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1614 lines
64 KiB
1614 lines
64 KiB
""" Bring-Your-Own-Blocks Network
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A flexible network w/ dataclass based config for stacking those NN blocks.
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This model is currently used to implement the following networks:
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GPU Efficient (ResNets) - gernet_l/m/s (original versions called genet, but this was already used (by SENet author)).
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Paper: `Neural Architecture Design for GPU-Efficient Networks` - https://arxiv.org/abs/2006.14090
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Code and weights: https://github.com/idstcv/GPU-Efficient-Networks, licensed Apache 2.0
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RepVGG - repvgg_*
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Paper: `Making VGG-style ConvNets Great Again` - https://arxiv.org/abs/2101.03697
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Code and weights: https://github.com/DingXiaoH/RepVGG, licensed MIT
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In all cases the models have been modified to fit within the design of ByobNet. I've remapped
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the original weights and verified accuracies.
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For GPU Efficient nets, I used the original names for the blocks since they were for the most part
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the same as original residual blocks in ResNe(X)t, DarkNet, and other existing models. Note also some
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changes introduced in RegNet were also present in the stem and bottleneck blocks for this model.
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A significant number of different network archs can be implemented here, including variants of the
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above nets that include attention.
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Hacked together by / copyright Ross Wightman, 2021.
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"""
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import math
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from dataclasses import dataclass, field, replace
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from typing import Tuple, List, Dict, Optional, Union, Any, Callable, Sequence
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from functools import partial
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import torch
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import torch.nn as nn
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from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
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from .helpers import build_model_with_cfg, named_apply
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from .layers import ClassifierHead, ConvNormAct, BatchNormAct2d, DropPath, AvgPool2dSame, \
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create_conv2d, get_act_layer, get_norm_act_layer, get_attn, make_divisible, to_2tuple, EvoNorm2dS0, EvoNorm2dS0a,\
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EvoNorm2dS1, EvoNorm2dS1a, EvoNorm2dS2, EvoNorm2dS2a, FilterResponseNormAct2d, FilterResponseNormTlu2d
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from .registry import register_model
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__all__ = ['ByobNet', 'ByoModelCfg', 'ByoBlockCfg', 'create_byob_stem', 'create_block']
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def _cfg(url='', **kwargs):
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return {
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'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
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'crop_pct': 0.875, 'interpolation': 'bilinear',
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'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
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'first_conv': 'stem.conv', 'classifier': 'head.fc',
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**kwargs
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}
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def _cfgr(url='', **kwargs):
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return {
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'url': url, 'num_classes': 1000, 'input_size': (3, 256, 256), 'pool_size': (8, 8),
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'crop_pct': 0.9, 'interpolation': 'bicubic',
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'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
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'first_conv': 'stem.conv1.conv', 'classifier': 'head.fc',
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**kwargs
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}
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default_cfgs = {
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# GPU-Efficient (ResNet) weights
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'gernet_s': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-ger-weights/gernet_s-756b4751.pth'),
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'gernet_m': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-ger-weights/gernet_m-0873c53a.pth'),
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'gernet_l': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-ger-weights/gernet_l-f31e2e8d.pth',
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input_size=(3, 256, 256), pool_size=(8, 8)),
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# RepVGG weights
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'repvgg_a2': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-repvgg-weights/repvgg_a2-c1ee6d2b.pth',
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first_conv=('stem.conv_kxk.conv', 'stem.conv_1x1.conv')),
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'repvgg_b0': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-repvgg-weights/repvgg_b0-80ac3f1b.pth',
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first_conv=('stem.conv_kxk.conv', 'stem.conv_1x1.conv')),
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'repvgg_b1': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-repvgg-weights/repvgg_b1-77ca2989.pth',
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first_conv=('stem.conv_kxk.conv', 'stem.conv_1x1.conv')),
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'repvgg_b1g4': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-repvgg-weights/repvgg_b1g4-abde5d92.pth',
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first_conv=('stem.conv_kxk.conv', 'stem.conv_1x1.conv')),
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'repvgg_b2': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-repvgg-weights/repvgg_b2-25b7494e.pth',
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first_conv=('stem.conv_kxk.conv', 'stem.conv_1x1.conv')),
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'repvgg_b2g4': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-repvgg-weights/repvgg_b2g4-165a85f2.pth',
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first_conv=('stem.conv_kxk.conv', 'stem.conv_1x1.conv')),
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'repvgg_b3': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-repvgg-weights/repvgg_b3-199bc50d.pth',
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first_conv=('stem.conv_kxk.conv', 'stem.conv_1x1.conv')),
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'repvgg_b3g4': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-repvgg-weights/repvgg_b3g4-73c370bf.pth',
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first_conv=('stem.conv_kxk.conv', 'stem.conv_1x1.conv')),
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# experimental configs
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'resnet51q': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet51q_ra2-d47dcc76.pth',
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first_conv='stem.conv1', input_size=(3, 256, 256), pool_size=(8, 8),
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test_input_size=(3, 288, 288), crop_pct=1.0),
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'resnet61q': _cfgr(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet61q_ra2-6afc536c.pth',
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test_input_size=(3, 288, 288), crop_pct=1.0),
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'resnext26ts': _cfgr(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/resnext26ts_256_ra2-8bbd9106.pth'),
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'gcresnext26ts': _cfgr(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/gcresnext26ts_256-e414378b.pth'),
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'seresnext26ts': _cfgr(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/seresnext26ts_256-6f0d74a3.pth'),
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'eca_resnext26ts': _cfgr(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/eca_resnext26ts_256-5a1d030f.pth'),
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'bat_resnext26ts': _cfgr(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/bat_resnext26ts_256-fa6fd595.pth',
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min_input_size=(3, 256, 256)),
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'resnet32ts': _cfgr(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/resnet32ts_256-aacf5250.pth'),
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'resnet33ts': _cfgr(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/resnet33ts_256-e91b09a4.pth'),
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'gcresnet33ts': _cfgr(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/gcresnet33ts_256-0e0cd345.pth'),
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'seresnet33ts': _cfgr(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/seresnet33ts_256-f8ad44d9.pth'),
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'eca_resnet33ts': _cfgr(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/eca_resnet33ts_256-8f98face.pth'),
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'gcresnet50t': _cfgr(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/gcresnet50t_256-96374d1c.pth'),
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'gcresnext50ts': _cfgr(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/gcresnext50ts_256-3e0f515e.pth'),
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# experimental models, likely to change ot be removed
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'regnetz_b16': _cfgr(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/regnetz_b_raa-677d9606.pth',
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mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
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input_size=(3, 224, 224), pool_size=(7, 7), test_input_size=(3, 288, 288), first_conv='stem.conv', crop_pct=0.94),
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'regnetz_c16': _cfgr(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/regnetz_c_rab2_256-a54bf36a.pth',
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mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), test_input_size=(3, 320, 320), first_conv='stem.conv', crop_pct=0.94),
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'regnetz_d32': _cfgr(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/regnetz_d_rab_256-b8073a89.pth',
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mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), test_input_size=(3, 320, 320), crop_pct=0.95),
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'regnetz_d8': _cfgr(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/regnetz_d8_bh-afc03c55.pth',
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mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), test_input_size=(3, 320, 320), crop_pct=1.0),
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'regnetz_e8': _cfgr(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/regnetz_e8_bh-aace8e6e.pth',
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mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), test_input_size=(3, 320, 320), crop_pct=1.0),
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'regnetz_b16_evos': _cfgr(
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url='',
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mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
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input_size=(3, 224, 224), pool_size=(7, 7), test_input_size=(3, 288, 288), first_conv='stem.conv', crop_pct=0.94),
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'regnetz_c16_evos': _cfgr(
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url='',
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mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), test_input_size=(3, 320, 320), first_conv='stem.conv', crop_pct=0.95),
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'regnetz_d8_evob': _cfgr(
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url='',
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mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), test_input_size=(3, 320, 320), crop_pct=0.95),
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'regnetz_d8_evos': _cfgr(
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url='',
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mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), test_input_size=(3, 320, 320), crop_pct=0.95),
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'regnetz_e8_evos': _cfgr(
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url='',
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mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), test_input_size=(3, 320, 320), crop_pct=0.95),
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}
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@dataclass
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class ByoBlockCfg:
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type: Union[str, nn.Module]
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d: int # block depth (number of block repeats in stage)
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c: int # number of output channels for each block in stage
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s: int = 2 # stride of stage (first block)
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gs: Optional[Union[int, Callable]] = None # group-size of blocks in stage, conv is depthwise if gs == 1
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br: float = 1. # bottleneck-ratio of blocks in stage
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# NOTE: these config items override the model cfgs that are applied to all blocks by default
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attn_layer: Optional[str] = None
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attn_kwargs: Optional[Dict[str, Any]] = None
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self_attn_layer: Optional[str] = None
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self_attn_kwargs: Optional[Dict[str, Any]] = None
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block_kwargs: Optional[Dict[str, Any]] = None
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@dataclass
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class ByoModelCfg:
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blocks: Tuple[Union[ByoBlockCfg, Tuple[ByoBlockCfg, ...]], ...]
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downsample: str = 'conv1x1'
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stem_type: str = '3x3'
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stem_pool: Optional[str] = 'maxpool'
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stem_chs: int = 32
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width_factor: float = 1.0
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num_features: int = 0 # num out_channels for final conv, no final 1x1 conv if 0
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zero_init_last: bool = True # zero init last weight (usually bn) in residual path
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fixed_input_size: bool = False # model constrained to a fixed-input size / img_size must be provided on creation
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act_layer: str = 'relu'
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norm_layer: str = 'batchnorm'
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# NOTE: these config items will be overridden by the block cfg (per-block) if they are set there
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attn_layer: Optional[str] = None
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attn_kwargs: dict = field(default_factory=lambda: dict())
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self_attn_layer: Optional[str] = None
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self_attn_kwargs: dict = field(default_factory=lambda: dict())
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block_kwargs: Dict[str, Any] = field(default_factory=lambda: dict())
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def _rep_vgg_bcfg(d=(4, 6, 16, 1), wf=(1., 1., 1., 1.), groups=0):
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c = (64, 128, 256, 512)
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group_size = 0
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if groups > 0:
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group_size = lambda chs, idx: chs // groups if (idx + 1) % 2 == 0 else 0
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bcfg = tuple([ByoBlockCfg(type='rep', d=d, c=c * wf, gs=group_size) for d, c, wf in zip(d, c, wf)])
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return bcfg
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def interleave_blocks(
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types: Tuple[str, str], d, every: Union[int, List[int]] = 1, first: bool = False, **kwargs
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) -> Tuple[ByoBlockCfg]:
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""" interleave 2 block types in stack
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"""
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assert len(types) == 2
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if isinstance(every, int):
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every = list(range(0 if first else every, d, every + 1))
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if not every:
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every = [d - 1]
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set(every)
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blocks = []
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for i in range(d):
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block_type = types[1] if i in every else types[0]
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blocks += [ByoBlockCfg(type=block_type, d=1, **kwargs)]
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return tuple(blocks)
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model_cfgs = dict(
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gernet_l=ByoModelCfg(
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blocks=(
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ByoBlockCfg(type='basic', d=1, c=128, s=2, gs=0, br=1.),
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ByoBlockCfg(type='basic', d=2, c=192, s=2, gs=0, br=1.),
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ByoBlockCfg(type='bottle', d=6, c=640, s=2, gs=0, br=1 / 4),
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ByoBlockCfg(type='bottle', d=5, c=640, s=2, gs=1, br=3.),
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ByoBlockCfg(type='bottle', d=4, c=640, s=1, gs=1, br=3.),
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),
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stem_chs=32,
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stem_pool=None,
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num_features=2560,
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),
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gernet_m=ByoModelCfg(
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blocks=(
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ByoBlockCfg(type='basic', d=1, c=128, s=2, gs=0, br=1.),
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ByoBlockCfg(type='basic', d=2, c=192, s=2, gs=0, br=1.),
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ByoBlockCfg(type='bottle', d=6, c=640, s=2, gs=0, br=1 / 4),
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ByoBlockCfg(type='bottle', d=4, c=640, s=2, gs=1, br=3.),
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ByoBlockCfg(type='bottle', d=1, c=640, s=1, gs=1, br=3.),
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),
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stem_chs=32,
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stem_pool=None,
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num_features=2560,
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),
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gernet_s=ByoModelCfg(
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blocks=(
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ByoBlockCfg(type='basic', d=1, c=48, s=2, gs=0, br=1.),
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ByoBlockCfg(type='basic', d=3, c=48, s=2, gs=0, br=1.),
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ByoBlockCfg(type='bottle', d=7, c=384, s=2, gs=0, br=1 / 4),
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ByoBlockCfg(type='bottle', d=2, c=560, s=2, gs=1, br=3.),
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ByoBlockCfg(type='bottle', d=1, c=256, s=1, gs=1, br=3.),
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),
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stem_chs=13,
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stem_pool=None,
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num_features=1920,
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),
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repvgg_a2=ByoModelCfg(
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blocks=_rep_vgg_bcfg(d=(2, 4, 14, 1), wf=(1.5, 1.5, 1.5, 2.75)),
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stem_type='rep',
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stem_chs=64,
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),
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repvgg_b0=ByoModelCfg(
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blocks=_rep_vgg_bcfg(wf=(1., 1., 1., 2.5)),
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stem_type='rep',
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stem_chs=64,
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),
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repvgg_b1=ByoModelCfg(
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blocks=_rep_vgg_bcfg(wf=(2., 2., 2., 4.)),
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stem_type='rep',
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stem_chs=64,
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),
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repvgg_b1g4=ByoModelCfg(
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blocks=_rep_vgg_bcfg(wf=(2., 2., 2., 4.), groups=4),
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stem_type='rep',
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stem_chs=64,
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),
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repvgg_b2=ByoModelCfg(
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blocks=_rep_vgg_bcfg(wf=(2.5, 2.5, 2.5, 5.)),
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stem_type='rep',
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stem_chs=64,
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),
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repvgg_b2g4=ByoModelCfg(
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blocks=_rep_vgg_bcfg(wf=(2.5, 2.5, 2.5, 5.), groups=4),
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stem_type='rep',
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stem_chs=64,
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),
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repvgg_b3=ByoModelCfg(
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blocks=_rep_vgg_bcfg(wf=(3., 3., 3., 5.)),
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stem_type='rep',
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stem_chs=64,
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),
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repvgg_b3g4=ByoModelCfg(
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blocks=_rep_vgg_bcfg(wf=(3., 3., 3., 5.), groups=4),
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stem_type='rep',
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stem_chs=64,
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),
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# 4 x conv stem w/ 2 act, no maxpool, 2,4,6,4 repeats, group size 32 in first 3 blocks
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# DW convs in last block, 2048 pre-FC, silu act
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resnet51q=ByoModelCfg(
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blocks=(
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ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=32, br=0.25),
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ByoBlockCfg(type='bottle', d=4, c=512, s=2, gs=32, br=0.25),
|
|
ByoBlockCfg(type='bottle', d=6, c=1536, s=2, gs=32, br=0.25),
|
|
ByoBlockCfg(type='bottle', d=4, c=1536, s=2, gs=1, br=1.0),
|
|
),
|
|
stem_chs=128,
|
|
stem_type='quad2',
|
|
stem_pool=None,
|
|
num_features=2048,
|
|
act_layer='silu',
|
|
),
|
|
|
|
# 4 x conv stem w/ 4 act, no maxpool, 1,4,6,4 repeats, edge block first, group size 32 in next 2 blocks
|
|
# DW convs in last block, 4 conv for each bottle block, 2048 pre-FC, silu act
|
|
resnet61q=ByoModelCfg(
|
|
blocks=(
|
|
ByoBlockCfg(type='edge', d=1, c=256, s=1, gs=0, br=1.0, block_kwargs=dict()),
|
|
ByoBlockCfg(type='bottle', d=4, c=512, s=2, gs=32, br=0.25),
|
|
ByoBlockCfg(type='bottle', d=6, c=1536, s=2, gs=32, br=0.25),
|
|
ByoBlockCfg(type='bottle', d=4, c=1536, s=2, gs=1, br=1.0),
|
|
),
|
|
stem_chs=128,
|
|
stem_type='quad',
|
|
stem_pool=None,
|
|
num_features=2048,
|
|
act_layer='silu',
|
|
block_kwargs=dict(extra_conv=True),
|
|
),
|
|
|
|
# A series of ResNeXt-26 models w/ one of none, GC, SE, ECA, BAT attn, group size 32, SiLU act,
|
|
# and a tiered stem w/ maxpool
|
|
resnext26ts=ByoModelCfg(
|
|
blocks=(
|
|
ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=32, br=0.25),
|
|
ByoBlockCfg(type='bottle', d=2, c=512, s=2, gs=32, br=0.25),
|
|
ByoBlockCfg(type='bottle', d=2, c=1024, s=2, gs=32, br=0.25),
|
|
ByoBlockCfg(type='bottle', d=2, c=2048, s=2, gs=32, br=0.25),
|
|
),
|
|
stem_chs=64,
|
|
stem_type='tiered',
|
|
stem_pool='maxpool',
|
|
act_layer='silu',
|
|
),
|
|
gcresnext26ts=ByoModelCfg(
|
|
blocks=(
|
|
ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=32, br=0.25),
|
|
ByoBlockCfg(type='bottle', d=2, c=512, s=2, gs=32, br=0.25),
|
|
ByoBlockCfg(type='bottle', d=2, c=1024, s=2, gs=32, br=0.25),
|
|
ByoBlockCfg(type='bottle', d=2, c=2048, s=2, gs=32, br=0.25),
|
|
),
|
|
stem_chs=64,
|
|
stem_type='tiered',
|
|
stem_pool='maxpool',
|
|
act_layer='silu',
|
|
attn_layer='gca',
|
|
),
|
|
seresnext26ts=ByoModelCfg(
|
|
blocks=(
|
|
ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=32, br=0.25),
|
|
ByoBlockCfg(type='bottle', d=2, c=512, s=2, gs=32, br=0.25),
|
|
ByoBlockCfg(type='bottle', d=2, c=1024, s=2, gs=32, br=0.25),
|
|
ByoBlockCfg(type='bottle', d=2, c=2048, s=2, gs=32, br=0.25),
|
|
),
|
|
stem_chs=64,
|
|
stem_type='tiered',
|
|
stem_pool='maxpool',
|
|
act_layer='silu',
|
|
attn_layer='se',
|
|
),
|
|
eca_resnext26ts=ByoModelCfg(
|
|
blocks=(
|
|
ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=32, br=0.25),
|
|
ByoBlockCfg(type='bottle', d=2, c=512, s=2, gs=32, br=0.25),
|
|
ByoBlockCfg(type='bottle', d=2, c=1024, s=2, gs=32, br=0.25),
|
|
ByoBlockCfg(type='bottle', d=2, c=2048, s=2, gs=32, br=0.25),
|
|
),
|
|
stem_chs=64,
|
|
stem_type='tiered',
|
|
stem_pool='maxpool',
|
|
act_layer='silu',
|
|
attn_layer='eca',
|
|
),
|
|
bat_resnext26ts=ByoModelCfg(
|
|
blocks=(
|
|
ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=32, br=0.25),
|
|
ByoBlockCfg(type='bottle', d=2, c=512, s=2, gs=32, br=0.25),
|
|
ByoBlockCfg(type='bottle', d=2, c=1024, s=2, gs=32, br=0.25),
|
|
ByoBlockCfg(type='bottle', d=2, c=2048, s=2, gs=32, br=0.25),
|
|
),
|
|
stem_chs=64,
|
|
stem_type='tiered',
|
|
stem_pool='maxpool',
|
|
act_layer='silu',
|
|
attn_layer='bat',
|
|
attn_kwargs=dict(block_size=8)
|
|
),
|
|
|
|
# ResNet-32 (2, 3, 3, 2) models w/ no attn, no groups, SiLU act, no pre-fc feat layer, tiered stem w/o maxpool
|
|
resnet32ts=ByoModelCfg(
|
|
blocks=(
|
|
ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=0, br=0.25),
|
|
ByoBlockCfg(type='bottle', d=3, c=512, s=2, gs=0, br=0.25),
|
|
ByoBlockCfg(type='bottle', d=3, c=1536, s=2, gs=0, br=0.25),
|
|
ByoBlockCfg(type='bottle', d=2, c=1536, s=2, gs=0, br=0.25),
|
|
),
|
|
stem_chs=64,
|
|
stem_type='tiered',
|
|
stem_pool='',
|
|
num_features=0,
|
|
act_layer='silu',
|
|
),
|
|
|
|
# ResNet-33 (2, 3, 3, 2) models w/ no attn, no groups, SiLU act, 1280 pre-FC feat, tiered stem w/o maxpool
|
|
resnet33ts=ByoModelCfg(
|
|
blocks=(
|
|
ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=0, br=0.25),
|
|
ByoBlockCfg(type='bottle', d=3, c=512, s=2, gs=0, br=0.25),
|
|
ByoBlockCfg(type='bottle', d=3, c=1536, s=2, gs=0, br=0.25),
|
|
ByoBlockCfg(type='bottle', d=2, c=1536, s=2, gs=0, br=0.25),
|
|
),
|
|
stem_chs=64,
|
|
stem_type='tiered',
|
|
stem_pool='',
|
|
num_features=1280,
|
|
act_layer='silu',
|
|
),
|
|
|
|
# A series of ResNet-33 (2, 3, 3, 2) models w/ one of GC, SE, ECA attn, no groups, SiLU act, 1280 pre-FC feat
|
|
# and a tiered stem w/ no maxpool
|
|
gcresnet33ts=ByoModelCfg(
|
|
blocks=(
|
|
ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=0, br=0.25),
|
|
ByoBlockCfg(type='bottle', d=3, c=512, s=2, gs=0, br=0.25),
|
|
ByoBlockCfg(type='bottle', d=3, c=1536, s=2, gs=0, br=0.25),
|
|
ByoBlockCfg(type='bottle', d=2, c=1536, s=2, gs=0, br=0.25),
|
|
),
|
|
stem_chs=64,
|
|
stem_type='tiered',
|
|
stem_pool='',
|
|
num_features=1280,
|
|
act_layer='silu',
|
|
attn_layer='gca',
|
|
),
|
|
seresnet33ts=ByoModelCfg(
|
|
blocks=(
|
|
ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=0, br=0.25),
|
|
ByoBlockCfg(type='bottle', d=3, c=512, s=2, gs=0, br=0.25),
|
|
ByoBlockCfg(type='bottle', d=3, c=1536, s=2, gs=0, br=0.25),
|
|
ByoBlockCfg(type='bottle', d=2, c=1536, s=2, gs=0, br=0.25),
|
|
),
|
|
stem_chs=64,
|
|
stem_type='tiered',
|
|
stem_pool='',
|
|
num_features=1280,
|
|
act_layer='silu',
|
|
attn_layer='se',
|
|
),
|
|
eca_resnet33ts=ByoModelCfg(
|
|
blocks=(
|
|
ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=0, br=0.25),
|
|
ByoBlockCfg(type='bottle', d=3, c=512, s=2, gs=0, br=0.25),
|
|
ByoBlockCfg(type='bottle', d=3, c=1536, s=2, gs=0, br=0.25),
|
|
ByoBlockCfg(type='bottle', d=2, c=1536, s=2, gs=0, br=0.25),
|
|
),
|
|
stem_chs=64,
|
|
stem_type='tiered',
|
|
stem_pool='',
|
|
num_features=1280,
|
|
act_layer='silu',
|
|
attn_layer='eca',
|
|
),
|
|
|
|
gcresnet50t=ByoModelCfg(
|
|
blocks=(
|
|
ByoBlockCfg(type='bottle', d=3, c=256, s=1, br=0.25),
|
|
ByoBlockCfg(type='bottle', d=4, c=512, s=2, br=0.25),
|
|
ByoBlockCfg(type='bottle', d=6, c=1024, s=2, br=0.25),
|
|
ByoBlockCfg(type='bottle', d=3, c=2048, s=2, br=0.25),
|
|
),
|
|
stem_chs=64,
|
|
stem_type='tiered',
|
|
stem_pool='',
|
|
attn_layer='gca',
|
|
),
|
|
|
|
gcresnext50ts=ByoModelCfg(
|
|
blocks=(
|
|
ByoBlockCfg(type='bottle', d=3, c=256, s=1, gs=32, br=0.25),
|
|
ByoBlockCfg(type='bottle', d=4, c=512, s=2, gs=32, br=0.25),
|
|
ByoBlockCfg(type='bottle', d=6, c=1024, s=2, gs=32, br=0.25),
|
|
ByoBlockCfg(type='bottle', d=3, c=2048, s=2, gs=32, br=0.25),
|
|
),
|
|
stem_chs=64,
|
|
stem_type='tiered',
|
|
stem_pool='maxpool',
|
|
# stem_pool=None,
|
|
act_layer='silu',
|
|
attn_layer='gca',
|
|
),
|
|
|
|
# experimental models, closer to a RegNetZ than a ResNet. Similar to EfficientNets but w/ groups instead of DW
|
|
regnetz_b16=ByoModelCfg(
|
|
blocks=(
|
|
ByoBlockCfg(type='bottle', d=2, c=48, s=2, gs=16, br=3),
|
|
ByoBlockCfg(type='bottle', d=6, c=96, s=2, gs=16, br=3),
|
|
ByoBlockCfg(type='bottle', d=12, c=192, s=2, gs=16, br=3),
|
|
ByoBlockCfg(type='bottle', d=2, c=288, s=2, gs=16, br=3),
|
|
),
|
|
stem_chs=32,
|
|
stem_pool='',
|
|
downsample='',
|
|
num_features=1536,
|
|
act_layer='silu',
|
|
attn_layer='se',
|
|
attn_kwargs=dict(rd_ratio=0.25),
|
|
block_kwargs=dict(bottle_in=True, linear_out=True),
|
|
),
|
|
regnetz_c16=ByoModelCfg(
|
|
blocks=(
|
|
ByoBlockCfg(type='bottle', d=2, c=48, s=2, gs=16, br=4),
|
|
ByoBlockCfg(type='bottle', d=6, c=96, s=2, gs=16, br=4),
|
|
ByoBlockCfg(type='bottle', d=12, c=192, s=2, gs=16, br=4),
|
|
ByoBlockCfg(type='bottle', d=2, c=288, s=2, gs=16, br=4),
|
|
),
|
|
stem_chs=32,
|
|
stem_pool='',
|
|
downsample='',
|
|
num_features=1536,
|
|
act_layer='silu',
|
|
attn_layer='se',
|
|
attn_kwargs=dict(rd_ratio=0.25),
|
|
block_kwargs=dict(bottle_in=True, linear_out=True),
|
|
),
|
|
regnetz_d32=ByoModelCfg(
|
|
blocks=(
|
|
ByoBlockCfg(type='bottle', d=3, c=64, s=1, gs=32, br=4),
|
|
ByoBlockCfg(type='bottle', d=6, c=128, s=2, gs=32, br=4),
|
|
ByoBlockCfg(type='bottle', d=12, c=256, s=2, gs=32, br=4),
|
|
ByoBlockCfg(type='bottle', d=3, c=384, s=2, gs=32, br=4),
|
|
),
|
|
stem_chs=64,
|
|
stem_type='tiered',
|
|
stem_pool='',
|
|
downsample='',
|
|
num_features=1792,
|
|
act_layer='silu',
|
|
attn_layer='se',
|
|
attn_kwargs=dict(rd_ratio=0.25),
|
|
block_kwargs=dict(bottle_in=True, linear_out=True),
|
|
),
|
|
regnetz_d8=ByoModelCfg(
|
|
blocks=(
|
|
ByoBlockCfg(type='bottle', d=3, c=64, s=1, gs=8, br=4),
|
|
ByoBlockCfg(type='bottle', d=6, c=128, s=2, gs=8, br=4),
|
|
ByoBlockCfg(type='bottle', d=12, c=256, s=2, gs=8, br=4),
|
|
ByoBlockCfg(type='bottle', d=3, c=384, s=2, gs=8, br=4),
|
|
),
|
|
stem_chs=64,
|
|
stem_type='tiered',
|
|
stem_pool='',
|
|
downsample='',
|
|
num_features=1792,
|
|
act_layer='silu',
|
|
attn_layer='se',
|
|
attn_kwargs=dict(rd_ratio=0.25),
|
|
block_kwargs=dict(bottle_in=True, linear_out=True),
|
|
),
|
|
regnetz_e8=ByoModelCfg(
|
|
blocks=(
|
|
ByoBlockCfg(type='bottle', d=3, c=96, s=1, gs=8, br=4),
|
|
ByoBlockCfg(type='bottle', d=8, c=192, s=2, gs=8, br=4),
|
|
ByoBlockCfg(type='bottle', d=16, c=384, s=2, gs=8, br=4),
|
|
ByoBlockCfg(type='bottle', d=3, c=512, s=2, gs=8, br=4),
|
|
),
|
|
stem_chs=64,
|
|
stem_type='tiered',
|
|
stem_pool='',
|
|
downsample='',
|
|
num_features=2048,
|
|
act_layer='silu',
|
|
attn_layer='se',
|
|
attn_kwargs=dict(rd_ratio=0.25),
|
|
block_kwargs=dict(bottle_in=True, linear_out=True),
|
|
),
|
|
|
|
# experimental EvoNorm configs
|
|
regnetz_b16_evos=ByoModelCfg(
|
|
blocks=(
|
|
ByoBlockCfg(type='bottle', d=2, c=48, s=2, gs=16, br=3),
|
|
ByoBlockCfg(type='bottle', d=6, c=96, s=2, gs=16, br=3),
|
|
ByoBlockCfg(type='bottle', d=12, c=192, s=2, gs=16, br=3),
|
|
ByoBlockCfg(type='bottle', d=2, c=288, s=2, gs=16, br=3),
|
|
),
|
|
stem_chs=32,
|
|
stem_pool='',
|
|
downsample='',
|
|
num_features=1536,
|
|
act_layer='silu',
|
|
norm_layer=partial(EvoNorm2dS0a, group_size=16),
|
|
attn_layer='se',
|
|
attn_kwargs=dict(rd_ratio=0.25),
|
|
block_kwargs=dict(bottle_in=True, linear_out=True),
|
|
),
|
|
regnetz_c16_evos=ByoModelCfg(
|
|
blocks=(
|
|
ByoBlockCfg(type='bottle', d=2, c=48, s=2, gs=16, br=4),
|
|
ByoBlockCfg(type='bottle', d=6, c=96, s=2, gs=16, br=4),
|
|
ByoBlockCfg(type='bottle', d=12, c=192, s=2, gs=16, br=4),
|
|
ByoBlockCfg(type='bottle', d=2, c=288, s=2, gs=16, br=4),
|
|
),
|
|
stem_chs=32,
|
|
stem_pool='',
|
|
downsample='',
|
|
num_features=1536,
|
|
act_layer='silu',
|
|
norm_layer=partial(EvoNorm2dS0a, group_size=16),
|
|
attn_layer='se',
|
|
attn_kwargs=dict(rd_ratio=0.25),
|
|
block_kwargs=dict(bottle_in=True, linear_out=True),
|
|
),
|
|
regnetz_d8_evob=ByoModelCfg(
|
|
blocks=(
|
|
ByoBlockCfg(type='bottle', d=3, c=64, s=1, gs=8, br=4),
|
|
ByoBlockCfg(type='bottle', d=6, c=128, s=2, gs=8, br=4),
|
|
ByoBlockCfg(type='bottle', d=12, c=256, s=2, gs=8, br=4),
|
|
ByoBlockCfg(type='bottle', d=3, c=384, s=2, gs=8, br=4),
|
|
),
|
|
stem_chs=64,
|
|
stem_type='tiered',
|
|
stem_pool='',
|
|
downsample='',
|
|
num_features=1792,
|
|
act_layer='silu',
|
|
norm_layer='evonormb0',
|
|
attn_layer='se',
|
|
attn_kwargs=dict(rd_ratio=0.25),
|
|
block_kwargs=dict(bottle_in=True, linear_out=True),
|
|
),
|
|
regnetz_d8_evos=ByoModelCfg(
|
|
blocks=(
|
|
ByoBlockCfg(type='bottle', d=3, c=64, s=1, gs=8, br=4),
|
|
ByoBlockCfg(type='bottle', d=6, c=128, s=2, gs=8, br=4),
|
|
ByoBlockCfg(type='bottle', d=12, c=256, s=2, gs=8, br=4),
|
|
ByoBlockCfg(type='bottle', d=3, c=384, s=2, gs=8, br=4),
|
|
),
|
|
stem_chs=64,
|
|
stem_type='deep',
|
|
stem_pool='',
|
|
downsample='',
|
|
num_features=1792,
|
|
act_layer='silu',
|
|
norm_layer=partial(EvoNorm2dS0a, group_size=16),
|
|
attn_layer='se',
|
|
attn_kwargs=dict(rd_ratio=0.25),
|
|
block_kwargs=dict(bottle_in=True, linear_out=True),
|
|
),
|
|
regnetz_e8_evos=ByoModelCfg(
|
|
blocks=(
|
|
ByoBlockCfg(type='bottle', d=3, c=96, s=1, gs=8, br=4),
|
|
ByoBlockCfg(type='bottle', d=8, c=192, s=2, gs=8, br=4),
|
|
ByoBlockCfg(type='bottle', d=16, c=384, s=2, gs=8, br=4),
|
|
ByoBlockCfg(type='bottle', d=3, c=512, s=2, gs=8, br=4),
|
|
),
|
|
stem_chs=64,
|
|
stem_type='deep',
|
|
stem_pool='',
|
|
downsample='',
|
|
num_features=2048,
|
|
act_layer='silu',
|
|
attn_layer='se',
|
|
attn_kwargs=dict(rd_ratio=0.25),
|
|
norm_layer=partial(EvoNorm2dS0a, group_size=16),
|
|
block_kwargs=dict(bottle_in=True, linear_out=True),
|
|
),
|
|
)
|
|
|
|
@register_model
|
|
def gernet_l(pretrained=False, **kwargs):
|
|
""" GEResNet-Large (GENet-Large from official impl)
|
|
`Neural Architecture Design for GPU-Efficient Networks` - https://arxiv.org/abs/2006.14090
|
|
"""
|
|
return _create_byobnet('gernet_l', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def gernet_m(pretrained=False, **kwargs):
|
|
""" GEResNet-Medium (GENet-Normal from official impl)
|
|
`Neural Architecture Design for GPU-Efficient Networks` - https://arxiv.org/abs/2006.14090
|
|
"""
|
|
return _create_byobnet('gernet_m', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def gernet_s(pretrained=False, **kwargs):
|
|
""" EResNet-Small (GENet-Small from official impl)
|
|
`Neural Architecture Design for GPU-Efficient Networks` - https://arxiv.org/abs/2006.14090
|
|
"""
|
|
return _create_byobnet('gernet_s', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def repvgg_a2(pretrained=False, **kwargs):
|
|
""" RepVGG-A2
|
|
`Making VGG-style ConvNets Great Again` - https://arxiv.org/abs/2101.03697
|
|
"""
|
|
return _create_byobnet('repvgg_a2', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def repvgg_b0(pretrained=False, **kwargs):
|
|
""" RepVGG-B0
|
|
`Making VGG-style ConvNets Great Again` - https://arxiv.org/abs/2101.03697
|
|
"""
|
|
return _create_byobnet('repvgg_b0', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def repvgg_b1(pretrained=False, **kwargs):
|
|
""" RepVGG-B1
|
|
`Making VGG-style ConvNets Great Again` - https://arxiv.org/abs/2101.03697
|
|
"""
|
|
return _create_byobnet('repvgg_b1', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def repvgg_b1g4(pretrained=False, **kwargs):
|
|
""" RepVGG-B1g4
|
|
`Making VGG-style ConvNets Great Again` - https://arxiv.org/abs/2101.03697
|
|
"""
|
|
return _create_byobnet('repvgg_b1g4', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def repvgg_b2(pretrained=False, **kwargs):
|
|
""" RepVGG-B2
|
|
`Making VGG-style ConvNets Great Again` - https://arxiv.org/abs/2101.03697
|
|
"""
|
|
return _create_byobnet('repvgg_b2', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def repvgg_b2g4(pretrained=False, **kwargs):
|
|
""" RepVGG-B2g4
|
|
`Making VGG-style ConvNets Great Again` - https://arxiv.org/abs/2101.03697
|
|
"""
|
|
return _create_byobnet('repvgg_b2g4', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def repvgg_b3(pretrained=False, **kwargs):
|
|
""" RepVGG-B3
|
|
`Making VGG-style ConvNets Great Again` - https://arxiv.org/abs/2101.03697
|
|
"""
|
|
return _create_byobnet('repvgg_b3', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def repvgg_b3g4(pretrained=False, **kwargs):
|
|
""" RepVGG-B3g4
|
|
`Making VGG-style ConvNets Great Again` - https://arxiv.org/abs/2101.03697
|
|
"""
|
|
return _create_byobnet('repvgg_b3g4', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def resnet51q(pretrained=False, **kwargs):
|
|
"""
|
|
"""
|
|
return _create_byobnet('resnet51q', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def resnet61q(pretrained=False, **kwargs):
|
|
"""
|
|
"""
|
|
return _create_byobnet('resnet61q', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def resnext26ts(pretrained=False, **kwargs):
|
|
"""
|
|
"""
|
|
return _create_byobnet('resnext26ts', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def gcresnext26ts(pretrained=False, **kwargs):
|
|
"""
|
|
"""
|
|
return _create_byobnet('gcresnext26ts', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def seresnext26ts(pretrained=False, **kwargs):
|
|
"""
|
|
"""
|
|
return _create_byobnet('seresnext26ts', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def eca_resnext26ts(pretrained=False, **kwargs):
|
|
"""
|
|
"""
|
|
return _create_byobnet('eca_resnext26ts', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def bat_resnext26ts(pretrained=False, **kwargs):
|
|
"""
|
|
"""
|
|
return _create_byobnet('bat_resnext26ts', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def resnet32ts(pretrained=False, **kwargs):
|
|
"""
|
|
"""
|
|
return _create_byobnet('resnet32ts', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def resnet33ts(pretrained=False, **kwargs):
|
|
"""
|
|
"""
|
|
return _create_byobnet('resnet33ts', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def gcresnet33ts(pretrained=False, **kwargs):
|
|
"""
|
|
"""
|
|
return _create_byobnet('gcresnet33ts', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def seresnet33ts(pretrained=False, **kwargs):
|
|
"""
|
|
"""
|
|
return _create_byobnet('seresnet33ts', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def eca_resnet33ts(pretrained=False, **kwargs):
|
|
"""
|
|
"""
|
|
return _create_byobnet('eca_resnet33ts', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def gcresnet50t(pretrained=False, **kwargs):
|
|
"""
|
|
"""
|
|
return _create_byobnet('gcresnet50t', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def gcresnext50ts(pretrained=False, **kwargs):
|
|
"""
|
|
"""
|
|
return _create_byobnet('gcresnext50ts', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def regnetz_b16(pretrained=False, **kwargs):
|
|
"""
|
|
"""
|
|
return _create_byobnet('regnetz_b16', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def regnetz_c16(pretrained=False, **kwargs):
|
|
"""
|
|
"""
|
|
return _create_byobnet('regnetz_c16', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def regnetz_d32(pretrained=False, **kwargs):
|
|
"""
|
|
"""
|
|
return _create_byobnet('regnetz_d32', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def regnetz_d8(pretrained=False, **kwargs):
|
|
"""
|
|
"""
|
|
return _create_byobnet('regnetz_d8', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def regnetz_e8(pretrained=False, **kwargs):
|
|
"""
|
|
"""
|
|
return _create_byobnet('regnetz_e8', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def regnetz_b16_evos(pretrained=False, **kwargs):
|
|
"""
|
|
"""
|
|
return _create_byobnet('regnetz_b16_evos', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def regnetz_c16_evos(pretrained=False, **kwargs):
|
|
"""
|
|
"""
|
|
return _create_byobnet('regnetz_c16_evos', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def regnetz_d8_evob(pretrained=False, **kwargs):
|
|
"""
|
|
"""
|
|
return _create_byobnet('regnetz_d8_evob', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def regnetz_d8_evos(pretrained=False, **kwargs):
|
|
"""
|
|
"""
|
|
return _create_byobnet('regnetz_d8_evos', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def regnetz_e8_evos(pretrained=False, **kwargs):
|
|
"""
|
|
"""
|
|
return _create_byobnet('regnetz_e8_evos', pretrained=pretrained, **kwargs)
|
|
|
|
|
|
def expand_blocks_cfg(stage_blocks_cfg: Union[ByoBlockCfg, Sequence[ByoBlockCfg]]) -> List[ByoBlockCfg]:
|
|
if not isinstance(stage_blocks_cfg, Sequence):
|
|
stage_blocks_cfg = (stage_blocks_cfg,)
|
|
block_cfgs = []
|
|
for i, cfg in enumerate(stage_blocks_cfg):
|
|
block_cfgs += [replace(cfg, d=1) for _ in range(cfg.d)]
|
|
return block_cfgs
|
|
|
|
|
|
def num_groups(group_size, channels):
|
|
if not group_size: # 0 or None
|
|
return 1 # normal conv with 1 group
|
|
else:
|
|
# NOTE group_size == 1 -> depthwise conv
|
|
assert channels % group_size == 0
|
|
return channels // group_size
|
|
|
|
|
|
@dataclass
|
|
class LayerFn:
|
|
conv_norm_act: Callable = ConvNormAct
|
|
norm_act: Callable = BatchNormAct2d
|
|
act: Callable = nn.ReLU
|
|
attn: Optional[Callable] = None
|
|
self_attn: Optional[Callable] = None
|
|
|
|
|
|
class DownsampleAvg(nn.Module):
|
|
def __init__(self, in_chs, out_chs, stride=1, dilation=1, apply_act=False, layers: LayerFn = None):
|
|
""" AvgPool Downsampling as in 'D' ResNet variants."""
|
|
super(DownsampleAvg, self).__init__()
|
|
layers = layers or LayerFn()
|
|
avg_stride = stride if dilation == 1 else 1
|
|
if stride > 1 or dilation > 1:
|
|
avg_pool_fn = AvgPool2dSame if avg_stride == 1 and dilation > 1 else nn.AvgPool2d
|
|
self.pool = avg_pool_fn(2, avg_stride, ceil_mode=True, count_include_pad=False)
|
|
else:
|
|
self.pool = nn.Identity()
|
|
self.conv = layers.conv_norm_act(in_chs, out_chs, 1, apply_act=apply_act)
|
|
|
|
def forward(self, x):
|
|
return self.conv(self.pool(x))
|
|
|
|
|
|
def create_shortcut(downsample_type, layers: LayerFn, in_chs, out_chs, stride, dilation, **kwargs):
|
|
assert downsample_type in ('avg', 'conv1x1', '')
|
|
if in_chs != out_chs or stride != 1 or dilation[0] != dilation[1]:
|
|
if not downsample_type:
|
|
return None # no shortcut
|
|
elif downsample_type == 'avg':
|
|
return DownsampleAvg(in_chs, out_chs, stride=stride, dilation=dilation[0], **kwargs)
|
|
else:
|
|
return layers.conv_norm_act(in_chs, out_chs, kernel_size=1, stride=stride, dilation=dilation[0], **kwargs)
|
|
else:
|
|
return nn.Identity() # identity shortcut
|
|
|
|
|
|
class BasicBlock(nn.Module):
|
|
""" ResNet Basic Block - kxk + kxk
|
|
"""
|
|
|
|
def __init__(
|
|
self, in_chs, out_chs, kernel_size=3, stride=1, dilation=(1, 1), group_size=None, bottle_ratio=1.0,
|
|
downsample='avg', attn_last=True, linear_out=False, layers: LayerFn = None, drop_block=None,
|
|
drop_path_rate=0.):
|
|
super(BasicBlock, self).__init__()
|
|
layers = layers or LayerFn()
|
|
mid_chs = make_divisible(out_chs * bottle_ratio)
|
|
groups = num_groups(group_size, mid_chs)
|
|
|
|
self.shortcut = create_shortcut(
|
|
downsample, in_chs=in_chs, out_chs=out_chs, stride=stride, dilation=dilation,
|
|
apply_act=False, layers=layers)
|
|
|
|
self.conv1_kxk = layers.conv_norm_act(in_chs, mid_chs, kernel_size, stride=stride, dilation=dilation[0])
|
|
self.attn = nn.Identity() if attn_last or layers.attn is None else layers.attn(mid_chs)
|
|
self.conv2_kxk = layers.conv_norm_act(
|
|
mid_chs, out_chs, kernel_size, dilation=dilation[1], groups=groups, drop_layer=drop_block, apply_act=False)
|
|
self.attn_last = nn.Identity() if not attn_last or layers.attn is None else layers.attn(out_chs)
|
|
self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()
|
|
self.act = nn.Identity() if linear_out else layers.act(inplace=True)
|
|
|
|
def init_weights(self, zero_init_last: bool = False):
|
|
if zero_init_last and self.shortcut is not None:
|
|
nn.init.zeros_(self.conv2_kxk.bn.weight)
|
|
for attn in (self.attn, self.attn_last):
|
|
if hasattr(attn, 'reset_parameters'):
|
|
attn.reset_parameters()
|
|
|
|
def forward(self, x):
|
|
shortcut = x
|
|
x = self.conv1_kxk(x)
|
|
x = self.conv2_kxk(x)
|
|
x = self.attn(x)
|
|
x = self.drop_path(x)
|
|
if self.shortcut is not None:
|
|
x = x + self.shortcut(shortcut)
|
|
return self.act(x)
|
|
|
|
|
|
class BottleneckBlock(nn.Module):
|
|
""" ResNet-like Bottleneck Block - 1x1 - kxk - 1x1
|
|
"""
|
|
|
|
def __init__(self, in_chs, out_chs, kernel_size=3, stride=1, dilation=(1, 1), bottle_ratio=1., group_size=None,
|
|
downsample='avg', attn_last=False, linear_out=False, extra_conv=False, bottle_in=False,
|
|
layers: LayerFn = None, drop_block=None, drop_path_rate=0.):
|
|
super(BottleneckBlock, self).__init__()
|
|
layers = layers or LayerFn()
|
|
mid_chs = make_divisible((in_chs if bottle_in else out_chs) * bottle_ratio)
|
|
groups = num_groups(group_size, mid_chs)
|
|
|
|
self.shortcut = create_shortcut(
|
|
downsample, in_chs=in_chs, out_chs=out_chs, stride=stride, dilation=dilation,
|
|
apply_act=False, layers=layers)
|
|
|
|
self.conv1_1x1 = layers.conv_norm_act(in_chs, mid_chs, 1)
|
|
self.conv2_kxk = layers.conv_norm_act(
|
|
mid_chs, mid_chs, kernel_size, stride=stride, dilation=dilation[0], groups=groups, drop_layer=drop_block)
|
|
if extra_conv:
|
|
self.conv2b_kxk = layers.conv_norm_act(mid_chs, mid_chs, kernel_size, dilation=dilation[1], groups=groups)
|
|
else:
|
|
self.conv2b_kxk = nn.Identity()
|
|
self.attn = nn.Identity() if attn_last or layers.attn is None else layers.attn(mid_chs)
|
|
self.conv3_1x1 = layers.conv_norm_act(mid_chs, out_chs, 1, apply_act=False)
|
|
self.attn_last = nn.Identity() if not attn_last or layers.attn is None else layers.attn(out_chs)
|
|
self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()
|
|
self.act = nn.Identity() if linear_out else layers.act(inplace=True)
|
|
|
|
def init_weights(self, zero_init_last: bool = False):
|
|
if zero_init_last and self.shortcut is not None:
|
|
nn.init.zeros_(self.conv3_1x1.bn.weight)
|
|
for attn in (self.attn, self.attn_last):
|
|
if hasattr(attn, 'reset_parameters'):
|
|
attn.reset_parameters()
|
|
|
|
def forward(self, x):
|
|
shortcut = x
|
|
x = self.conv1_1x1(x)
|
|
x = self.conv2_kxk(x)
|
|
x = self.conv2b_kxk(x)
|
|
x = self.attn(x)
|
|
x = self.conv3_1x1(x)
|
|
x = self.attn_last(x)
|
|
x = self.drop_path(x)
|
|
if self.shortcut is not None:
|
|
x = x + self.shortcut(shortcut)
|
|
return self.act(x)
|
|
|
|
|
|
class DarkBlock(nn.Module):
|
|
""" DarkNet-like (1x1 + 3x3 w/ stride) block
|
|
|
|
The GE-Net impl included a 1x1 + 3x3 block in their search space. It was not used in the feature models.
|
|
This block is pretty much a DarkNet block (also DenseNet) hence the name. Neither DarkNet or DenseNet
|
|
uses strides within the block (external 3x3 or maxpool downsampling is done in front of the block repeats).
|
|
|
|
If one does want to use a lot of these blocks w/ stride, I'd recommend using the EdgeBlock (3x3 /w stride + 1x1)
|
|
for more optimal compute.
|
|
"""
|
|
|
|
def __init__(self, in_chs, out_chs, kernel_size=3, stride=1, dilation=(1, 1), bottle_ratio=1.0, group_size=None,
|
|
downsample='avg', attn_last=True, linear_out=False, layers: LayerFn = None, drop_block=None,
|
|
drop_path_rate=0.):
|
|
super(DarkBlock, self).__init__()
|
|
layers = layers or LayerFn()
|
|
mid_chs = make_divisible(out_chs * bottle_ratio)
|
|
groups = num_groups(group_size, mid_chs)
|
|
|
|
self.shortcut = create_shortcut(
|
|
downsample, in_chs=in_chs, out_chs=out_chs, stride=stride, dilation=dilation,
|
|
apply_act=False, layers=layers)
|
|
|
|
self.conv1_1x1 = layers.conv_norm_act(in_chs, mid_chs, 1)
|
|
self.attn = nn.Identity() if attn_last or layers.attn is None else layers.attn(mid_chs)
|
|
self.conv2_kxk = layers.conv_norm_act(
|
|
mid_chs, out_chs, kernel_size, stride=stride, dilation=dilation[0],
|
|
groups=groups, drop_layer=drop_block, apply_act=False)
|
|
self.attn_last = nn.Identity() if not attn_last or layers.attn is None else layers.attn(out_chs)
|
|
self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()
|
|
self.act = nn.Identity() if linear_out else layers.act(inplace=True)
|
|
|
|
def init_weights(self, zero_init_last: bool = False):
|
|
if zero_init_last and self.shortcut is not None:
|
|
nn.init.zeros_(self.conv2_kxk.bn.weight)
|
|
for attn in (self.attn, self.attn_last):
|
|
if hasattr(attn, 'reset_parameters'):
|
|
attn.reset_parameters()
|
|
|
|
def forward(self, x):
|
|
shortcut = x
|
|
x = self.conv1_1x1(x)
|
|
x = self.attn(x)
|
|
x = self.conv2_kxk(x)
|
|
x = self.attn_last(x)
|
|
x = self.drop_path(x)
|
|
if self.shortcut is not None:
|
|
x = x + self.shortcut(shortcut)
|
|
return self.act(x)
|
|
|
|
|
|
class EdgeBlock(nn.Module):
|
|
""" EdgeResidual-like (3x3 + 1x1) block
|
|
|
|
A two layer block like DarkBlock, but with the order of the 3x3 and 1x1 convs reversed.
|
|
Very similar to the EfficientNet Edge-Residual block but this block it ends with activations, is
|
|
intended to be used with either expansion or bottleneck contraction, and can use DW/group/non-grouped convs.
|
|
|
|
FIXME is there a more common 3x3 + 1x1 conv block to name this after?
|
|
"""
|
|
|
|
def __init__(self, in_chs, out_chs, kernel_size=3, stride=1, dilation=(1, 1), bottle_ratio=1.0, group_size=None,
|
|
downsample='avg', attn_last=False, linear_out=False, layers: LayerFn = None,
|
|
drop_block=None, drop_path_rate=0.):
|
|
super(EdgeBlock, self).__init__()
|
|
layers = layers or LayerFn()
|
|
mid_chs = make_divisible(out_chs * bottle_ratio)
|
|
groups = num_groups(group_size, mid_chs)
|
|
|
|
self.shortcut = create_shortcut(
|
|
downsample, in_chs=in_chs, out_chs=out_chs, stride=stride, dilation=dilation,
|
|
apply_act=False, layers=layers)
|
|
|
|
self.conv1_kxk = layers.conv_norm_act(
|
|
in_chs, mid_chs, kernel_size, stride=stride, dilation=dilation[0], groups=groups, drop_layer=drop_block)
|
|
self.attn = nn.Identity() if attn_last or layers.attn is None else layers.attn(mid_chs)
|
|
self.conv2_1x1 = layers.conv_norm_act(mid_chs, out_chs, 1, apply_act=False)
|
|
self.attn_last = nn.Identity() if not attn_last or layers.attn is None else layers.attn(out_chs)
|
|
self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()
|
|
self.act = nn.Identity() if linear_out else layers.act(inplace=True)
|
|
|
|
def init_weights(self, zero_init_last: bool = False):
|
|
if zero_init_last and self.shortcut is not None:
|
|
nn.init.zeros_(self.conv2_1x1.bn.weight)
|
|
for attn in (self.attn, self.attn_last):
|
|
if hasattr(attn, 'reset_parameters'):
|
|
attn.reset_parameters()
|
|
|
|
def forward(self, x):
|
|
shortcut = x
|
|
x = self.conv1_kxk(x)
|
|
x = self.attn(x)
|
|
x = self.conv2_1x1(x)
|
|
x = self.attn_last(x)
|
|
x = self.drop_path(x)
|
|
if self.shortcut is not None:
|
|
x = x + self.shortcut(shortcut)
|
|
return self.act(x)
|
|
|
|
|
|
class RepVggBlock(nn.Module):
|
|
""" RepVGG Block.
|
|
|
|
Adapted from impl at https://github.com/DingXiaoH/RepVGG
|
|
|
|
This version does not currently support the deploy optimization. It is currently fixed in 'train' mode.
|
|
"""
|
|
|
|
def __init__(self, in_chs, out_chs, kernel_size=3, stride=1, dilation=(1, 1), bottle_ratio=1.0, group_size=None,
|
|
downsample='', layers: LayerFn = None, drop_block=None, drop_path_rate=0.):
|
|
super(RepVggBlock, self).__init__()
|
|
layers = layers or LayerFn()
|
|
groups = num_groups(group_size, in_chs)
|
|
|
|
use_ident = in_chs == out_chs and stride == 1 and dilation[0] == dilation[1]
|
|
self.identity = layers.norm_act(out_chs, apply_act=False) if use_ident else None
|
|
self.conv_kxk = layers.conv_norm_act(
|
|
in_chs, out_chs, kernel_size, stride=stride, dilation=dilation[0],
|
|
groups=groups, drop_layer=drop_block, apply_act=False)
|
|
self.conv_1x1 = layers.conv_norm_act(in_chs, out_chs, 1, stride=stride, groups=groups, apply_act=False)
|
|
self.attn = nn.Identity() if layers.attn is None else layers.attn(out_chs)
|
|
self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0. and use_ident else nn.Identity()
|
|
self.act = layers.act(inplace=True)
|
|
|
|
def init_weights(self, zero_init_last: bool = False):
|
|
# NOTE this init overrides that base model init with specific changes for the block type
|
|
for m in self.modules():
|
|
if isinstance(m, nn.BatchNorm2d):
|
|
nn.init.normal_(m.weight, .1, .1)
|
|
nn.init.normal_(m.bias, 0, .1)
|
|
if hasattr(self.attn, 'reset_parameters'):
|
|
self.attn.reset_parameters()
|
|
|
|
def forward(self, x):
|
|
if self.identity is None:
|
|
x = self.conv_1x1(x) + self.conv_kxk(x)
|
|
else:
|
|
identity = self.identity(x)
|
|
x = self.conv_1x1(x) + self.conv_kxk(x)
|
|
x = self.drop_path(x) # not in the paper / official impl, experimental
|
|
x = x + identity
|
|
x = self.attn(x) # no attn in the paper / official impl, experimental
|
|
return self.act(x)
|
|
|
|
|
|
class SelfAttnBlock(nn.Module):
|
|
""" ResNet-like Bottleneck Block - 1x1 - optional kxk - self attn - 1x1
|
|
"""
|
|
|
|
def __init__(self, in_chs, out_chs, kernel_size=3, stride=1, dilation=(1, 1), bottle_ratio=1., group_size=None,
|
|
downsample='avg', extra_conv=False, linear_out=False, bottle_in=False, post_attn_na=True,
|
|
feat_size=None, layers: LayerFn = None, drop_block=None, drop_path_rate=0.):
|
|
super(SelfAttnBlock, self).__init__()
|
|
assert layers is not None
|
|
mid_chs = make_divisible((in_chs if bottle_in else out_chs) * bottle_ratio)
|
|
groups = num_groups(group_size, mid_chs)
|
|
|
|
self.shortcut = create_shortcut(
|
|
downsample, in_chs=in_chs, out_chs=out_chs, stride=stride, dilation=dilation,
|
|
apply_act=False, layers=layers)
|
|
|
|
self.conv1_1x1 = layers.conv_norm_act(in_chs, mid_chs, 1)
|
|
if extra_conv:
|
|
self.conv2_kxk = layers.conv_norm_act(
|
|
mid_chs, mid_chs, kernel_size, stride=stride, dilation=dilation[0],
|
|
groups=groups, drop_layer=drop_block)
|
|
stride = 1 # striding done via conv if enabled
|
|
else:
|
|
self.conv2_kxk = nn.Identity()
|
|
opt_kwargs = {} if feat_size is None else dict(feat_size=feat_size)
|
|
# FIXME need to dilate self attn to have dilated network support, moop moop
|
|
self.self_attn = layers.self_attn(mid_chs, stride=stride, **opt_kwargs)
|
|
self.post_attn = layers.norm_act(mid_chs) if post_attn_na else nn.Identity()
|
|
self.conv3_1x1 = layers.conv_norm_act(mid_chs, out_chs, 1, apply_act=False)
|
|
self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()
|
|
self.act = nn.Identity() if linear_out else layers.act(inplace=True)
|
|
|
|
def init_weights(self, zero_init_last: bool = False):
|
|
if zero_init_last and self.shortcut is not None:
|
|
nn.init.zeros_(self.conv3_1x1.bn.weight)
|
|
if hasattr(self.self_attn, 'reset_parameters'):
|
|
self.self_attn.reset_parameters()
|
|
|
|
def forward(self, x):
|
|
shortcut = x
|
|
x = self.conv1_1x1(x)
|
|
x = self.conv2_kxk(x)
|
|
x = self.self_attn(x)
|
|
x = self.post_attn(x)
|
|
x = self.conv3_1x1(x)
|
|
x = self.drop_path(x)
|
|
if self.shortcut is not None:
|
|
x = x + self.shortcut(shortcut)
|
|
return self.act(x)
|
|
|
|
_block_registry = dict(
|
|
basic=BasicBlock,
|
|
bottle=BottleneckBlock,
|
|
dark=DarkBlock,
|
|
edge=EdgeBlock,
|
|
rep=RepVggBlock,
|
|
self_attn=SelfAttnBlock,
|
|
)
|
|
|
|
|
|
def register_block(block_type:str, block_fn: nn.Module):
|
|
_block_registry[block_type] = block_fn
|
|
|
|
|
|
def create_block(block: Union[str, nn.Module], **kwargs):
|
|
if isinstance(block, (nn.Module, partial)):
|
|
return block(**kwargs)
|
|
assert block in _block_registry, f'Unknown block type ({block}'
|
|
return _block_registry[block](**kwargs)
|
|
|
|
|
|
class Stem(nn.Sequential):
|
|
|
|
def __init__(self, in_chs, out_chs, kernel_size=3, stride=4, pool='maxpool',
|
|
num_rep=3, num_act=None, chs_decay=0.5, layers: LayerFn = None):
|
|
super().__init__()
|
|
assert stride in (2, 4)
|
|
layers = layers or LayerFn()
|
|
|
|
if isinstance(out_chs, (list, tuple)):
|
|
num_rep = len(out_chs)
|
|
stem_chs = out_chs
|
|
else:
|
|
stem_chs = [round(out_chs * chs_decay ** i) for i in range(num_rep)][::-1]
|
|
|
|
self.stride = stride
|
|
self.feature_info = [] # track intermediate features
|
|
prev_feat = ''
|
|
stem_strides = [2] + [1] * (num_rep - 1)
|
|
if stride == 4 and not pool:
|
|
# set last conv in stack to be strided if stride == 4 and no pooling layer
|
|
stem_strides[-1] = 2
|
|
|
|
num_act = num_rep if num_act is None else num_act
|
|
# if num_act < num_rep, first convs in stack won't have bn + act
|
|
stem_norm_acts = [False] * (num_rep - num_act) + [True] * num_act
|
|
prev_chs = in_chs
|
|
curr_stride = 1
|
|
for i, (ch, s, na) in enumerate(zip(stem_chs, stem_strides, stem_norm_acts)):
|
|
layer_fn = layers.conv_norm_act if na else create_conv2d
|
|
conv_name = f'conv{i + 1}'
|
|
if i > 0 and s > 1:
|
|
self.feature_info.append(dict(num_chs=prev_chs, reduction=curr_stride, module=prev_feat))
|
|
self.add_module(conv_name, layer_fn(prev_chs, ch, kernel_size=kernel_size, stride=s))
|
|
prev_chs = ch
|
|
curr_stride *= s
|
|
prev_feat = conv_name
|
|
|
|
if pool and 'max' in pool.lower():
|
|
self.feature_info.append(dict(num_chs=prev_chs, reduction=curr_stride, module=prev_feat))
|
|
self.add_module('pool', nn.MaxPool2d(3, 2, 1))
|
|
curr_stride *= 2
|
|
prev_feat = 'pool'
|
|
|
|
self.feature_info.append(dict(num_chs=prev_chs, reduction=curr_stride, module=prev_feat))
|
|
assert curr_stride == stride
|
|
|
|
|
|
def create_byob_stem(in_chs, out_chs, stem_type='', pool_type='', feat_prefix='stem', layers: LayerFn = None):
|
|
layers = layers or LayerFn()
|
|
assert stem_type in ('', 'quad', 'quad2', 'tiered', 'deep', 'rep', '7x7', '3x3')
|
|
if 'quad' in stem_type:
|
|
# based on NFNet stem, stack of 4 3x3 convs
|
|
num_act = 2 if 'quad2' in stem_type else None
|
|
stem = Stem(in_chs, out_chs, num_rep=4, num_act=num_act, pool=pool_type, layers=layers)
|
|
elif 'tiered' in stem_type:
|
|
# 3x3 stack of 3 convs as in my ResNet-T
|
|
stem = Stem(in_chs, (3 * out_chs // 8, out_chs // 2, out_chs), pool=pool_type, layers=layers)
|
|
elif 'deep' in stem_type:
|
|
# 3x3 stack of 3 convs as in ResNet-D
|
|
stem = Stem(in_chs, out_chs, num_rep=3, chs_decay=1.0, pool=pool_type, layers=layers)
|
|
elif 'rep' in stem_type:
|
|
stem = RepVggBlock(in_chs, out_chs, stride=2, layers=layers)
|
|
elif '7x7' in stem_type:
|
|
# 7x7 stem conv as in ResNet
|
|
if pool_type:
|
|
stem = Stem(in_chs, out_chs, 7, num_rep=1, pool=pool_type, layers=layers)
|
|
else:
|
|
stem = layers.conv_norm_act(in_chs, out_chs, 7, stride=2)
|
|
else:
|
|
# 3x3 stem conv as in RegNet is the default
|
|
if pool_type:
|
|
stem = Stem(in_chs, out_chs, 3, num_rep=1, pool=pool_type, layers=layers)
|
|
else:
|
|
stem = layers.conv_norm_act(in_chs, out_chs, 3, stride=2)
|
|
|
|
if isinstance(stem, Stem):
|
|
feature_info = [dict(f, module='.'.join([feat_prefix, f['module']])) for f in stem.feature_info]
|
|
else:
|
|
feature_info = [dict(num_chs=out_chs, reduction=2, module=feat_prefix)]
|
|
return stem, feature_info
|
|
|
|
|
|
def reduce_feat_size(feat_size, stride=2):
|
|
return None if feat_size is None else tuple([s // stride for s in feat_size])
|
|
|
|
|
|
def override_kwargs(block_kwargs, model_kwargs):
|
|
""" Override model level attn/self-attn/block kwargs w/ block level
|
|
|
|
NOTE: kwargs are NOT merged across levels, block_kwargs will fully replace model_kwargs
|
|
for the block if set to anything that isn't None.
|
|
|
|
i.e. an empty block_kwargs dict will remove kwargs set at model level for that block
|
|
"""
|
|
out_kwargs = block_kwargs if block_kwargs is not None else model_kwargs
|
|
return out_kwargs or {} # make sure None isn't returned
|
|
|
|
|
|
def update_block_kwargs(block_kwargs: Dict[str, Any], block_cfg: ByoBlockCfg, model_cfg: ByoModelCfg, ):
|
|
layer_fns = block_kwargs['layers']
|
|
|
|
# override attn layer / args with block local config
|
|
attn_set = block_cfg.attn_layer is not None
|
|
if attn_set or block_cfg.attn_kwargs is not None:
|
|
# override attn layer config
|
|
if attn_set and not block_cfg.attn_layer:
|
|
# empty string for attn_layer type will disable attn for this block
|
|
attn_layer = None
|
|
else:
|
|
attn_kwargs = override_kwargs(block_cfg.attn_kwargs, model_cfg.attn_kwargs)
|
|
attn_layer = block_cfg.attn_layer or model_cfg.attn_layer
|
|
attn_layer = partial(get_attn(attn_layer), **attn_kwargs) if attn_layer is not None else None
|
|
layer_fns = replace(layer_fns, attn=attn_layer)
|
|
|
|
# override self-attn layer / args with block local cfg
|
|
self_attn_set = block_cfg.self_attn_layer is not None
|
|
if self_attn_set or block_cfg.self_attn_kwargs is not None:
|
|
# override attn layer config
|
|
if self_attn_set and not block_cfg.self_attn_layer: # attn_layer == ''
|
|
# empty string for self_attn_layer type will disable attn for this block
|
|
self_attn_layer = None
|
|
else:
|
|
self_attn_kwargs = override_kwargs(block_cfg.self_attn_kwargs, model_cfg.self_attn_kwargs)
|
|
self_attn_layer = block_cfg.self_attn_layer or model_cfg.self_attn_layer
|
|
self_attn_layer = partial(get_attn(self_attn_layer), **self_attn_kwargs) \
|
|
if self_attn_layer is not None else None
|
|
layer_fns = replace(layer_fns, self_attn=self_attn_layer)
|
|
|
|
block_kwargs['layers'] = layer_fns
|
|
|
|
# add additional block_kwargs specified in block_cfg or model_cfg, precedence to block if set
|
|
block_kwargs.update(override_kwargs(block_cfg.block_kwargs, model_cfg.block_kwargs))
|
|
|
|
|
|
def create_byob_stages(
|
|
cfg: ByoModelCfg, drop_path_rate: float, output_stride: int, stem_feat: Dict[str, Any],
|
|
feat_size: Optional[int] = None,
|
|
layers: Optional[LayerFn] = None,
|
|
block_kwargs_fn: Optional[Callable] = update_block_kwargs):
|
|
|
|
layers = layers or LayerFn()
|
|
feature_info = []
|
|
block_cfgs = [expand_blocks_cfg(s) for s in cfg.blocks]
|
|
depths = [sum([bc.d for bc in stage_bcs]) for stage_bcs in block_cfgs]
|
|
dpr = [x.tolist() for x in torch.linspace(0, drop_path_rate, sum(depths)).split(depths)]
|
|
dilation = 1
|
|
net_stride = stem_feat['reduction']
|
|
prev_chs = stem_feat['num_chs']
|
|
prev_feat = stem_feat
|
|
stages = []
|
|
for stage_idx, stage_block_cfgs in enumerate(block_cfgs):
|
|
stride = stage_block_cfgs[0].s
|
|
if stride != 1 and prev_feat:
|
|
feature_info.append(prev_feat)
|
|
if net_stride >= output_stride and stride > 1:
|
|
dilation *= stride
|
|
stride = 1
|
|
net_stride *= stride
|
|
first_dilation = 1 if dilation in (1, 2) else 2
|
|
|
|
blocks = []
|
|
for block_idx, block_cfg in enumerate(stage_block_cfgs):
|
|
out_chs = make_divisible(block_cfg.c * cfg.width_factor)
|
|
group_size = block_cfg.gs
|
|
if isinstance(group_size, Callable):
|
|
group_size = group_size(out_chs, block_idx)
|
|
block_kwargs = dict( # Blocks used in this model must accept these arguments
|
|
in_chs=prev_chs,
|
|
out_chs=out_chs,
|
|
stride=stride if block_idx == 0 else 1,
|
|
dilation=(first_dilation, dilation),
|
|
group_size=group_size,
|
|
bottle_ratio=block_cfg.br,
|
|
downsample=cfg.downsample,
|
|
drop_path_rate=dpr[stage_idx][block_idx],
|
|
layers=layers,
|
|
)
|
|
if block_cfg.type in ('self_attn',):
|
|
# add feat_size arg for blocks that support/need it
|
|
block_kwargs['feat_size'] = feat_size
|
|
block_kwargs_fn(block_kwargs, block_cfg=block_cfg, model_cfg=cfg)
|
|
blocks += [create_block(block_cfg.type, **block_kwargs)]
|
|
first_dilation = dilation
|
|
prev_chs = out_chs
|
|
if stride > 1 and block_idx == 0:
|
|
feat_size = reduce_feat_size(feat_size, stride)
|
|
|
|
stages += [nn.Sequential(*blocks)]
|
|
prev_feat = dict(num_chs=prev_chs, reduction=net_stride, module=f'stages.{stage_idx}')
|
|
|
|
feature_info.append(prev_feat)
|
|
return nn.Sequential(*stages), feature_info
|
|
|
|
|
|
def get_layer_fns(cfg: ByoModelCfg):
|
|
act = get_act_layer(cfg.act_layer)
|
|
norm_act = get_norm_act_layer(norm_layer=cfg.norm_layer, act_layer=act)
|
|
conv_norm_act = partial(ConvNormAct, norm_layer=cfg.norm_layer, act_layer=act)
|
|
attn = partial(get_attn(cfg.attn_layer), **cfg.attn_kwargs) if cfg.attn_layer else None
|
|
self_attn = partial(get_attn(cfg.self_attn_layer), **cfg.self_attn_kwargs) if cfg.self_attn_layer else None
|
|
layer_fn = LayerFn(conv_norm_act=conv_norm_act, norm_act=norm_act, act=act, attn=attn, self_attn=self_attn)
|
|
return layer_fn
|
|
|
|
|
|
class ByobNet(nn.Module):
|
|
""" 'Bring-your-own-blocks' Net
|
|
|
|
A flexible network backbone that allows building model stem + blocks via
|
|
dataclass cfg definition w/ factory functions for module instantiation.
|
|
|
|
Current assumption is that both stem and blocks are in conv-bn-act order (w/ block ending in act).
|
|
"""
|
|
def __init__(self, cfg: ByoModelCfg, num_classes=1000, in_chans=3, global_pool='avg', output_stride=32,
|
|
zero_init_last=True, img_size=None, drop_rate=0., drop_path_rate=0.):
|
|
super().__init__()
|
|
self.num_classes = num_classes
|
|
self.drop_rate = drop_rate
|
|
layers = get_layer_fns(cfg)
|
|
if cfg.fixed_input_size:
|
|
assert img_size is not None, 'img_size argument is required for fixed input size model'
|
|
feat_size = to_2tuple(img_size) if img_size is not None else None
|
|
|
|
self.feature_info = []
|
|
stem_chs = int(round((cfg.stem_chs or cfg.blocks[0].c) * cfg.width_factor))
|
|
self.stem, stem_feat = create_byob_stem(in_chans, stem_chs, cfg.stem_type, cfg.stem_pool, layers=layers)
|
|
self.feature_info.extend(stem_feat[:-1])
|
|
feat_size = reduce_feat_size(feat_size, stride=stem_feat[-1]['reduction'])
|
|
|
|
self.stages, stage_feat = create_byob_stages(
|
|
cfg, drop_path_rate, output_stride, stem_feat[-1], layers=layers, feat_size=feat_size)
|
|
self.feature_info.extend(stage_feat[:-1])
|
|
|
|
prev_chs = stage_feat[-1]['num_chs']
|
|
if cfg.num_features:
|
|
self.num_features = int(round(cfg.width_factor * cfg.num_features))
|
|
self.final_conv = layers.conv_norm_act(prev_chs, self.num_features, 1)
|
|
else:
|
|
self.num_features = prev_chs
|
|
self.final_conv = nn.Identity()
|
|
self.feature_info += [
|
|
dict(num_chs=self.num_features, reduction=stage_feat[-1]['reduction'], module='final_conv')]
|
|
|
|
self.head = ClassifierHead(self.num_features, num_classes, pool_type=global_pool, drop_rate=self.drop_rate)
|
|
|
|
# init weights
|
|
named_apply(partial(_init_weights, zero_init_last=zero_init_last), self)
|
|
|
|
def get_classifier(self):
|
|
return self.head.fc
|
|
|
|
def reset_classifier(self, num_classes, global_pool='avg'):
|
|
self.head = ClassifierHead(self.num_features, num_classes, pool_type=global_pool, drop_rate=self.drop_rate)
|
|
|
|
def forward_features(self, x):
|
|
x = self.stem(x)
|
|
x = self.stages(x)
|
|
x = self.final_conv(x)
|
|
return x
|
|
|
|
def forward(self, x):
|
|
x = self.forward_features(x)
|
|
x = self.head(x)
|
|
return x
|
|
|
|
|
|
def _init_weights(module, name='', zero_init_last=False):
|
|
if isinstance(module, nn.Conv2d):
|
|
fan_out = module.kernel_size[0] * module.kernel_size[1] * module.out_channels
|
|
fan_out //= module.groups
|
|
module.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
|
|
if module.bias is not None:
|
|
module.bias.data.zero_()
|
|
elif isinstance(module, nn.Linear):
|
|
nn.init.normal_(module.weight, mean=0.0, std=0.01)
|
|
if module.bias is not None:
|
|
nn.init.zeros_(module.bias)
|
|
elif isinstance(module, nn.BatchNorm2d):
|
|
nn.init.ones_(module.weight)
|
|
nn.init.zeros_(module.bias)
|
|
elif hasattr(module, 'init_weights'):
|
|
module.init_weights(zero_init_last=zero_init_last)
|
|
|
|
|
|
def _create_byobnet(variant, pretrained=False, **kwargs):
|
|
return build_model_with_cfg(
|
|
ByobNet, variant, pretrained,
|
|
default_cfg=default_cfgs[variant],
|
|
model_cfg=model_cfgs[variant],
|
|
feature_cfg=dict(flatten_sequential=True),
|
|
**kwargs)
|