repo stringlengths 7 90 | file_url stringlengths 81 315 | file_path stringlengths 4 228 | content stringlengths 0 32.8k | language stringclasses 1
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values | commit_sha stringlengths 40 40 | retrieved_at stringdate 2026-01-04 14:38:15 2026-01-05 02:33:18 | truncated bool 2
classes |
|---|---|---|---|---|---|---|---|---|
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/layers/blur_pool.py | timm/layers/blur_pool.py | """
BlurPool layer inspired by
- Kornia's Max_BlurPool2d
- Making Convolutional Networks Shift-Invariant Again :cite:`zhang2019shiftinvar`
Hacked together by Chris Ha and Ross Wightman
"""
from functools import partial
from math import comb # Python 3.8
from typing import Callable, Optional, Type, Union
import tor... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/layers/ml_decoder.py | timm/layers/ml_decoder.py | from typing import Optional
import torch
from torch import nn
from torch import nn, Tensor
from torch.nn.modules.transformer import _get_activation_fn
def add_ml_decoder_head(model):
if hasattr(model, 'global_pool') and hasattr(model, 'fc'): # most CNN models, like Resnet50
model.global_pool = nn.Identi... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/layers/inplace_abn.py | timm/layers/inplace_abn.py | import torch
from torch import nn as nn
try:
from inplace_abn.functions import inplace_abn, inplace_abn_sync
has_iabn = True
except ImportError:
has_iabn = False
def inplace_abn(x, weight, bias, running_mean, running_var,
training=True, momentum=0.1, eps=1e-05, activation="leaky_re... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/layers/global_context.py | timm/layers/global_context.py | """ Global Context Attention Block
Paper: `GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond`
- https://arxiv.org/abs/1904.11492
Official code consulted as reference: https://github.com/xvjiarui/GCNet
Hacked together by / Copyright 2021 Ross Wightman
"""
from typing import Optional, Tuple, Ty... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/layers/pos_embed.py | timm/layers/pos_embed.py | """ Position Embedding Utilities
Hacked together by / Copyright 2022 Ross Wightman
"""
import logging
import math
from typing import List, Tuple, Optional, Union
import torch
import torch.nn.functional as F
from ._fx import register_notrace_function
_logger = logging.getLogger(__name__)
@torch.fx.wrap
@register_n... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/layers/cbam.py | timm/layers/cbam.py | """ CBAM (sort-of) Attention
Experimental impl of CBAM: Convolutional Block Attention Module: https://arxiv.org/abs/1807.06521
WARNING: Results with these attention layers have been mixed. They can significantly reduce performance on
some tasks, especially fine-grained it seems. I may end up removing this impl.
Hack... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/layers/cond_conv2d.py | timm/layers/cond_conv2d.py | """ PyTorch Conditionally Parameterized Convolution (CondConv)
Paper: CondConv: Conditionally Parameterized Convolutions for Efficient Inference
(https://arxiv.org/abs/1904.04971)
Hacked together by / Copyright 2020 Ross Wightman
"""
import math
from functools import partial
from typing import Union, Tuple
import t... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/layers/layer_scale.py | timm/layers/layer_scale.py | import torch
from torch import nn
class LayerScale(nn.Module):
""" LayerScale on tensors with channels in last-dim.
"""
def __init__(
self,
dim: int,
init_values: float = 1e-5,
inplace: bool = False,
device=None,
dtype=None,
) -> ... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/layers/selective_kernel.py | timm/layers/selective_kernel.py | """ Selective Kernel Convolution/Attention
Paper: Selective Kernel Networks (https://arxiv.org/abs/1903.06586)
Hacked together by / Copyright 2020 Ross Wightman
"""
from typing import List, Optional, Tuple, Type, Union
import torch
from torch import nn as nn
from .conv_bn_act import ConvNormAct
from .helpers import... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/layers/split_attn.py | timm/layers/split_attn.py | """ Split Attention Conv2d (for ResNeSt Models)
Paper: `ResNeSt: Split-Attention Networks` - /https://arxiv.org/abs/2004.08955
Adapted from original PyTorch impl at https://github.com/zhanghang1989/ResNeSt
Modified for torchscript compat, performance, and consistency with timm by Ross Wightman
"""
from typing import... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/layers/helpers.py | timm/layers/helpers.py | """ Layer/Module Helpers
Hacked together by / Copyright 2020 Ross Wightman
"""
from itertools import repeat
import collections.abc
# From PyTorch internals
def _ntuple(n):
"""Return a function that converts input to an n-tuple.
Scalar values are repeated n times, while iterables are converted to tuples.
... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/layers/create_norm.py | timm/layers/create_norm.py | """ Norm Layer Factory
Create norm modules by string (to mirror create_act and creat_norm-act fns)
Copyright 2022 Ross Wightman
"""
import functools
import types
from typing import Type
import torch.nn as nn
from .norm import (
GroupNorm,
GroupNorm1,
LayerNorm,
LayerNorm2d,
LayerNormFp32,
La... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/layers/create_conv2d.py | timm/layers/create_conv2d.py | """ Create Conv2d Factory Method
Hacked together by / Copyright 2020 Ross Wightman
"""
from .mixed_conv2d import MixedConv2d
from .cond_conv2d import CondConv2d
from .conv2d_same import create_conv2d_pad
def create_conv2d(in_channels, out_channels, kernel_size, **kwargs):
""" Select a 2d convolution implementat... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/layers/space_to_depth.py | timm/layers/space_to_depth.py | import torch
import torch.nn as nn
class SpaceToDepth(nn.Module):
"""Rearrange spatial dimensions into channel dimension.
Divides spatial dimensions by block_size and multiplies channels by block_size^2.
Used in TResNet as an efficient stem operation.
Args:
block_size: Spatial reduction fact... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/layers/conv2d_same.py | timm/layers/conv2d_same.py | """ Conv2d w/ Same Padding
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Tuple, Optional, Union
from ._fx import register_notrace_module
from .config import is_exportable, is_scriptable
from .padding import pad_same, pad_sam... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/layers/lambda_layer.py | timm/layers/lambda_layer.py | """ Lambda Layer
Paper: `LambdaNetworks: Modeling Long-Range Interactions Without Attention`
- https://arxiv.org/abs/2102.08602
@misc{2102.08602,
Author = {Irwan Bello},
Title = {LambdaNetworks: Modeling Long-Range Interactions Without Attention},
Year = {2021},
}
Status:
This impl is a WIP. Code snippets in the... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/layers/separable_conv.py | timm/layers/separable_conv.py | """ Depthwise Separable Conv Modules
Basic DWS convs. Other variations of DWS exist with batch norm or activations between the
DW and PW convs such as the Depthwise modules in MobileNetV2 / EfficientNet and Xception.
Hacked together by / Copyright 2020 Ross Wightman
"""
from typing import Optional, Type, Union
from ... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/layers/std_conv.py | timm/layers/std_conv.py | """ Convolution with Weight Standardization (StdConv and ScaledStdConv)
StdConv:
@article{weightstandardization,
author = {Siyuan Qiao and Huiyu Wang and Chenxi Liu and Wei Shen and Alan Yuille},
title = {Weight Standardization},
journal = {arXiv preprint arXiv:1903.10520},
year = {2019},
}
Code:... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/layers/config.py | timm/layers/config.py | """ Model / Layer Config singleton state
"""
import os
import warnings
from typing import Any, Optional
import torch
__all__ = [
'is_exportable', 'is_scriptable', 'is_no_jit', 'use_fused_attn',
'set_exportable', 'set_scriptable', 'set_no_jit', 'set_layer_config', 'set_fused_attn',
'set_reentrant_ckpt', 'u... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/layers/create_attn.py | timm/layers/create_attn.py | """ Attention Factory
Hacked together by / Copyright 2021 Ross Wightman
"""
import torch
from functools import partial
from .bottleneck_attn import BottleneckAttn
from .cbam import CbamModule, LightCbamModule
from .coord_attn import CoordAttn, EfficientLocalAttn, StripAttn, SimpleCoordAttn
from .eca import EcaModule,... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/layers/norm.py | timm/layers/norm.py | """ Normalization layers and wrappers
Norm layer definitions that support fast norm and consistent channel arg order (always first arg).
Hacked together by / Copyright 2022 Ross Wightman
"""
import numbers
from typing import Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from .fast_norm im... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/layers/pool2d_same.py | timm/layers/pool2d_same.py | """ AvgPool2d w/ Same Padding
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import List, Tuple, Optional, Union
from ._fx import register_notrace_module
from .helpers import to_2tuple
from .padding import pad_same, get_padding_valu... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/layers/__init__.py | timm/layers/__init__.py | from ._fx import (
create_feature_extractor,
get_graph_node_names,
register_notrace_function,
register_notrace_module,
is_notrace_module,
is_notrace_function,
get_notrace_modules,
get_notrace_functions,
)
from .activations import *
from .adaptive_avgmax_pool import (
adaptive_avgmax_... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/layers/adaptive_avgmax_pool.py | timm/layers/adaptive_avgmax_pool.py | """ PyTorch selectable adaptive pooling
Adaptive pooling with the ability to select the type of pooling from:
* 'avg' - Average pooling
* 'max' - Max pooling
* 'avgmax' - Sum of average and max pooling re-scaled by 0.5
* 'avgmaxc' - Concatenation of average and max pooling along feature dim, doubles fea... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/layers/attention.py | timm/layers/attention.py | from typing import Final, Optional, Type
import torch
from torch import nn as nn
from torch.nn import functional as F
from ._fx import register_notrace_function
from .config import use_fused_attn
from .pos_embed_sincos import apply_rot_embed_cat
@torch.fx.wrap
@register_notrace_function
def maybe_add_mask(scores: t... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/layers/grid.py | timm/layers/grid.py | from typing import Tuple
import torch
def ndgrid(*tensors) -> Tuple[torch.Tensor, ...]:
"""generate N-D grid in dimension order.
The ndgrid function is like meshgrid except that the order of the first two input arguments are switched.
That is, the statement
[X1,X2,X3] = ndgrid(x1,x2,x3)
produc... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/layers/patch_embed.py | timm/layers/patch_embed.py | """ Image to Patch Embedding using Conv2d
A convolution based approach to patchifying a 2D image w/ embedding projection.
Based on code in:
* https://github.com/google-research/vision_transformer
* https://github.com/google-research/big_vision/tree/main/big_vision
Hacked together by / Copyright 2020 Ross Wightma... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/layers/_fx.py | timm/layers/_fx.py | from typing import Callable, Dict, List, Optional, Union, Tuple, Type
import torch
from torch import nn
try:
# NOTE we wrap torchvision fns to use timm leaf / no trace definitions
from torchvision.models.feature_extraction import create_feature_extractor as _create_feature_extractor
from torchvision.model... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/layers/mixed_conv2d.py | timm/layers/mixed_conv2d.py | """ PyTorch Mixed Convolution
Paper: MixConv: Mixed Depthwise Convolutional Kernels (https://arxiv.org/abs/1907.09595)
Hacked together by / Copyright 2020 Ross Wightman
"""
from typing import List, Union
import torch
from torch import nn as nn
from .conv2d_same import create_conv2d_pad
def _split_channels(num_cha... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/layers/coord_attn.py | timm/layers/coord_attn.py | """ Coordinate Attention and Variants
Coordinate Attention decomposes channel attention into two 1D feature encoding processes
to capture long-range dependencies with precise positional information. This module includes
the original implementation along with simplified and other variants.
Papers / References:
- Coord... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/layers/evo_norm.py | timm/layers/evo_norm.py | """ EvoNorm in PyTorch
Based on `Evolving Normalization-Activation Layers` - https://arxiv.org/abs/2004.02967
@inproceedings{NEURIPS2020,
author = {Liu, Hanxiao and Brock, Andy and Simonyan, Karen and Le, Quoc},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Larochelle and M. Ranzato ... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/layers/conv_bn_act.py | timm/layers/conv_bn_act.py | """ Conv2d + BN + Act
Hacked together by / Copyright 2020 Ross Wightman
"""
from typing import Any, Dict, Optional, Type
from torch import nn as nn
from .typing import LayerType, PadType
from .blur_pool import create_aa
from .create_conv2d import create_conv2d
from .create_norm_act import get_norm_act_layer
class ... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/layers/grn.py | timm/layers/grn.py | """ Global Response Normalization Module
Based on the GRN layer presented in
`ConvNeXt-V2 - Co-designing and Scaling ConvNets with Masked Autoencoders` - https://arxiv.org/abs/2301.00808
This implementation
* works for both NCHW and NHWC tensor layouts
* uses affine param names matching existing torch norm layers
* s... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/layers/linear.py | timm/layers/linear.py | """ Linear layer (alternate definition)
"""
import torch
import torch.nn.functional as F
from torch import nn as nn
class Linear(nn.Linear):
r"""Applies a linear transformation to the incoming data: :math:`y = xA^T + b`
Wraps torch.nn.Linear to support AMP + torchscript usage by manually casting
weight &... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/layers/classifier.py | timm/layers/classifier.py | """ Classifier head and layer factory
Hacked together by / Copyright 2020 Ross Wightman
"""
from collections import OrderedDict
from functools import partial
from typing import Optional, Union, Callable
import torch
import torch.nn as nn
from torch.nn import functional as F
from .adaptive_avgmax_pool import SelectAd... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/layers/split_batchnorm.py | timm/layers/split_batchnorm.py | """ Split BatchNorm
A PyTorch BatchNorm layer that splits input batch into N equal parts and passes each through
a separate BN layer. The first split is passed through the parent BN layers with weight/bias
keys the same as the original BN. All other splits pass through BN sub-layers under the '.aux_bn'
namespace.
Thi... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/layers/activations.py | timm/layers/activations.py | """ Activations
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from torch import nn as nn
from torch.nn import functional as F
def swish(x, inplace:... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/layers/padding.py | timm/layers/padding.py | """ Padding Helpers
Hacked together by / Copyright 2020 Ross Wightman
"""
import math
from typing import List, Tuple, Union
import torch
import torch.nn.functional as F
from .helpers import to_2tuple
# Calculate symmetric padding for a convolution
def get_padding(kernel_size: int, stride: int = 1, dilation: int = ... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/data/naflex_transforms.py | timm/data/naflex_transforms.py | """ NaFlex (NaViT + FlexiViT) Transforms and Collation
Implements PyTorch versions of the transforms described in the NaViT and FlexiViT papers:
- NaViT: https://arxiv.org/abs/2307.14995
- FlexiViT: https://arxiv.org/abs/2212.08013
Enables variable resolution/aspect ratio image handling with efficient patching.
Hack... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/data/imagenet_info.py | timm/data/imagenet_info.py | import csv
import os
import pkgutil
import re
from typing import Dict, List, Optional, Union
from .dataset_info import DatasetInfo
# NOTE no ambiguity wrt to mapping from # classes to ImageNet subset so far, but likely to change
_NUM_CLASSES_TO_SUBSET = {
1000: 'imagenet-1k',
11221: 'imagenet-21k-miil', # m... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/data/naflex_mixup.py | timm/data/naflex_mixup.py | """Variable‑size Mixup / CutMix utilities for NaFlex data loaders.
This module provides:
* `mix_batch_variable_size` – pixel‑level Mixup/CutMix that operates on a
list of images whose spatial sizes differ, mixing only their central overlap
so no resizing is required.
* `pairwise_mixup_target` – builds soft‑label ... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/data/distributed_sampler.py | timm/data/distributed_sampler.py | import math
import torch
from torch.utils.data import Sampler
import torch.distributed as dist
class OrderedDistributedSampler(Sampler):
"""Sampler that restricts data loading to a subset of the dataset.
It is especially useful in conjunction with
:class:`torch.nn.parallel.DistributedDataParallel`. In suc... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/data/real_labels.py | timm/data/real_labels.py | """ Real labels evaluator for ImageNet
Paper: `Are we done with ImageNet?` - https://arxiv.org/abs/2006.07159
Based on Numpy example at https://github.com/google-research/reassessed-imagenet
Hacked together by / Copyright 2020 Ross Wightman
"""
import os
import json
import numpy as np
import pkgutil
class RealLabels... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/data/auto_augment.py | timm/data/auto_augment.py | """ AutoAugment, RandAugment, AugMix, and 3-Augment for PyTorch
This code implements the searched ImageNet policies with various tweaks and improvements and
does not include any of the search code.
AA and RA Implementation adapted from:
https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/au... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | true |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/data/naflex_random_erasing.py | timm/data/naflex_random_erasing.py | """Patch-level random erasing augmentation for NaFlex Vision Transformers.
This module implements random erasing specifically designed for patchified images,
operating at the patch granularity rather than pixel level. It supports two modes:
- 'patch': Randomly erases individual patches (speckle-like noise)
- 'region':... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/data/loader.py | timm/data/loader.py | """ Loader Factory, Fast Collate, CUDA Prefetcher
Prefetcher and Fast Collate inspired by NVIDIA APEX example at
https://github.com/NVIDIA/apex/commit/d5e2bb4bdeedd27b1dfaf5bb2b24d6c000dee9be#diff-cf86c282ff7fba81fad27a559379d5bf
Hacked together by / Copyright 2019, Ross Wightman
"""
import logging
import random
from... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/data/constants.py | timm/data/constants.py | DEFAULT_CROP_PCT = 0.875
DEFAULT_CROP_MODE = 'center'
IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406)
IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225)
IMAGENET_INCEPTION_MEAN = (0.5, 0.5, 0.5)
IMAGENET_INCEPTION_STD = (0.5, 0.5, 0.5)
IMAGENET_DPN_MEAN = (124 / 255, 117 / 255, 104 / 255)
IMAGENET_DPN_STD = tuple([1 / (.0167 *... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/data/dataset.py | timm/data/dataset.py | """ Quick n Simple Image Folder, Tarfile based DataSet
Hacked together by / Copyright 2019, Ross Wightman
"""
import io
import logging
from typing import Optional
import torch
import torch.utils.data as data
from PIL import Image
from .readers import create_reader
_logger = logging.getLogger(__name__)
_ERROR_RETR... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/data/dataset_info.py | timm/data/dataset_info.py | from abc import ABC, abstractmethod
from typing import Dict, List, Optional, Union
class DatasetInfo(ABC):
def __init__(self):
pass
@abstractmethod
def num_classes(self):
pass
@abstractmethod
def label_names(self):
pass
@abstractmethod
def label_descriptions(sel... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/data/transforms_factory.py | timm/data/transforms_factory.py | """ Transforms Factory
Factory methods for building image transforms for use with TIMM (PyTorch Image Models)
Hacked together by / Copyright 2019, Ross Wightman
"""
import math
from typing import Optional, Tuple, Union
import torch
from torchvision import transforms
from timm.data.constants import IMAGENET_DEFAULT_M... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/data/naflex_dataset.py | timm/data/naflex_dataset.py | """ Dynamic Sequence Length Datasets for Variable Resolution Image Processing
Implements two dataset wrappers:
1. NaFlexMapDatasetWrapper - Map-style dataset that returns batches with variable sequence lengths
TODO: 2. NaFlexIterableDatasetWrapper - Iterable dataset that yields batches with variable sequence lengths
... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/data/config.py | timm/data/config.py | import logging
from .constants import *
_logger = logging.getLogger(__name__)
def resolve_data_config(
args=None,
pretrained_cfg=None,
model=None,
use_test_size=False,
verbose=False
):
assert model or args or pretrained_cfg, "At least one of model, args, or pretrained_cfg... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/data/__init__.py | timm/data/__init__.py | from .auto_augment import RandAugment, AutoAugment, rand_augment_ops, auto_augment_policy,\
rand_augment_transform, auto_augment_transform
from .config import resolve_data_config, resolve_model_data_config
from .constants import *
from .dataset import ImageDataset, IterableImageDataset, AugMixDataset
from .dataset_... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/data/naflex_loader.py | timm/data/naflex_loader.py | """NaFlex data loader for dynamic sequence length training.
This module provides a specialized data loader for Vision Transformer models that supports:
- Dynamic sequence length sampling during training for improved efficiency
- Variable patch size training with probabilistic selection
- Patch-level random erasing aug... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/data/dataset_factory.py | timm/data/dataset_factory.py | """ Dataset Factory
Hacked together by / Copyright 2021, Ross Wightman
"""
import os
from typing import Optional
from torchvision.datasets import CIFAR100, CIFAR10, MNIST, KMNIST, FashionMNIST, ImageFolder
try:
from torchvision.datasets import Places365
has_places365 = True
except ImportError:
has_places3... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/data/mixup.py | timm/data/mixup.py | """ Mixup and Cutmix
Papers:
mixup: Beyond Empirical Risk Minimization (https://arxiv.org/abs/1710.09412)
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features (https://arxiv.org/abs/1905.04899)
Code Reference:
CutMix: https://github.com/clovaai/CutMix-PyTorch
Hacked together by / Co... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/data/random_erasing.py | timm/data/random_erasing.py | """ Random Erasing (Cutout)
Originally inspired by impl at https://github.com/zhunzhong07/Random-Erasing, Apache 2.0
Copyright Zhun Zhong & Liang Zheng
Hacked together by / Copyright 2019, Ross Wightman
"""
import random
import math
import torch
def _get_pixels(per_pixel, rand_color, patch_size, dtype=torch.float3... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/data/transforms.py | timm/data/transforms.py | import math
import numbers
import random
import warnings
from typing import List, Sequence, Tuple, Union
import torch
import torchvision.transforms as transforms
import torchvision.transforms.functional as F
try:
from torchvision.transforms.functional import InterpolationMode
has_interpolation_mode = True
exce... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/data/tf_preprocessing.py | timm/data/tf_preprocessing.py | """ Tensorflow Preprocessing Adapter
Allows use of Tensorflow preprocessing pipeline in PyTorch Transform
Copyright of original Tensorflow code below.
Hacked together by / Copyright 2020 Ross Wightman
"""
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/data/readers/reader_factory.py | timm/data/readers/reader_factory.py | import os
from typing import Optional
from .reader_image_folder import ReaderImageFolder
from .reader_image_in_tar import ReaderImageInTar
def create_reader(
name: str,
root: Optional[str] = None,
split: str = 'train',
**kwargs,
):
kwargs = {k: v for k, v in kwargs.items() if v is... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/data/readers/img_extensions.py | timm/data/readers/img_extensions.py | from copy import deepcopy
__all__ = ['get_img_extensions', 'is_img_extension', 'set_img_extensions', 'add_img_extensions', 'del_img_extensions']
IMG_EXTENSIONS = ('.png', '.jpg', '.jpeg') # singleton, kept public for bwd compat use
_IMG_EXTENSIONS_SET = set(IMG_EXTENSIONS) # set version, private, kept in sync
de... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/data/readers/reader.py | timm/data/readers/reader.py | from abc import abstractmethod
class Reader:
def __init__(self):
pass
@abstractmethod
def _filename(self, index, basename=False, absolute=False):
pass
def filename(self, index, basename=False, absolute=False):
return self._filename(index, basename=basename, absolute=absolute)... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/data/readers/reader_wds.py | timm/data/readers/reader_wds.py | """ Dataset reader for webdataset
Hacked together by / Copyright 2022 Ross Wightman
"""
import io
import json
import logging
import math
import os
import random
import sys
from dataclasses import dataclass
from functools import partial
from itertools import islice
from typing import Any, Callable, Dict, List, Optional... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/data/readers/reader_image_tar.py | timm/data/readers/reader_image_tar.py | """ A dataset reader that reads single tarfile based datasets
This reader can read datasets consisting if a single tarfile containing images.
I am planning to deprecated it in favour of ParerImageInTar.
Hacked together by / Copyright 2020 Ross Wightman
"""
import os
import tarfile
from timm.utils.misc import natural... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/data/readers/reader_hfds.py | timm/data/readers/reader_hfds.py | """ Dataset reader that wraps Hugging Face datasets
Hacked together by / Copyright 2022 Ross Wightman
"""
import io
import math
from typing import Optional
import torch
import torch.distributed as dist
from PIL import Image
try:
import datasets
except ImportError as e:
print("Please install Hugging Face data... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/data/readers/class_map.py | timm/data/readers/class_map.py | import os
import pickle
def load_class_map(map_or_filename, root=''):
if isinstance(map_or_filename, dict):
assert dict, 'class_map dict must be non-empty'
return map_or_filename
class_map_path = map_or_filename
if not os.path.exists(class_map_path):
class_map_path = os.path.join(r... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/data/readers/shared_count.py | timm/data/readers/shared_count.py | from multiprocessing import Value
class SharedCount:
def __init__(self, epoch: int = 0):
self.shared_epoch = Value('i', epoch)
@property
def value(self):
return self.shared_epoch.value
@value.setter
def value(self, epoch):
self.shared_epoch.value = epoch
| python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/data/readers/__init__.py | timm/data/readers/__init__.py | from .reader_factory import create_reader
from .img_extensions import *
| python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/data/readers/reader_image_folder.py | timm/data/readers/reader_image_folder.py | """ A dataset reader that extracts images from folders
Folders are scanned recursively to find image files. Labels are based
on the folder hierarchy, just leaf folders by default.
Hacked together by / Copyright 2020 Ross Wightman
"""
import os
from typing import Dict, List, Optional, Set, Tuple, Union
from timm.util... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/data/readers/reader_hfids.py | timm/data/readers/reader_hfids.py | """ Dataset reader for HF IterableDataset
"""
import math
import os
from itertools import repeat, chain
from typing import Optional
import torch
import torch.distributed as dist
from PIL import Image
try:
import datasets
from datasets.distributed import split_dataset_by_node
from datasets.splits import Sp... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/data/readers/reader_tfds.py | timm/data/readers/reader_tfds.py | """ Dataset reader that wraps TFDS datasets
Wraps many (most?) TFDS image-classification datasets
from https://github.com/tensorflow/datasets
https://www.tensorflow.org/datasets/catalog/overview#image_classification
Hacked together by / Copyright 2020 Ross Wightman
"""
import math
import os
import sys
from typing imp... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/data/readers/reader_image_in_tar.py | timm/data/readers/reader_image_in_tar.py | """ A dataset reader that reads tarfile based datasets
This reader can extract image samples from:
* a single tar of image files
* a folder of multiple tarfiles containing imagefiles
* a tar of tars containing image files
Labels are based on the combined folder and/or tar name structure.
Hacked together by / Copyrig... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/task/task.py | timm/task/task.py | """Base training task abstraction.
This module provides the base TrainingTask class that encapsulates a complete
forward pass including loss computation. Tasks return a dictionary with loss
components and outputs for logging.
"""
from typing import Dict, Optional
import torch
import torch.nn as nn
class TrainingTas... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/task/__init__.py | timm/task/__init__.py | """Training task abstractions for timm.
This module provides task-based abstractions for training loops where each task
encapsulates both the forward pass and loss computation, returning a dictionary
with loss components and outputs for logging.
"""
from .task import TrainingTask
from .classification import Classifica... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/task/classification.py | timm/task/classification.py | """Classification training task."""
import logging
from typing import Callable, Dict, Optional, Union
import torch
import torch.nn as nn
from .task import TrainingTask
_logger = logging.getLogger(__name__)
class ClassificationTask(TrainingTask):
"""Standard supervised classification task.
Simple task that... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/timm/task/distillation.py | timm/task/distillation.py | """Knowledge distillation training tasks and components."""
import logging
from typing import Dict, Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models import create_model
from timm.utils import unwrap_model
from .task import TrainingTask
_logger = logging.getLogger(... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/convert/convert_nest_flax.py | convert/convert_nest_flax.py | """
Convert weights from https://github.com/google-research/nested-transformer
NOTE: You'll need https://github.com/google/CommonLoopUtils, not included in requirements.txt
"""
import sys
import numpy as np
import torch
from clu import checkpoint
arch_depths = {
'nest_base': [2, 2, 20],
'nest_small': [2, 2... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
huggingface/pytorch-image-models | https://github.com/huggingface/pytorch-image-models/blob/31a85de79aad229c070c595c5ea0c43dfc95461e/convert/convert_from_mxnet.py | convert/convert_from_mxnet.py | import argparse
import hashlib
import os
import mxnet as mx
import gluoncv
import torch
from timm import create_model
parser = argparse.ArgumentParser(description='Convert from MXNet')
parser.add_argument('--model', default='all', type=str, metavar='MODEL',
help='Name of model to train (default: "... | python | Apache-2.0 | 31a85de79aad229c070c595c5ea0c43dfc95461e | 2026-01-04T14:41:55.902905Z | false |
myshell-ai/OpenVoice | https://github.com/myshell-ai/OpenVoice/blob/74a1d147b17a8c3092dd5430504bd83ef6c7eb23/setup.py | setup.py | from setuptools import setup, find_packages
setup(name='MyShell-OpenVoice',
version='0.0.0',
description='Instant voice cloning by MyShell.',
long_description=open('README.md').read().strip(),
long_description_content_type='text/markdown',
keywords=[
'text-to-speech',
... | python | MIT | 74a1d147b17a8c3092dd5430504bd83ef6c7eb23 | 2026-01-04T14:42:07.279944Z | false |
myshell-ai/OpenVoice | https://github.com/myshell-ai/OpenVoice/blob/74a1d147b17a8c3092dd5430504bd83ef6c7eb23/openvoice/api.py | openvoice/api.py | import torch
import numpy as np
import re
import soundfile
from openvoice import utils
from openvoice import commons
import os
import librosa
from openvoice.text import text_to_sequence
from openvoice.mel_processing import spectrogram_torch
from openvoice.models import SynthesizerTrn
class OpenVoiceBaseClass(object):... | python | MIT | 74a1d147b17a8c3092dd5430504bd83ef6c7eb23 | 2026-01-04T14:42:07.279944Z | false |
myshell-ai/OpenVoice | https://github.com/myshell-ai/OpenVoice/blob/74a1d147b17a8c3092dd5430504bd83ef6c7eb23/openvoice/attentions.py | openvoice/attentions.py | import math
import torch
from torch import nn
from torch.nn import functional as F
from openvoice import commons
import logging
logger = logging.getLogger(__name__)
class LayerNorm(nn.Module):
def __init__(self, channels, eps=1e-5):
super().__init__()
self.channels = channels
self.eps = ... | python | MIT | 74a1d147b17a8c3092dd5430504bd83ef6c7eb23 | 2026-01-04T14:42:07.279944Z | false |
myshell-ai/OpenVoice | https://github.com/myshell-ai/OpenVoice/blob/74a1d147b17a8c3092dd5430504bd83ef6c7eb23/openvoice/models.py | openvoice/models.py | import math
import torch
from torch import nn
from torch.nn import functional as F
from openvoice import commons
from openvoice import modules
from openvoice import attentions
from torch.nn import Conv1d, ConvTranspose1d, Conv2d
from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
from openvoice... | python | MIT | 74a1d147b17a8c3092dd5430504bd83ef6c7eb23 | 2026-01-04T14:42:07.279944Z | false |
myshell-ai/OpenVoice | https://github.com/myshell-ai/OpenVoice/blob/74a1d147b17a8c3092dd5430504bd83ef6c7eb23/openvoice/mel_processing.py | openvoice/mel_processing.py | import torch
import torch.utils.data
from librosa.filters import mel as librosa_mel_fn
MAX_WAV_VALUE = 32768.0
def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
"""
PARAMS
------
C: compression factor
"""
return torch.log(torch.clamp(x, min=clip_val) * C)
def dynamic_range_decompr... | python | MIT | 74a1d147b17a8c3092dd5430504bd83ef6c7eb23 | 2026-01-04T14:42:07.279944Z | false |
myshell-ai/OpenVoice | https://github.com/myshell-ai/OpenVoice/blob/74a1d147b17a8c3092dd5430504bd83ef6c7eb23/openvoice/modules.py | openvoice/modules.py | import math
import torch
from torch import nn
from torch.nn import functional as F
from torch.nn import Conv1d
from torch.nn.utils import weight_norm, remove_weight_norm
from openvoice import commons
from openvoice.commons import init_weights, get_padding
from openvoice.transforms import piecewise_rational_quadratic_... | python | MIT | 74a1d147b17a8c3092dd5430504bd83ef6c7eb23 | 2026-01-04T14:42:07.279944Z | false |
myshell-ai/OpenVoice | https://github.com/myshell-ai/OpenVoice/blob/74a1d147b17a8c3092dd5430504bd83ef6c7eb23/openvoice/commons.py | openvoice/commons.py | import math
import torch
from torch.nn import functional as F
def init_weights(m, mean=0.0, std=0.01):
classname = m.__class__.__name__
if classname.find("Conv") != -1:
m.weight.data.normal_(mean, std)
def get_padding(kernel_size, dilation=1):
return int((kernel_size * dilation - dilation) / 2)
... | python | MIT | 74a1d147b17a8c3092dd5430504bd83ef6c7eb23 | 2026-01-04T14:42:07.279944Z | false |
myshell-ai/OpenVoice | https://github.com/myshell-ai/OpenVoice/blob/74a1d147b17a8c3092dd5430504bd83ef6c7eb23/openvoice/utils.py | openvoice/utils.py | import re
import json
import numpy as np
def get_hparams_from_file(config_path):
with open(config_path, "r", encoding="utf-8") as f:
data = f.read()
config = json.loads(data)
hparams = HParams(**config)
return hparams
class HParams:
def __init__(self, **kwargs):
for k, v in kwarg... | python | MIT | 74a1d147b17a8c3092dd5430504bd83ef6c7eb23 | 2026-01-04T14:42:07.279944Z | false |
myshell-ai/OpenVoice | https://github.com/myshell-ai/OpenVoice/blob/74a1d147b17a8c3092dd5430504bd83ef6c7eb23/openvoice/__init__.py | openvoice/__init__.py | python | MIT | 74a1d147b17a8c3092dd5430504bd83ef6c7eb23 | 2026-01-04T14:42:07.279944Z | false | |
myshell-ai/OpenVoice | https://github.com/myshell-ai/OpenVoice/blob/74a1d147b17a8c3092dd5430504bd83ef6c7eb23/openvoice/se_extractor.py | openvoice/se_extractor.py | import os
import glob
import torch
import hashlib
import librosa
import base64
from glob import glob
import numpy as np
from pydub import AudioSegment
from faster_whisper import WhisperModel
import hashlib
import base64
import librosa
from whisper_timestamped.transcribe import get_audio_tensor, get_vad_segments
model_... | python | MIT | 74a1d147b17a8c3092dd5430504bd83ef6c7eb23 | 2026-01-04T14:42:07.279944Z | false |
myshell-ai/OpenVoice | https://github.com/myshell-ai/OpenVoice/blob/74a1d147b17a8c3092dd5430504bd83ef6c7eb23/openvoice/openvoice_app.py | openvoice/openvoice_app.py | import os
import torch
import argparse
import gradio as gr
from zipfile import ZipFile
import langid
from openvoice import se_extractor
from openvoice.api import BaseSpeakerTTS, ToneColorConverter
parser = argparse.ArgumentParser()
parser.add_argument("--share", action='store_true', default=False, help="make link publ... | python | MIT | 74a1d147b17a8c3092dd5430504bd83ef6c7eb23 | 2026-01-04T14:42:07.279944Z | false |
myshell-ai/OpenVoice | https://github.com/myshell-ai/OpenVoice/blob/74a1d147b17a8c3092dd5430504bd83ef6c7eb23/openvoice/transforms.py | openvoice/transforms.py | import torch
from torch.nn import functional as F
import numpy as np
DEFAULT_MIN_BIN_WIDTH = 1e-3
DEFAULT_MIN_BIN_HEIGHT = 1e-3
DEFAULT_MIN_DERIVATIVE = 1e-3
def piecewise_rational_quadratic_transform(
inputs,
unnormalized_widths,
unnormalized_heights,
unnormalized_derivatives,
inverse=False,
... | python | MIT | 74a1d147b17a8c3092dd5430504bd83ef6c7eb23 | 2026-01-04T14:42:07.279944Z | false |
myshell-ai/OpenVoice | https://github.com/myshell-ai/OpenVoice/blob/74a1d147b17a8c3092dd5430504bd83ef6c7eb23/openvoice/text/english.py | openvoice/text/english.py | """ from https://github.com/keithito/tacotron """
'''
Cleaners are transformations that run over the input text at both training and eval time.
Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
hyperparameter. Some cleaners are English-specific. You'll typically want to use... | python | MIT | 74a1d147b17a8c3092dd5430504bd83ef6c7eb23 | 2026-01-04T14:42:07.279944Z | false |
myshell-ai/OpenVoice | https://github.com/myshell-ai/OpenVoice/blob/74a1d147b17a8c3092dd5430504bd83ef6c7eb23/openvoice/text/symbols.py | openvoice/text/symbols.py | '''
Defines the set of symbols used in text input to the model.
'''
# japanese_cleaners
# _pad = '_'
# _punctuation = ',.!?-'
# _letters = 'AEINOQUabdefghijkmnoprstuvwyzʃʧ↓↑ '
'''# japanese_cleaners2
_pad = '_'
_punctuation = ',.!?-~…'
_letters = 'AEINOQUabdefghijkmnoprstuvwyzʃʧʦ↓↑ '
'''
'''# korean_... | python | MIT | 74a1d147b17a8c3092dd5430504bd83ef6c7eb23 | 2026-01-04T14:42:07.279944Z | false |
myshell-ai/OpenVoice | https://github.com/myshell-ai/OpenVoice/blob/74a1d147b17a8c3092dd5430504bd83ef6c7eb23/openvoice/text/cleaners.py | openvoice/text/cleaners.py | import re
from openvoice.text.english import english_to_lazy_ipa, english_to_ipa2, english_to_lazy_ipa2
from openvoice.text.mandarin import number_to_chinese, chinese_to_bopomofo, latin_to_bopomofo, chinese_to_romaji, chinese_to_lazy_ipa, chinese_to_ipa, chinese_to_ipa2
def cjke_cleaners2(text):
text = re.sub(r'\[... | python | MIT | 74a1d147b17a8c3092dd5430504bd83ef6c7eb23 | 2026-01-04T14:42:07.279944Z | false |
myshell-ai/OpenVoice | https://github.com/myshell-ai/OpenVoice/blob/74a1d147b17a8c3092dd5430504bd83ef6c7eb23/openvoice/text/__init__.py | openvoice/text/__init__.py | """ from https://github.com/keithito/tacotron """
from openvoice.text import cleaners
from openvoice.text.symbols import symbols
# Mappings from symbol to numeric ID and vice versa:
_symbol_to_id = {s: i for i, s in enumerate(symbols)}
_id_to_symbol = {i: s for i, s in enumerate(symbols)}
def text_to_sequence(text,... | python | MIT | 74a1d147b17a8c3092dd5430504bd83ef6c7eb23 | 2026-01-04T14:42:07.279944Z | false |
myshell-ai/OpenVoice | https://github.com/myshell-ai/OpenVoice/blob/74a1d147b17a8c3092dd5430504bd83ef6c7eb23/openvoice/text/mandarin.py | openvoice/text/mandarin.py | import os
import sys
import re
from pypinyin import lazy_pinyin, BOPOMOFO
import jieba
import cn2an
import logging
# List of (Latin alphabet, bopomofo) pairs:
_latin_to_bopomofo = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
('a', 'ㄟˉ'),
('b', 'ㄅㄧˋ'),
('c', 'ㄙㄧˉ'),
('d', 'ㄉㄧˋ'),
('e'... | python | MIT | 74a1d147b17a8c3092dd5430504bd83ef6c7eb23 | 2026-01-04T14:42:07.279944Z | false |
chatanywhere/GPT_API_free | https://github.com/chatanywhere/GPT_API_free/blob/19d8c2842657cb576a380a1a2a3c2c5c4acc8bc2/demo.py | demo.py | from openai import OpenAI
client = OpenAI(
# defaults to os.environ.get("OPENAI_API_KEY")
api_key="YOUR API KEY",
base_url="https://api.chatanywhere.tech/v1"
)
# 非流式响应
def gpt_35_api(messages: list):
"""为提供的对话消息创建新的回答
Args:
messages (list): 完整的对话消息
"""
completion = client.chat.c... | python | MIT | 19d8c2842657cb576a380a1a2a3c2c5c4acc8bc2 | 2026-01-04T14:42:09.575296Z | false |
gradio-app/gradio | https://github.com/gradio-app/gradio/blob/3b56b13268ddcaf74d4e85e5dbf5a616d7843995/render_readme.py | render_readme.py | #!/usr/bin/env python
import re
from pathlib import Path
README_TEMPLATE_FILEPATH = "readme_template.md"
GETTING_STARTED_TEMPLATE_FILEPATH = "guides/01_getting-started/01_quickstart.md"
readme_template = Path(README_TEMPLATE_FILEPATH).read_text()
getting_started_template = Path(GETTING_STARTED_TEMPLATE_FILEPATH).rea... | python | Apache-2.0 | 3b56b13268ddcaf74d4e85e5dbf5a616d7843995 | 2026-01-04T14:40:26.993006Z | false |
gradio-app/gradio | https://github.com/gradio-app/gradio/blob/3b56b13268ddcaf74d4e85e5dbf5a616d7843995/scripts/delete_old_spaces.py | scripts/delete_old_spaces.py | import argparse
import datetime
from typing import Optional
from huggingface_hub import HfApi
def delete_space(space_id: str, hf_token: str, api_client: Optional[HfApi] = None):
api_client = api_client or HfApi()
api_client.delete_repo(repo_id=space_id, token=hf_token, repo_type="space")
def get_spaces_to_... | python | Apache-2.0 | 3b56b13268ddcaf74d4e85e5dbf5a616d7843995 | 2026-01-04T14:40:26.993006Z | false |
gradio-app/gradio | https://github.com/gradio-app/gradio/blob/3b56b13268ddcaf74d4e85e5dbf5a616d7843995/scripts/overwrite_xray_config.py | scripts/overwrite_xray_config.py | import json
import os
import random
import gradio as gr
def fake_func():
return "Hello There"
def xray_model(diseases, img):
return {disease: random.random() for disease in diseases}
def ct_model(diseases, img):
return {disease: 0.1 for disease in diseases}
with gr.Blocks() as demo:
gr.Markdown... | python | Apache-2.0 | 3b56b13268ddcaf74d4e85e5dbf5a616d7843995 | 2026-01-04T14:40:26.993006Z | false |
gradio-app/gradio | https://github.com/gradio-app/gradio/blob/3b56b13268ddcaf74d4e85e5dbf5a616d7843995/scripts/download_artifacts.py | scripts/download_artifacts.py | import argparse
import requests
WORKFLOW_RUN_ENDPOINT = (
"https://api.github.com/repos/{owner}/{repo}/actions/runs/{run_id}/artifacts"
)
ARTIFACT_DOWNLOAD_ENDPOINT = (
"https://api.github.com/repos/{owner}/{repo}/actions/artifacts/{artifact_id}/zip"
)
def download_artifact(
owner: str, repo: str, run_i... | python | Apache-2.0 | 3b56b13268ddcaf74d4e85e5dbf5a616d7843995 | 2026-01-04T14:40:26.993006Z | false |
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