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Major module / path restructure, timm.models.layers -> timm.layers, add _ prefix to all non model modules in timm.models
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‎avg_checkpoints.py

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@@ -16,7 +16,7 @@
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import os
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import glob
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import hashlib
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from timm.models.helpers import load_state_dict
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from timm.models import load_state_dict
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parser = argparse.ArgumentParser(description='PyTorch Checkpoint Averager')
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parser.add_argument('--input', default='', type=str, metavar='PATH',

‎clean_checkpoint.py

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import hashlib
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import shutil
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from collections import OrderedDict
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from timm.models.helpers import load_state_dict
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from timm.models import load_state_dict
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parser = argparse.ArgumentParser(description='PyTorch Checkpoint Cleaner')
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parser.add_argument('--checkpoint', default='', type=str, metavar='PATH',

‎hubconf.py

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@@ -1,4 +1,3 @@
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dependencies = ['torch']
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from timm.models import registry
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globals().update(registry._model_entrypoints)
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import timm
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globals().update(timm.models._registry._model_entrypoints)

‎inference.py

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@@ -5,24 +5,23 @@
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Hacked together by / Copyright 2020 Ross Wightman (https://github.com/rwightman)
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"""
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import os
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import time
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import argparse
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import json
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import logging
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import os
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import time
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from contextlib import suppress
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from functools import partial
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import numpy as np
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import pandas as pd
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import torch
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from timm.models import create_model, apply_test_time_pool, load_checkpoint
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from timm.data import create_dataset, create_loader, resolve_data_config
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from timm.layers import apply_test_time_pool
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from timm.models import create_model
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from timm.utils import AverageMeter, setup_default_logging, set_jit_fuser
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try:
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from apex import amp
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has_apex = True

‎tests/test_layers.py

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@@ -1,10 +1,7 @@
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import pytest
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import torch
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import torch.nn as nn
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import platform
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import os
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from timm.models.layers import create_act_layer, get_act_layer, set_layer_config
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from timm.layers import create_act_layer, set_layer_config
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class MLP(nn.Module):

‎tests/test_models.py

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@@ -14,7 +14,7 @@
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import timm
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from timm import list_models, create_model, set_scriptable, get_pretrained_cfg_value
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from timm.models.fx_features import _leaf_modules, _autowrap_functions
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from timm.models._features_fx import _leaf_modules, _autowrap_functions
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if hasattr(torch._C, '_jit_set_profiling_executor'):
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# legacy executor is too slow to compile large models for unit tests

‎timm/__init__.py

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@@ -1,4 +1,4 @@
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from .version import __version__
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from .layers import is_scriptable, is_exportable, set_scriptable, set_exportable
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from .models import create_model, list_models, list_pretrained, is_model, list_modules, model_entrypoint, \
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is_scriptable, is_exportable, set_scriptable, set_exportable, \
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is_model_pretrained, get_pretrained_cfg, get_pretrained_cfg_value

‎timm/data/readers/class_map.py

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@@ -1,6 +1,7 @@
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import os
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import pickle
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def load_class_map(map_or_filename, root=''):
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if isinstance(map_or_filename, dict):
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assert dict, 'class_map dict must be non-empty'
@@ -14,7 +15,7 @@ def load_class_map(map_or_filename, root=''):
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with open(class_map_path) as f:
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class_to_idx = {v.strip(): k for k, v in enumerate(f)}
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elif class_map_ext == '.pkl':
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with open(class_map_path,'rb') as f:
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with open(class_map_path, 'rb') as f:
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class_to_idx = pickle.load(f)
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else:
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assert False, f'Unsupported class map file extension ({class_map_ext}).'

‎timm/layers/__init__.py

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@@ -0,0 +1,44 @@
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from .activations import *
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from .adaptive_avgmax_pool import \
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adaptive_avgmax_pool2d, select_adaptive_pool2d, AdaptiveAvgMaxPool2d, SelectAdaptivePool2d
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from .blur_pool import BlurPool2d
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from .classifier import ClassifierHead, create_classifier
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from .cond_conv2d import CondConv2d, get_condconv_initializer
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from .config import is_exportable, is_scriptable, is_no_jit, set_exportable, set_scriptable, set_no_jit,\
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set_layer_config
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from .conv2d_same import Conv2dSame, conv2d_same
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from .conv_bn_act import ConvNormAct, ConvNormActAa, ConvBnAct
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from .create_act import create_act_layer, get_act_layer, get_act_fn
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from .create_attn import get_attn, create_attn
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from .create_conv2d import create_conv2d
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from .create_norm import get_norm_layer, create_norm_layer
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from .create_norm_act import get_norm_act_layer, create_norm_act_layer, get_norm_act_layer
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from .drop import DropBlock2d, DropPath, drop_block_2d, drop_path
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from .eca import EcaModule, CecaModule, EfficientChannelAttn, CircularEfficientChannelAttn
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from .evo_norm import EvoNorm2dB0, EvoNorm2dB1, EvoNorm2dB2,\
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EvoNorm2dS0, EvoNorm2dS0a, EvoNorm2dS1, EvoNorm2dS1a, EvoNorm2dS2, EvoNorm2dS2a
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from .fast_norm import is_fast_norm, set_fast_norm, fast_group_norm, fast_layer_norm
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from .filter_response_norm import FilterResponseNormTlu2d, FilterResponseNormAct2d
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from .gather_excite import GatherExcite
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from .global_context import GlobalContext
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from .helpers import to_ntuple, to_2tuple, to_3tuple, to_4tuple, make_divisible, extend_tuple
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from .inplace_abn import InplaceAbn
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from .linear import Linear
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from .mixed_conv2d import MixedConv2d
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from .mlp import Mlp, GluMlp, GatedMlp, ConvMlp
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from .non_local_attn import NonLocalAttn, BatNonLocalAttn
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from .norm import GroupNorm, GroupNorm1, LayerNorm, LayerNorm2d
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from .norm_act import BatchNormAct2d, GroupNormAct, convert_sync_batchnorm
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from .padding import get_padding, get_same_padding, pad_same
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from .patch_embed import PatchEmbed
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from .pool2d_same import AvgPool2dSame, create_pool2d
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from .squeeze_excite import SEModule, SqueezeExcite, EffectiveSEModule, EffectiveSqueezeExcite
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from .selective_kernel import SelectiveKernel
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from .separable_conv import SeparableConv2d, SeparableConvNormAct
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from .space_to_depth import SpaceToDepthModule
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from .split_attn import SplitAttn
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from .split_batchnorm import SplitBatchNorm2d, convert_splitbn_model
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from .std_conv import StdConv2d, StdConv2dSame, ScaledStdConv2d, ScaledStdConv2dSame
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from .test_time_pool import TestTimePoolHead, apply_test_time_pool
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from .trace_utils import _assert, _float_to_int
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from .weight_init import trunc_normal_, trunc_normal_tf_, variance_scaling_, lecun_normal_

‎timm/models/layers/activations.py renamed to ‎timm/layers/activations.py

File renamed without changes.

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