn3(x)
if self.se is not None:
... | shortcut = x
x = self.conv1(x)
x = self.bn1(x)
x = self.act1(x)
x = self.conv2(x)
x = self.bn2(x)
x = self.drop_block(x)
x = self.act2(x)
x = self.aa(x)
x = self.conv3(x)
x = self.bn3(x)
if self.se is not None:
... | 65 | 965 | 240 | 1,205 | """PyTorch ResNet
This started as a copy of https://github.com/pytorch/vision 'resnet.py' (BSD-3-Clause) with
additional dropout and dynamic global avg/max pool.
ResNeXt, SE-ResNeXt, SENet, and MXNet Gluon stem/downsample variants, tiered stems added by Ross Wightman
Copyright 2019, Ross Wightman
"""
import math
fro... | """PyTorch ResNet
This started as a copy of https://github.com/pytorch/vision 'resnet.py' (BSD-3-Clause) with
additional dropout and dynamic global avg/max pool.
ResNeXt, SE-ResNeXt, SENet, and MXNet Gluon stem/downsample variants, tiered stems added by Ross Wightman
Copyright 2019, Ross Wightman
"""
import math
fro... |
17,842 | /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/resnetv2.py | 156,157 | 114,178 | zero_init_last | Bottleneck | 157,160 | 157,160 | class Bottleneck(nn.Module):
"""Non Pre-activation bottleneck block, equiv to V1.5/V1b Bottleneck. Used for ViT.
"""
def __init__(
self,
in_chs,
out_chs=None,
bottle_ratio=0.25,
stride=1,
dilation=1,
first_dilation=None,
... | class Bottleneck(nn.Module):
"""Non Pre-activation bottleneck block, equiv to V1.5/V1b Bottleneck. Used for ViT.
"""
def __init__(
self,
in_chs,
out_chs=None,
bottle_ratio=0.25,
stride=1,
dilation=1,
first_dilation=None,
... | if getattr(self.norm3, 'weight', None) is not None:
nn.init.zeros_(self.norm3.weight)
| if getattr(self.norm3, 'weight', None) is not None:
nn.init.zeros_(self.norm3.weight)
| 8 | 718 | 41 | 759 | """Pre-Activation ResNet v2 with GroupNorm and Weight Standardization.
A PyTorch implementation of ResNetV2 adapted from the Google Big-Transfer (BiT) source code
at https://github.com/google-research/big_transfer to match timm interfaces. The BiT weights have
been included here as pretrained models from their origina... | """Pre-Activation ResNet v2 with GroupNorm and Weight Standardization.
A PyTorch implementation of ResNetV2 adapted from the Google Big-Transfer (BiT) source code
at https://github.com/google-research/big_transfer to match timm interfaces. The BiT weights have
been included here as pretrained models from their origina... |
17,843 | /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/resnetv2.py | 221,222 | 198,225 | forward | DownsampleAvg | 222,225 | 222,225 | class DownsampleAvg(nn.Module):
def __init__(
self,
in_chs,
out_chs,
stride=1,
dilation=1,
first_dilation=None,
preact=True,
conv_layer=None,
norm_layer=None,
):
""" AvgPool Downsampling as in 'D'... | class DownsampleAvg(nn.Module):
def __init__(
self,
in_chs,
out_chs,
stride=1,
dilation=1,
first_dilation=None,
preact=True,
conv_layer=None,
norm_layer=None,
):
""" AvgPool Downsampling as in 'D'... | return self.norm(self.conv(self.pool(x)))
| return self.norm(self.conv(self.pool(x)))
| 2 | 327 | 22 | 349 | """Pre-Activation ResNet v2 with GroupNorm and Weight Standardization.
A PyTorch implementation of ResNetV2 adapted from the Google Big-Transfer (BiT) source code
at https://github.com/google-research/big_transfer to match timm interfaces. The BiT weights have
been included here as pretrained models from their origina... | """Pre-Activation ResNet v2 with GroupNorm and Weight Standardization.
A PyTorch implementation of ResNetV2 adapted from the Google Big-Transfer (BiT) source code
at https://github.com/google-research/big_transfer to match timm interfaces. The BiT weights have
been included here as pretrained models from their origina... |
17,844 | /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/resnetv2.py | 194,195 | 178,198 | forward | DownsampleConv | 195,198 | 195,198 | class DownsampleConv(nn.Module):
def __init__(
self,
in_chs,
out_chs,
stride=1,
dilation=1,
first_dilation=None,
preact=True,
conv_layer=None,
norm_layer=None,
):
super(DownsampleConv, self).__ini... | class DownsampleConv(nn.Module):
def __init__(
self,
in_chs,
out_chs,
stride=1,
dilation=1,
first_dilation=None,
preact=True,
conv_layer=None,
norm_layer=None,
):
super(DownsampleConv, self).__ini... | return self.norm(self.conv(x))
| return self.norm(self.conv(x))
| 2 | 179 | 18 | 197 | """Pre-Activation ResNet v2 with GroupNorm and Weight Standardization.
A PyTorch implementation of ResNetV2 adapted from the Google Big-Transfer (BiT) source code
at https://github.com/google-research/big_transfer to match timm interfaces. The BiT weights have
been included here as pretrained models from their origina... | """Pre-Activation ResNet v2 with GroupNorm and Weight Standardization.
A PyTorch implementation of ResNetV2 adapted from the Google Big-Transfer (BiT) source code
at https://github.com/google-research/big_transfer to match timm interfaces. The BiT weights have
been included here as pretrained models from their origina... |
17,845 | /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/resnetv2.py | 95,96 | 49,114 | zero_init_last | PreActBottleneck | 96,98 | 96,98 | class PreActBottleneck(nn.Module):
"""Pre-activation (v2) bottleneck block.
Follows the implementation of "Identity Mappings in Deep Residual Networks":
https://github.com/KaimingHe/resnet-1k-layers/blob/master/resnet-pre-act.lua
Except it puts the stride on 3x3 conv when available.
"""
def _... | class PreActBottleneck(nn.Module):
"""Pre-activation (v2) bottleneck block.
Follows the implementation of "Identity Mappings in Deep Residual Networks":
https://github.com/KaimingHe/resnet-1k-layers/blob/master/resnet-pre-act.lua
Except it puts the stride on 3x3 conv when available.
"""
def _... | nn.init.zeros_(self.conv3.weight)
| nn.init.zeros_(self.conv3.weight)
| 1 | 734 | 20 | 754 | """Pre-Activation ResNet v2 with GroupNorm and Weight Standardization.
A PyTorch implementation of ResNetV2 adapted from the Google Big-Transfer (BiT) source code
at https://github.com/google-research/big_transfer to match timm interfaces. The BiT weights have
been included here as pretrained models from their origina... | """Pre-Activation ResNet v2 with GroupNorm and Weight Standardization.
A PyTorch implementation of ResNetV2 adapted from the Google Big-Transfer (BiT) source code
at https://github.com/google-research/big_transfer to match timm interfaces. The BiT weights have
been included here as pretrained models from their origina... |
17,846 | /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/resnetv2.py | 270,271 | 225,275 | forward | ResNetStage | 271,275 | 271,275 | class ResNetStage(nn.Module):
"""ResNet Stage."""
def __init__(
self,
in_chs,
out_chs,
stride,
dilation,
depth,
bottle_ratio=0.25,
groups=1,
avg_down=False,
block_dpr=None,
block_f... | class ResNetStage(nn.Module):
"""ResNet Stage."""
def __init__(
self,
in_chs,
out_chs,
stride,
dilation,
depth,
bottle_ratio=0.25,
groups=1,
avg_down=False,
block_dpr=None,
block_f... | x = self.blocks(x)
return x
| x = self.blocks(x)
return x
| 5 | 477 | 20 | 497 | """Pre-Activation ResNet v2 with GroupNorm and Weight Standardization.
A PyTorch implementation of ResNetV2 adapted from the Google Big-Transfer (BiT) source code
at https://github.com/google-research/big_transfer to match timm interfaces. The BiT weights have
been included here as pretrained models from their origina... | """Pre-Activation ResNet v2 with GroupNorm and Weight Standardization.
A PyTorch implementation of ResNetV2 adapted from the Google Big-Transfer (BiT) source code
at https://github.com/google-research/big_transfer to match timm interfaces. The BiT weights have
been included here as pretrained models from their origina... |
17,847 | /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/resnetv2.py | 458,459 | 324,480 | reset_classifier | ResNetV2 | 459,462 | 459,462 | class ResNetV2(nn.Module):
"""Implementation of Pre-activation (v2) ResNet mode.
"""
def __init__(
self,
layers,
channels=(256, 512, 1024, 2048),
num_classes=1000,
in_chans=3,
global_pool='avg',
output_stride=32,
... | class ResNetV2(nn.Module):
"""Implementation of Pre-activation (v2) ResNet mode.
"""
def __init__(
self,
layers,
channels=(256, 512, 1024, 2048),
num_classes=1000,
in_chans=3,
global_pool='avg',
output_stride=32,
... | self.num_classes = num_classes
self.head.reset(num_classes, global_pool)
| self.num_classes = num_classes
self.head.reset(num_classes, global_pool)
| 5 | 1,864 | 31 | 1,895 | """Pre-Activation ResNet v2 with GroupNorm and Weight Standardization.
A PyTorch implementation of ResNetV2 adapted from the Google Big-Transfer (BiT) source code
at https://github.com/google-research/big_transfer to match timm interfaces. The BiT weights have
been included here as pretrained models from their origina... | """Pre-Activation ResNet v2 with GroupNorm and Weight Standardization.
A PyTorch implementation of ResNetV2 adapted from the Google Big-Transfer (BiT) source code
at https://github.com/google-research/big_transfer to match timm interfaces. The BiT weights have
been included here as pretrained models from their origina... |
17,849 | /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/rexnet.py | 79,80 | 32,95 | forward | LinearBottleneck | 80,95 | 80,95 | class LinearBottleneck(nn.Module):
def __init__(
self,
in_chs,
out_chs,
stride,
dilation=(1, 1),
exp_ratio=1.0,
se_ratio=0.,
ch_div=1,
act_layer='swish',
dw_act_layer='relu6',
drop_pat... | class LinearBottleneck(nn.Module):
def __init__(
self,
in_chs,
out_chs,
stride,
dilation=(1, 1),
exp_ratio=1.0,
se_ratio=0.,
ch_div=1,
act_layer='swish',
dw_act_layer='relu6',
drop_pat... | shortcut = x
if self.conv_exp is not None:
x = self.conv_exp(x)
x = self.conv_dw(x)
if self.se is not None:
x = self.se(x)
x = self.act_dw(x)
x = self.conv_pwl(x)
if self.use_shortcut:
if self.drop_path is not None:
... | shortcut = x
if self.conv_exp is not None:
x = self.conv_exp(x)
x = self.conv_dw(x)
if self.se is not None:
x = self.se(x)
x = self.act_dw(x)
x = self.conv_pwl(x)
if self.use_shortcut:
if self.drop_path is not None:
... | 49 | 554 | 183 | 737 | """ ReXNet
A PyTorch impl of `ReXNet: Diminishing Representational Bottleneck on Convolutional Neural Network` -
https://arxiv.org/abs/2007.00992
Adapted from original impl at https://github.com/clovaai/rexnet
Copyright (c) 2020-present NAVER Corp. MIT license
Changes for timm, feature extraction, and rounded channe... | """ ReXNet
A PyTorch impl of `ReXNet: Diminishing Representational Bottleneck on Convolutional Neural Network` -
https://arxiv.org/abs/2007.00992
Adapted from original impl at https://github.com/clovaai/rexnet
Copyright (c) 2020-present NAVER Corp. MIT license
Changes for timm, feature extraction, and rounded channe... |
17,850 | /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/rexnet.py | 232,233 | 170,252 | reset_classifier | RexNet | 233,235 | 233,235 | class RexNet(nn.Module):
def __init__(
self,
in_chans=3,
num_classes=1000,
global_pool='avg',
output_stride=32,
initial_chs=16,
final_chs=180,
width_mult=1.0,
depth_mult=1.0,
se_ratio=1/12.,
... | class RexNet(nn.Module):
def __init__(
self,
in_chans=3,
num_classes=1000,
global_pool='avg',
output_stride=32,
initial_chs=16,
final_chs=180,
width_mult=1.0,
depth_mult=1.0,
se_ratio=1/12.,
... | self.head = ClassifierHead(self.num_features, num_classes, pool_type=global_pool, drop_rate=self.drop_rate)
| self.head = ClassifierHead(self.num_features, num_classes, pool_type=global_pool, drop_rate=self.drop_rate)
| 6 | 857 | 41 | 898 | """ ReXNet
A PyTorch impl of `ReXNet: Diminishing Representational Bottleneck on Convolutional Neural Network` -
https://arxiv.org/abs/2007.00992
Adapted from original impl at https://github.com/clovaai/rexnet
Copyright (c) 2020-present NAVER Corp. MIT license
Changes for timm, feature extraction, and rounded channe... | """ ReXNet
A PyTorch impl of `ReXNet: Diminishing Representational Bottleneck on Convolutional Neural Network` -
https://arxiv.org/abs/2007.00992
Adapted from original impl at https://github.com/clovaai/rexnet
Copyright (c) 2020-present NAVER Corp. MIT license
Changes for timm, feature extraction, and rounded channe... |
17,852 | /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/senet.py | 61,62 | 56,84 | forward | Bottleneck | 62,84 | 62,84 | class Bottleneck(nn.Module):
"""
Base class for bottlenecks that implements `forward()` method.
"""
def forward(self, x):
FILL_FUNC_BODY | class Bottleneck(nn.Module):
"""
Base class for bottlenecks that implements `forward()` method.
"""
def forward(self, x):
FILL_FUNC_BODY | shortcut = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
shortcut = se... | shortcut = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
shortcut = se... | 45 | 55 | 174 | 229 | """
SEResNet implementation from Cadene's pretrained models
https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/senet.py
Additional credit to https://github.com/creafz
Original model: https://github.com/hujie-frank/SENet
ResNet code gently borrowed from
https://github.com/pytorch/v... | """
SEResNet implementation from Cadene's pretrained models
https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/senet.py
Additional credit to https://github.com/creafz
Original model: https://github.com/hujie-frank/SENet
ResNet code gently borrowed from
https://github.com/pytorch/v... |
17,853 | /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/senet.py | 46,47 | 37,56 | forward | SEModule | 47,56 | 47,56 | class SEModule(nn.Module):
def __init__(self, channels, reduction):
super(SEModule, self).__init__()
self.fc1 = nn.Conv2d(channels, channels // reduction, kernel_size=1)
self.relu = nn.ReLU(inplace=True)
self.fc2 = nn.Conv2d(channels // reduction, channels, kernel_size=1)
se... | class SEModule(nn.Module):
def __init__(self, channels, reduction):
super(SEModule, self).__init__()
self.fc1 = nn.Conv2d(channels, channels // reduction, kernel_size=1)
self.relu = nn.ReLU(inplace=True)
self.fc2 = nn.Conv2d(channels // reduction, channels, kernel_size=1)
se... | module_input = x
x = x.mean((2, 3), keepdim=True)
x = self.fc1(x)
x = self.relu(x)
x = self.fc2(x)
x = self.sigmoid(x)
return module_input * x
| module_input = x
x = x.mean((2, 3), keepdim=True)
x = self.fc1(x)
x = self.relu(x)
x = self.fc2(x)
x = self.sigmoid(x)
return module_input * x
| 24 | 145 | 89 | 234 | """
SEResNet implementation from Cadene's pretrained models
https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/senet.py
Additional credit to https://github.com/creafz
Original model: https://github.com/hujie-frank/SENet
ResNet code gently borrowed from
https://github.com/pytorch/v... | """
SEResNet implementation from Cadene's pretrained models
https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/senet.py
Additional credit to https://github.com/creafz
Original model: https://github.com/hujie-frank/SENet
ResNet code gently borrowed from
https://github.com/pytorch/v... |
17,855 | /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/senet.py | 163,164 | 149,183 | forward | SEResNetBlock | 164,183 | 164,183 | class SEResNetBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, groups, reduction, stride=1, downsample=None):
super(SEResNetBlock, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3, padding=1, stride=stride, bias=False)
self.bn1 = nn.BatchNorm2d... | class SEResNetBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, groups, reduction, stride=1, downsample=None):
super(SEResNetBlock, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3, padding=1, stride=stride, bias=False)
self.bn1 = nn.BatchNorm2d... | shortcut = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
if self.downsample is not None:
shortcut = self.downsample(x)
out = self.se_module(out) + short... | shortcut = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
if self.downsample is not None:
shortcut = self.downsample(x)
out = self.se_module(out) + short... | 39 | 251 | 148 | 399 | """
SEResNet implementation from Cadene's pretrained models
https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/senet.py
Additional credit to https://github.com/creafz
Original model: https://github.com/hujie-frank/SENet
ResNet code gently borrowed from
https://github.com/pytorch/v... | """
SEResNet implementation from Cadene's pretrained models
https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/senet.py
Additional credit to https://github.com/creafz
Original model: https://github.com/hujie-frank/SENet
ResNet code gently borrowed from
https://github.com/pytorch/v... |
17,857 | /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/sequencer.py | 250,251 | 245,257 | forward | Downsample2d | 251,257 | 251,257 | class Downsample2d(nn.Module):
def __init__(self, input_dim, output_dim, patch_size):
super().__init__()
self.down = nn.Conv2d(input_dim, output_dim, kernel_size=patch_size, stride=patch_size)
def forward(self, x):
FILL_FUNC_BODY | class Downsample2d(nn.Module):
def __init__(self, input_dim, output_dim, patch_size):
super().__init__()
self.down = nn.Conv2d(input_dim, output_dim, kernel_size=patch_size, stride=patch_size)
def forward(self, x):
FILL_FUNC_BODY | x = x.permute(0, 3, 1, 2)
x = self.down(x)
x = x.permute(0, 2, 3, 1)
return x
| x = x.permute(0, 3, 1, 2)
x = self.down(x)
x = x.permute(0, 2, 3, 1)
return x
| 17 | 96 | 64 | 160 | """ Sequencer
Paper: `Sequencer: Deep LSTM for Image Classification` - https://arxiv.org/pdf/2205.01972.pdf
"""
# Copyright (c) 2022. Yuki Tatsunami
# Licensed under the Apache License, Version 2.0 (the "License");
import math
from functools import partial
from itertools import accumulate
from typing import Tuple
... | """ Sequencer
Paper: `Sequencer: Deep LSTM for Image Classification` - https://arxiv.org/pdf/2205.01972.pdf
"""
# Copyright (c) 2022. Yuki Tatsunami
# Licensed under the Apache License, Version 2.0 (the "License");
import math
from functools import partial
from itertools import accumulate
from typing import Tuple
... |
17,858 | /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/sequencer.py | 126,127 | 67,161 | forward | RNN2dBase | 127,161 | 127,161 | class RNN2dBase(nn.Module):
def __init__(
self,
input_size: int,
hidden_size: int,
num_layers: int = 1,
bias: bool = True,
bidirectional: bool = True,
union="cat",
with_fc=True,
):
super().__init__()
... | class RNN2dBase(nn.Module):
def __init__(
self,
input_size: int,
hidden_size: int,
num_layers: int = 1,
bias: bool = True,
bidirectional: bool = True,
union="cat",
with_fc=True,
):
super().__init__()
... | B, H, W, C = x.shape
if self.with_vertical:
v = x.permute(0, 2, 1, 3)
v = v.reshape(-1, H, C)
v, _ = self.rnn_v(v)
v = v.reshape(B, W, H, -1)
v = v.permute(0, 2, 1, 3)
else:
v = None
if self.with_horizontal:
... | B, H, W, C = x.shape
if self.with_vertical:
v = x.permute(0, 2, 1, 3)
v = v.reshape(-1, H, C)
v, _ = self.rnn_v(v)
v = v.reshape(B, W, H, -1)
v = v.permute(0, 2, 1, 3)
else:
v = None
if self.with_horizontal:
... | 111 | 648 | 330 | 978 | """ Sequencer
Paper: `Sequencer: Deep LSTM for Image Classification` - https://arxiv.org/pdf/2205.01972.pdf
"""
# Copyright (c) 2022. Yuki Tatsunami
# Licensed under the Apache License, Version 2.0 (the "License");
import math
from functools import partial
from itertools import accumulate
from typing import Tuple
... | """ Sequencer
Paper: `Sequencer: Deep LSTM for Image Classification` - https://arxiv.org/pdf/2205.01972.pdf
"""
# Copyright (c) 2022. Yuki Tatsunami
# Licensed under the Apache License, Version 2.0 (the "License");
import math
from functools import partial
from itertools import accumulate
from typing import Tuple
... |
17,859 | /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/sequencer.py | 63,64 | 59,67 | forward | RNNIdentity | 64,67 | 64,67 | class RNNIdentity(nn.Module):
def __init__(self, *args, **kwargs):
super(RNNIdentity, self).__init__()
def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, None]:
FILL_FUNC_BODY | class RNNIdentity(nn.Module):
def __init__(self, *args, **kwargs):
super(RNNIdentity, self).__init__()
def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, None]:
FILL_FUNC_BODY | return x, None
| return x, None
| 3 | 77 | 11 | 88 | """ Sequencer
Paper: `Sequencer: Deep LSTM for Image Classification` - https://arxiv.org/pdf/2205.01972.pdf
"""
# Copyright (c) 2022. Yuki Tatsunami
# Licensed under the Apache License, Version 2.0 (the "License");
import math
from functools import partial
from itertools import accumulate
from typing import Tuple
... | """ Sequencer
Paper: `Sequencer: Deep LSTM for Image Classification` - https://arxiv.org/pdf/2205.01972.pdf
"""
# Copyright (c) 2022. Yuki Tatsunami
# Licensed under the Apache License, Version 2.0 (the "License");
import math
from functools import partial
from itertools import accumulate
from typing import Tuple
... |
17,860 | /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/sequencer.py | 435,436 | 311,441 | forward | Sequencer2d | 436,441 | 436,441 | class Sequencer2d(nn.Module):
def __init__(
self,
num_classes=1000,
img_size=224,
in_chans=3,
global_pool='avg',
layers=(4, 3, 8, 3),
patch_sizes=(7, 2, 2, 1),
embed_dims=(192, 384, 384, 384),
hidden_sizes=(4... | class Sequencer2d(nn.Module):
def __init__(
self,
num_classes=1000,
img_size=224,
in_chans=3,
global_pool='avg',
layers=(4, 3, 8, 3),
patch_sizes=(7, 2, 2, 1),
embed_dims=(192, 384, 384, 384),
hidden_sizes=(4... | 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 | 1,474 | 35 | 1,509 | """ Sequencer
Paper: `Sequencer: Deep LSTM for Image Classification` - https://arxiv.org/pdf/2205.01972.pdf
"""
# Copyright (c) 2022. Yuki Tatsunami
# Licensed under the Apache License, Version 2.0 (the "License");
import math
from functools import partial
from itertools import accumulate
from typing import Tuple
... | """ Sequencer
Paper: `Sequencer: Deep LSTM for Image Classification` - https://arxiv.org/pdf/2205.01972.pdf
"""
# Copyright (c) 2022. Yuki Tatsunami
# Licensed under the Apache License, Version 2.0 (the "License");
import math
from functools import partial
from itertools import accumulate
from typing import Tuple
... |
17,861 | /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/sequencer.py | 226,227 | 194,232 | forward | Sequencer2dBlock | 227,232 | 227,232 | class Sequencer2dBlock(nn.Module):
def __init__(
self,
dim,
hidden_size,
mlp_ratio=3.0,
rnn_layer=LSTM2d,
mlp_layer=Mlp,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
act_layer=nn.GELU,
num_layers=1,
... | class Sequencer2dBlock(nn.Module):
def __init__(
self,
dim,
hidden_size,
mlp_ratio=3.0,
rnn_layer=LSTM2d,
mlp_layer=Mlp,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
act_layer=nn.GELU,
num_layers=1,
... | x = x + self.drop_path(self.rnn_tokens(self.norm1(x)))
x = x + self.drop_path(self.mlp_channels(self.norm2(x)))
return x
| x = x + self.drop_path(self.rnn_tokens(self.norm1(x)))
x = x + self.drop_path(self.mlp_channels(self.norm2(x)))
return x
| 12 | 342 | 65 | 407 | """ Sequencer
Paper: `Sequencer: Deep LSTM for Image Classification` - https://arxiv.org/pdf/2205.01972.pdf
"""
# Copyright (c) 2022. Yuki Tatsunami
# Licensed under the Apache License, Version 2.0 (the "License");
import math
from functools import partial
from itertools import accumulate
from typing import Tuple
... | """ Sequencer
Paper: `Sequencer: Deep LSTM for Image Classification` - https://arxiv.org/pdf/2205.01972.pdf
"""
# Copyright (c) 2022. Yuki Tatsunami
# Licensed under the Apache License, Version 2.0 (the "License");
import math
from functools import partial
from itertools import accumulate
from typing import Tuple
... |
17,862 | /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/sequencer.py | 305,306 | 257,311 | forward | Sequencer2dStage | 306,311 | 306,311 | class Sequencer2dStage(nn.Module):
def __init__(
self,
dim,
dim_out,
depth,
patch_size,
hidden_size,
mlp_ratio,
downsample=False,
block_layer=Sequencer2dBlock,
rnn_layer=LSTM2d,
mlp_la... | class Sequencer2dStage(nn.Module):
def __init__(
self,
dim,
dim_out,
depth,
patch_size,
hidden_size,
mlp_ratio,
downsample=False,
block_layer=Sequencer2dBlock,
rnn_layer=LSTM2d,
mlp_la... | x = self.downsample(x)
x = self.blocks(x)
return x
| x = self.downsample(x)
x = self.blocks(x)
return x
| 8 | 449 | 32 | 481 | """ Sequencer
Paper: `Sequencer: Deep LSTM for Image Classification` - https://arxiv.org/pdf/2205.01972.pdf
"""
# Copyright (c) 2022. Yuki Tatsunami
# Licensed under the Apache License, Version 2.0 (the "License");
import math
from functools import partial
from itertools import accumulate
from typing import Tuple
... | """ Sequencer
Paper: `Sequencer: Deep LSTM for Image Classification` - https://arxiv.org/pdf/2205.01972.pdf
"""
# Copyright (c) 2022. Yuki Tatsunami
# Licensed under the Apache License, Version 2.0 (the "License");
import math
from functools import partial
from itertools import accumulate
from typing import Tuple
... |
17,863 | /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/sequencer.py | 236,237 | 232,245 | forward | Shuffle | 237,245 | 237,245 | class Shuffle(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
FILL_FUNC_BODY | class Shuffle(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
FILL_FUNC_BODY | if self.training:
B, H, W, C = x.shape
r = torch.randperm(H * W)
x = x.reshape(B, -1, C)
x = x[:, r, :].reshape(B, H, W, -1)
return x
| if self.training:
B, H, W, C = x.shape
r = torch.randperm(H * W)
x = x.reshape(B, -1, C)
x = x[:, r, :].reshape(B, H, W, -1)
return x
| 28 | 45 | 88 | 133 | """ Sequencer
Paper: `Sequencer: Deep LSTM for Image Classification` - https://arxiv.org/pdf/2205.01972.pdf
"""
# Copyright (c) 2022. Yuki Tatsunami
# Licensed under the Apache License, Version 2.0 (the "License");
import math
from functools import partial
from itertools import accumulate
from typing import Tuple
... | """ Sequencer
Paper: `Sequencer: Deep LSTM for Image Classification` - https://arxiv.org/pdf/2205.01972.pdf
"""
# Copyright (c) 2022. Yuki Tatsunami
# Licensed under the Apache License, Version 2.0 (the "License");
import math
from functools import partial
from itertools import accumulate
from typing import Tuple
... |
17,865 | /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/sknet.py | 68,69 | 22,83 | forward | SelectiveKernelBasic | 69,83 | 69,83 | class SelectiveKernelBasic(nn.Module):
expansion = 1
def __init__(
self,
inplanes,
planes,
stride=1,
downsample=None,
cardinality=1,
base_width=64,
sk_kwargs=None,
reduce_first=1,
dilation=1,... | class SelectiveKernelBasic(nn.Module):
expansion = 1
def __init__(
self,
inplanes,
planes,
stride=1,
downsample=None,
cardinality=1,
base_width=64,
sk_kwargs=None,
reduce_first=1,
dilation=1,... | shortcut = x
x = self.conv1(x)
x = self.conv2(x)
if self.se is not None:
x = self.se(x)
if self.drop_path is not None:
x = self.drop_path(x)
if self.downsample is not None:
shortcut = self.downsample(shortcut)
x += shortcut
... | shortcut = x
x = self.conv1(x)
x = self.conv2(x)
if self.se is not None:
x = self.se(x)
if self.drop_path is not None:
x = self.drop_path(x)
if self.downsample is not None:
shortcut = self.downsample(shortcut)
x += shortcut
... | 41 | 531 | 129 | 660 | """ Selective Kernel Networks (ResNet base)
Paper: Selective Kernel Networks (https://arxiv.org/abs/1903.06586)
This was inspired by reading 'Compounding the Performance Improvements...' (https://arxiv.org/abs/2001.06268)
and a streamlined impl at https://github.com/clovaai/assembled-cnn but I ended up building somet... | """ Selective Kernel Networks (ResNet base)
Paper: Selective Kernel Networks (https://arxiv.org/abs/1903.06586)
This was inspired by reading 'Compounding the Performance Improvements...' (https://arxiv.org/abs/2001.06268)
and a streamlined impl at https://github.com/clovaai/assembled-cnn but I ended up building somet... |
17,866 | /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/sknet.py | 128,129 | 83,144 | forward | SelectiveKernelBottleneck | 129,144 | 129,144 | class SelectiveKernelBottleneck(nn.Module):
expansion = 4
def __init__(
self,
inplanes,
planes,
stride=1,
downsample=None,
cardinality=1,
base_width=64,
sk_kwargs=None,
reduce_first=1,
dilati... | class SelectiveKernelBottleneck(nn.Module):
expansion = 4
def __init__(
self,
inplanes,
planes,
stride=1,
downsample=None,
cardinality=1,
base_width=64,
sk_kwargs=None,
reduce_first=1,
dilati... | shortcut = x
x = self.conv1(x)
x = self.conv2(x)
x = self.conv3(x)
if self.se is not None:
x = self.se(x)
if self.drop_path is not None:
x = self.drop_path(x)
if self.downsample is not None:
shortcut = self.downsample(shortcut)
... | shortcut = x
x = self.conv1(x)
x = self.conv2(x)
x = self.conv3(x)
if self.se is not None:
x = self.se(x)
if self.drop_path is not None:
x = self.drop_path(x)
if self.downsample is not None:
shortcut = self.downsample(shortcut)
... | 44 | 550 | 141 | 691 | """ Selective Kernel Networks (ResNet base)
Paper: Selective Kernel Networks (https://arxiv.org/abs/1903.06586)
This was inspired by reading 'Compounding the Performance Improvements...' (https://arxiv.org/abs/2001.06268)
and a streamlined impl at https://github.com/clovaai/assembled-cnn but I ended up building somet... | """ Selective Kernel Networks (ResNet base)
Paper: Selective Kernel Networks (https://arxiv.org/abs/1903.06586)
This was inspired by reading 'Compounding the Performance Improvements...' (https://arxiv.org/abs/2001.06268)
and a streamlined impl at https://github.com/clovaai/assembled-cnn but I ended up building somet... |
17,868 | /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/swin_transformer.py | 353,354 | 331,363 | forward | PatchMerging | 354,363 | 354,363 | class PatchMerging(nn.Module):
""" Patch Merging Layer.
"""
def __init__(
self,
dim: int,
out_dim: Optional[int] = None,
norm_layer: Callable = nn.LayerNorm,
):
"""
Args:
dim: Number of input channels.
out_dim: Numb... | class PatchMerging(nn.Module):
""" Patch Merging Layer.
"""
def __init__(
self,
dim: int,
out_dim: Optional[int] = None,
norm_layer: Callable = nn.LayerNorm,
):
"""
Args:
dim: Number of input channels.
out_dim: Numb... | B, H, W, C = x.shape
_assert(H % 2 == 0, f"x height ({H}) is not even.")
_assert(W % 2 == 0, f"x width ({W}) is not even.")
x = x.reshape(B, H // 2, 2, W // 2, 2, C).permute(0, 1, 3, 4, 2, 5).flatten(3)
x = self.norm(x)
x = self.reduction(x)
return x
| B, H, W, C = x.shape
_assert(H % 2 == 0, f"x height ({H}) is not even.")
_assert(W % 2 == 0, f"x width ({W}) is not even.")
x = x.reshape(B, H // 2, 2, W // 2, 2, C).permute(0, 1, 3, 4, 2, 5).flatten(3)
x = self.norm(x)
x = self.reduction(x)
return x
| 53 | 215 | 152 | 367 | """ Swin Transformer
A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`
- https://arxiv.org/pdf/2103.14030
Code/weights from https://github.com/microsoft/Swin-Transformer, original copyright/license info below
S3 (AutoFormerV2, https://arxiv.org/abs/2111.14725) Swin weig... | """ Swin Transformer
A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`
- https://arxiv.org/pdf/2103.14030
Code/weights from https://github.com/microsoft/Swin-Transformer, original copyright/license info below
S3 (AutoFormerV2, https://arxiv.org/abs/2111.14725) Swin weig... |
17,871 | /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/swin_transformer.py | 439,440 | 363,449 | forward | SwinTransformerStage | 440,449 | 440,449 | class SwinTransformerStage(nn.Module):
""" A basic Swin Transformer layer for one stage.
"""
def __init__(
self,
dim: int,
out_dim: int,
input_resolution: Tuple[int, int],
depth: int,
downsample: bool = True,
num_heads: int... | class SwinTransformerStage(nn.Module):
""" A basic Swin Transformer layer for one stage.
"""
def __init__(
self,
dim: int,
out_dim: int,
input_resolution: Tuple[int, int],
depth: int,
downsample: bool = True,
num_heads: int... | x = self.downsample(x)
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint_seq(self.blocks, x)
else:
x = self.blocks(x)
return x
| x = self.downsample(x)
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint_seq(self.blocks, x)
else:
x = self.blocks(x)
return x
| 18 | 814 | 79 | 893 | """ Swin Transformer
A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`
- https://arxiv.org/pdf/2103.14030
Code/weights from https://github.com/microsoft/Swin-Transformer, original copyright/license info below
S3 (AutoFormerV2, https://arxiv.org/abs/2111.14725) Swin weig... | """ Swin Transformer
A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`
- https://arxiv.org/pdf/2103.14030
Code/weights from https://github.com/microsoft/Swin-Transformer, original copyright/license info below
S3 (AutoFormerV2, https://arxiv.org/abs/2111.14725) Swin weig... |
17,872 | /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/swin_transformer.py | 149,150 | 92,188 | forward | WindowAttention | 155,188 | 150,188 | class WindowAttention(nn.Module):
""" Window based multi-head self attention (W-MSA) module with relative position bias.
It supports shifted and non-shifted windows.
"""
fused_attn: torch.jit.Final[bool]
def __init__(
self,
dim: int,
num_heads: int,
h... | class WindowAttention(nn.Module):
""" Window based multi-head self attention (W-MSA) module with relative position bias.
It supports shifted and non-shifted windows.
"""
fused_attn: torch.jit.Final[bool]
def __init__(
self,
dim: int,
num_heads: int,
h... | B_, N, C = x.shape
qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
q, k, v = qkv.unbind(0)
if self.fused_attn:
attn_mask = self._get_rel_pos_bias()
if mask is not None:
num_win = mask.shape[0]
mask = ... | """
Args:
x: input features with shape of (num_windows*B, N, C)
mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
"""
B_, N, C = x.shape
qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
q, k, v = qk... | 133 | 954 | 531 | 1,485 | """ Swin Transformer
A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`
- https://arxiv.org/pdf/2103.14030
Code/weights from https://github.com/microsoft/Swin-Transformer, original copyright/license info below
S3 (AutoFormerV2, https://arxiv.org/abs/2111.14725) Swin weig... | """ Swin Transformer
A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`
- https://arxiv.org/pdf/2103.14030
Code/weights from https://github.com/microsoft/Swin-Transformer, original copyright/license info below
S3 (AutoFormerV2, https://arxiv.org/abs/2111.14725) Swin weig... |
17,874 | /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/swin_transformer_v2.py | 349,350 | 332,359 | forward | PatchMerging | 350,359 | 350,359 | class PatchMerging(nn.Module):
""" Patch Merging Layer.
"""
def __init__(self, dim: int, out_dim: Optional[int] = None, norm_layer: nn.Module = nn.LayerNorm) -> None:
"""
Args:
dim (int): Number of input channels.
out_dim (int): Number of output channels (or 2 * dim ... | class PatchMerging(nn.Module):
""" Patch Merging Layer.
"""
def __init__(self, dim: int, out_dim: Optional[int] = None, norm_layer: nn.Module = nn.LayerNorm) -> None:
"""
Args:
dim (int): Number of input channels.
out_dim (int): Number of output channels (or 2 * dim ... | B, H, W, C = x.shape
_assert(H % 2 == 0, f"x height ({H}) is not even.")
_assert(W % 2 == 0, f"x width ({W}) is not even.")
x = x.reshape(B, H // 2, 2, W // 2, 2, C).permute(0, 1, 3, 4, 2, 5).flatten(3)
x = self.reduction(x)
x = self.norm(x)
return x
| B, H, W, C = x.shape
_assert(H % 2 == 0, f"x height ({H}) is not even.")
_assert(W % 2 == 0, f"x width ({W}) is not even.")
x = x.reshape(B, H // 2, 2, W // 2, 2, C).permute(0, 1, 3, 4, 2, 5).flatten(3)
x = self.reduction(x)
x = self.norm(x)
return x
| 53 | 243 | 152 | 395 | """ Swin Transformer V2
A PyTorch impl of : `Swin Transformer V2: Scaling Up Capacity and Resolution`
- https://arxiv.org/abs/2111.09883
Code/weights from https://github.com/microsoft/Swin-Transformer, original copyright/license info below
Modifications and additions for timm hacked together by / Copyright 2022, ... | """ Swin Transformer V2
A PyTorch impl of : `Swin Transformer V2: Scaling Up Capacity and Resolution`
- https://arxiv.org/abs/2111.09883
Code/weights from https://github.com/microsoft/Swin-Transformer, original copyright/license info below
Modifications and additions for timm hacked together by / Copyright 2022, ... |
17,876 | /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/swin_transformer_v2.py | 286,289 | 194,332 | _calc_window_shift | SwinTransformerV2Block | 289,295 | 289,295 | class SwinTransformerV2Block(nn.Module):
""" Swin Transformer Block.
"""
def __init__(
self,
dim: int,
input_resolution: _int_or_tuple_2_t,
num_heads: int,
window_size: _int_or_tuple_2_t = 7,
shift_size: _int_or_tuple_2_t = 0,
... | class SwinTransformerV2Block(nn.Module):
""" Swin Transformer Block.
"""
def __init__(
self,
dim: int,
input_resolution: _int_or_tuple_2_t,
num_heads: int,
window_size: _int_or_tuple_2_t = 7,
shift_size: _int_or_tuple_2_t = 0,
... | target_window_size = to_2tuple(target_window_size)
target_shift_size = to_2tuple(target_shift_size)
window_size = [r if r <= w else w for r, w in zip(self.input_resolution, target_window_size)]
shift_size = [0 if r <= w else s for r, w, s in zip(self.input_resolution, window_size, target... | target_window_size = to_2tuple(target_window_size)
target_shift_size = to_2tuple(target_shift_size)
window_size = [r if r <= w else w for r, w in zip(self.input_resolution, target_window_size)]
shift_size = [0 if r <= w else s for r, w, s in zip(self.input_resolution, window_size, target... | 41 | 1,797 | 134 | 1,931 | """ Swin Transformer V2
A PyTorch impl of : `Swin Transformer V2: Scaling Up Capacity and Resolution`
- https://arxiv.org/abs/2111.09883
Code/weights from https://github.com/microsoft/Swin-Transformer, original copyright/license info below
Modifications and additions for timm hacked together by / Copyright 2022, ... | """ Swin Transformer V2
A PyTorch impl of : `Swin Transformer V2: Scaling Up Capacity and Resolution`
- https://arxiv.org/abs/2111.09883
Code/weights from https://github.com/microsoft/Swin-Transformer, original copyright/license info below
Modifications and additions for timm hacked together by / Copyright 2022, ... |
17,877 | /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/swin_transformer_v2.py | 447,448 | 359,455 | _init_respostnorm | SwinTransformerV2Stage | 448,455 | 448,455 | class SwinTransformerV2Stage(nn.Module):
""" A Swin Transformer V2 Stage.
"""
def __init__(
self,
dim: int,
out_dim: int,
input_resolution: _int_or_tuple_2_t,
depth: int,
num_heads: int,
window_size: _int_or_tuple_2_t,
... | class SwinTransformerV2Stage(nn.Module):
""" A Swin Transformer V2 Stage.
"""
def __init__(
self,
dim: int,
out_dim: int,
input_resolution: _int_or_tuple_2_t,
depth: int,
num_heads: int,
window_size: _int_or_tuple_2_t,
... | for blk in self.blocks:
nn.init.constant_(blk.norm1.bias, 0)
nn.init.constant_(blk.norm1.weight, 0)
nn.init.constant_(blk.norm2.bias, 0)
nn.init.constant_(blk.norm2.weight, 0)
| for blk in self.blocks:
nn.init.constant_(blk.norm1.bias, 0)
nn.init.constant_(blk.norm1.weight, 0)
nn.init.constant_(blk.norm2.bias, 0)
nn.init.constant_(blk.norm2.weight, 0)
| 12 | 1,016 | 101 | 1,117 | """ Swin Transformer V2
A PyTorch impl of : `Swin Transformer V2: Scaling Up Capacity and Resolution`
- https://arxiv.org/abs/2111.09883
Code/weights from https://github.com/microsoft/Swin-Transformer, original copyright/license info below
Modifications and additions for timm hacked together by / Copyright 2022, ... | """ Swin Transformer V2
A PyTorch impl of : `Swin Transformer V2: Scaling Up Capacity and Resolution`
- https://arxiv.org/abs/2111.09883
Code/weights from https://github.com/microsoft/Swin-Transformer, original copyright/license info below
Modifications and additions for timm hacked together by / Copyright 2022, ... |
17,880 | /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/swin_transformer_v2_cr.py | 430,431 | 416,439 | forward | PatchEmbed | 431,439 | 431,439 | class PatchEmbed(nn.Module):
""" 2D Image to Patch Embedding """
def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, norm_layer=None):
super().__init__()
img_size = to_2tuple(img_size)
patch_size = to_2tuple(patch_size)
self.img_size = img_size
self... | class PatchEmbed(nn.Module):
""" 2D Image to Patch Embedding """
def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, norm_layer=None):
super().__init__()
img_size = to_2tuple(img_size)
patch_size = to_2tuple(patch_size)
self.img_size = img_size
self... | B, C, H, W = x.shape
_assert(H == self.img_size[0], f"Input image height ({H}) doesn't match model ({self.img_size[0]}).")
_assert(W == self.img_size[1], f"Input image width ({W}) doesn't match model ({self.img_size[1]}).")
x = self.proj(x)
x = self.norm(x.permute(0, 2, 3, 1)).pe... | B, C, H, W = x.shape
_assert(H == self.img_size[0], f"Input image height ({H}) doesn't match model ({self.img_size[0]}).")
_assert(W == self.img_size[1], f"Input image width ({W}) doesn't match model ({self.img_size[1]}).")
x = self.proj(x)
x = self.norm(x.permute(0, 2, 3, 1)).pe... | 42 | 275 | 159 | 434 | """ Swin Transformer V2
A PyTorch impl of : `Swin Transformer V2: Scaling Up Capacity and Resolution`
- https://arxiv.org/pdf/2111.09883
Code adapted from https://github.com/ChristophReich1996/Swin-Transformer-V2, original copyright/license info below
This implementation is experimental and subject to change in ... | """ Swin Transformer V2
A PyTorch impl of : `Swin Transformer V2: Scaling Up Capacity and Resolution`
- https://arxiv.org/pdf/2111.09883
Code adapted from https://github.com/ChristophReich1996/Swin-Transformer-V2, original copyright/license info below
This implementation is experimental and subject to change in ... |
17,881 | /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/swin_transformer_v2_cr.py | 402,403 | 390,416 | forward | PatchMerging | 409,416 | 403,416 | class PatchMerging(nn.Module):
""" This class implements the patch merging as a strided convolution with a normalization before.
Args:
dim (int): Number of input channels
norm_layer (Type[nn.Module]): Type of normalization layer to be utilized.
"""
def __init__(self, dim: int, norm_laye... | class PatchMerging(nn.Module):
""" This class implements the patch merging as a strided convolution with a normalization before.
Args:
dim (int): Number of input channels
norm_layer (Type[nn.Module]): Type of normalization layer to be utilized.
"""
def __init__(self, dim: int, norm_laye... | B, H, W, C = x.shape
x = x.reshape(B, H // 2, 2, W // 2, 2, C).permute(0, 1, 3, 4, 2, 5).flatten(3)
x = self.norm(x)
x = self.reduction(x)
return x
| """ Forward pass.
Args:
x (torch.Tensor): Input tensor of the shape [B, C, H, W]
Returns:
output (torch.Tensor): Output tensor of the shape [B, 2 * C, H // 2, W // 2]
"""
B, H, W, C = x.shape
x = x.reshape(B, H // 2, 2, W // 2, 2, C).permute(0, 1, ... | 31 | 277 | 102 | 379 | """ Swin Transformer V2
A PyTorch impl of : `Swin Transformer V2: Scaling Up Capacity and Resolution`
- https://arxiv.org/pdf/2111.09883
Code adapted from https://github.com/ChristophReich1996/Swin-Transformer-V2, original copyright/license info below
This implementation is experimental and subject to change in ... | """ Swin Transformer V2
A PyTorch impl of : `Swin Transformer V2: Scaling Up Capacity and Resolution`
- https://arxiv.org/pdf/2111.09883
Code adapted from https://github.com/ChristophReich1996/Swin-Transformer-V2, original copyright/license info below
This implementation is experimental and subject to change in ... |
17,884 | /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/swin_transformer_v2_cr.py | 513,514 | 439,545 | update_input_size | SwinTransformerV2CrStage | 520,526 | 514,526 | class SwinTransformerV2CrStage(nn.Module):
r"""This class implements a stage of the Swin transformer including multiple layers.
Args:
embed_dim (int): Number of input channels
depth (int): Depth of the stage (number of layers)
downscale (bool): If true input is downsampled (see Fig. 3 o... | class SwinTransformerV2CrStage(nn.Module):
r"""This class implements a stage of the Swin transformer including multiple layers.
Args:
embed_dim (int): Number of input channels
depth (int): Depth of the stage (number of layers)
downscale (bool): If true input is downsampled (see Fig. 3 o... | self.feat_size: Tuple[int, int] = (
(new_feat_size[0] // 2, new_feat_size[1] // 2) if self.downscale else new_feat_size
)
for block in self.blocks:
block.update_input_size(new_window_size=new_window_size, new_feat_size=self.feat_size)
| """Method updates the resolution to utilize and the window size and so the pair-wise relative positions.
Args:
new_window_size (int): New window size
new_feat_size (Tuple[int, int]): New input resolution
"""
self.feat_size: Tuple[int, int] = (
(new_fe... | 22 | 1,274 | 114 | 1,388 | """ Swin Transformer V2
A PyTorch impl of : `Swin Transformer V2: Scaling Up Capacity and Resolution`
- https://arxiv.org/pdf/2111.09883
Code adapted from https://github.com/ChristophReich1996/Swin-Transformer-V2, original copyright/license info below
This implementation is experimental and subject to change in ... | """ Swin Transformer V2
A PyTorch impl of : `Swin Transformer V2: Scaling Up Capacity and Resolution`
- https://arxiv.org/pdf/2111.09883
Code adapted from https://github.com/ChristophReich1996/Swin-Transformer-V2, original copyright/license info below
This implementation is experimental and subject to change in ... |
17,885 | /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/swin_transformer_v2_cr.py | 142,143 | 95,216 | _make_pair_wise_relative_positions | WindowMultiHeadAttention | 144,155 | 143,155 | class WindowMultiHeadAttention(nn.Module):
r"""This class implements window-based Multi-Head-Attention with log-spaced continuous position bias.
Args:
dim (int): Number of input features
window_size (int): Window size
num_heads (int): Number of attention heads
drop_attn (float):... | class WindowMultiHeadAttention(nn.Module):
r"""This class implements window-based Multi-Head-Attention with log-spaced continuous position bias.
Args:
dim (int): Number of input features
window_size (int): Window size
num_heads (int): Number of attention heads
drop_attn (float):... | device = self.logit_scale.device
coordinates = torch.stack(ndgrid(
torch.arange(self.window_size[0], device=device),
torch.arange(self.window_size[1], device=device)
), dim=0).flatten(1)
relative_coordinates = coordinates[:, :, None] - coordinates[:, None, :]
... | """Method initializes the pair-wise relative positions to compute the positional biases."""
device = self.logit_scale.device
coordinates = torch.stack(ndgrid(
torch.arange(self.window_size[0], device=device),
torch.arange(self.window_size[1], device=device)
), dim... | 38 | 1,573 | 208 | 1,781 | """ Swin Transformer V2
A PyTorch impl of : `Swin Transformer V2: Scaling Up Capacity and Resolution`
- https://arxiv.org/pdf/2111.09883
Code adapted from https://github.com/ChristophReich1996/Swin-Transformer-V2, original copyright/license info below
This implementation is experimental and subject to change in ... | """ Swin Transformer V2
A PyTorch impl of : `Swin Transformer V2: Scaling Up Capacity and Resolution`
- https://arxiv.org/pdf/2111.09883
Code adapted from https://github.com/ChristophReich1996/Swin-Transformer-V2, original copyright/license info below
This implementation is experimental and subject to change in ... |
17,887 | /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 | 206,208 | 165,247 | train | Attention | 208,212 | 208,212 | class Attention(torch.nn.Module):
fused_attn: torch.jit.Final[bool]
attention_bias_cache: Dict[str, torch.Tensor]
def __init__(
self,
dim,
key_dim,
num_heads=8,
attn_ratio=4,
resolution=(14, 14),
):
super().__init__()
... | class Attention(torch.nn.Module):
fused_attn: torch.jit.Final[bool]
attention_bias_cache: Dict[str, torch.Tensor]
def __init__(
self,
dim,
key_dim,
num_heads=8,
attn_ratio=4,
resolution=(14, 14),
):
super().__init__()
... | super().train(mode)
if mode and self.attention_bias_cache:
self.attention_bias_cache = {} # clear ab cache
| super().train(mode)
if mode and self.attention_bias_cache:
self.attention_bias_cache = {} # clear ab cache
| 12 | 1,080 | 44 | 1,124 | """ 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 ... |
17,888 | /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 | 130,131 | 110,135 | forward | ConvLayer | 131,135 | 131,135 | class ConvLayer(nn.Module):
def __init__(
self,
dim,
depth,
act_layer,
drop_path=0.,
conv_expand_ratio=4.,
):
super().__init__()
self.dim = dim
self.depth = depth
self.blocks = nn.Sequential(*[
MB... | class ConvLayer(nn.Module):
def __init__(
self,
dim,
depth,
act_layer,
drop_path=0.,
conv_expand_ratio=4.,
):
super().__init__()
self.dim = dim
self.depth = depth
self.blocks = nn.Sequential(*[
MB... | x = self.blocks(x)
return x
| x = self.blocks(x)
return x
| 5 | 173 | 20 | 193 | """ 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 ... |
17,889 | /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 | 37,39 | 29,52 | fuse | ConvNorm | 39,52 | 39,52 | class ConvNorm(torch.nn.Sequential):
def __init__(self, in_chs, out_chs, ks=1, stride=1, pad=0, dilation=1, groups=1, bn_weight_init=1):
super().__init__()
self.conv = nn.Conv2d(in_chs, out_chs, ks, stride, pad, dilation, groups, bias=False)
self.bn = nn.BatchNorm2d(out_chs)
torch.nn... | class ConvNorm(torch.nn.Sequential):
def __init__(self, in_chs, out_chs, ks=1, stride=1, pad=0, dilation=1, groups=1, bn_weight_init=1):
super().__init__()
self.conv = nn.Conv2d(in_chs, out_chs, ks, stride, pad, dilation, groups, bias=False)
self.bn = nn.BatchNorm2d(out_chs)
torch.nn... | c, bn = self.conv, self.bn
w = bn.weight / (bn.running_var + bn.eps) ** 0.5
w = c.weight * w[:, None, None, None]
b = bn.bias - bn.running_mean * bn.weight / \
(bn.running_var + bn.eps) ** 0.5
m = torch.nn.Conv2d(
w.size(1) * self.conv.groups, w.size(0), w... | c, bn = self.conv, self.bn
w = bn.weight / (bn.running_var + bn.eps) ** 0.5
w = c.weight * w[:, None, None, None]
b = bn.bias - bn.running_mean * bn.weight / \
(bn.running_var + bn.eps) ** 0.5
m = torch.nn.Conv2d(
w.size(1) * self.conv.groups, w.size(0), w... | 52 | 206 | 223 | 429 | """ 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 ... |
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