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295 lines
10 KiB
295 lines
10 KiB
"""PyTorch SelecSLS Net example for ImageNet Classification
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License: CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/legalcode)
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Author: Dushyant Mehta (@mehtadushy)
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SelecSLS (core) Network Architecture as proposed in "XNect: Real-time Multi-person 3D
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Human Pose Estimation with a Single RGB Camera, Mehta et al."
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https://arxiv.org/abs/1907.00837
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Based on ResNet implementation in https://github.com/rwightman/pytorch-image-models
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and SelecSLS Net implementation in https://github.com/mehtadushy/SelecSLS-Pytorch
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"""
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import math
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from .registry import register_model
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from .helpers import load_pretrained
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from .layers import SelectAdaptivePool2d
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from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
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__all__ = ['SelecSLS'] # model_registry will add each entrypoint fn to this
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def _cfg(url='', **kwargs):
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return {
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'url': url,
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'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (4, 4),
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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', 'classifier': 'fc',
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**kwargs
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}
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default_cfgs = {
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'selecsls42': _cfg(
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url='',
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interpolation='bicubic'),
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'selecsls42b': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-selecsls/selecsls42b-8af30141.pth',
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interpolation='bicubic'),
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'selecsls60': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-selecsls/selecsls60-bbf87526.pth',
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interpolation='bicubic'),
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'selecsls60b': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-selecsls/selecsls60b-94e619b5.pth',
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interpolation='bicubic'),
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'selecsls84': _cfg(
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url='',
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interpolation='bicubic'),
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}
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def conv_bn(in_chs, out_chs, k=3, stride=1, padding=None, dilation=1):
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if padding is None:
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padding = ((stride - 1) + dilation * (k - 1)) // 2
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return nn.Sequential(
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nn.Conv2d(in_chs, out_chs, k, stride, padding=padding, dilation=dilation, bias=False),
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nn.BatchNorm2d(out_chs),
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nn.ReLU(inplace=True)
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)
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class SelecSLSBlock(nn.Module):
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def __init__(self, in_chs, skip_chs, mid_chs, out_chs, is_first, stride, dilation=1):
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super(SelecSLSBlock, self).__init__()
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self.stride = stride
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self.is_first = is_first
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assert stride in [1, 2]
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# Process input with 4 conv blocks with the same number of input and output channels
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self.conv1 = conv_bn(in_chs, mid_chs, 3, stride, dilation=dilation)
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self.conv2 = conv_bn(mid_chs, mid_chs, 1)
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self.conv3 = conv_bn(mid_chs, mid_chs // 2, 3)
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self.conv4 = conv_bn(mid_chs // 2, mid_chs, 1)
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self.conv5 = conv_bn(mid_chs, mid_chs // 2, 3)
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self.conv6 = conv_bn(2 * mid_chs + (0 if is_first else skip_chs), out_chs, 1)
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def forward(self, x):
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assert isinstance(x, list)
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assert len(x) in [1, 2]
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d1 = self.conv1(x[0])
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d2 = self.conv3(self.conv2(d1))
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d3 = self.conv5(self.conv4(d2))
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if self.is_first:
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out = self.conv6(torch.cat([d1, d2, d3], 1))
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return [out, out]
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else:
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return [self.conv6(torch.cat([d1, d2, d3, x[1]], 1)), x[1]]
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class SelecSLS(nn.Module):
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"""SelecSLS42 / SelecSLS60 / SelecSLS84
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Parameters
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----------
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cfg : network config dictionary specifying block type, feature, and head args
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num_classes : int, default 1000
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Number of classification classes.
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in_chans : int, default 3
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Number of input (color) channels.
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drop_rate : float, default 0.
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Dropout probability before classifier, for training
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global_pool : str, default 'avg'
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Global pooling type. One of 'avg', 'max', 'avgmax', 'catavgmax'
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"""
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def __init__(self, cfg, num_classes=1000, in_chans=3, drop_rate=0.0, global_pool='avg'):
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self.num_classes = num_classes
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self.drop_rate = drop_rate
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super(SelecSLS, self).__init__()
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self.stem = conv_bn(in_chans, 32, stride=2)
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self.features = nn.Sequential(*[cfg['block'](*block_args) for block_args in cfg['features']])
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self.head = nn.Sequential(*[conv_bn(*conv_args) for conv_args in cfg['head']])
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self.num_features = cfg['num_features']
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self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
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self.fc = nn.Linear(self.num_features * self.global_pool.feat_mult(), num_classes)
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for n, m in self.named_modules():
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if isinstance(m, nn.Conv2d):
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nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
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elif isinstance(m, nn.BatchNorm2d):
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nn.init.constant_(m.weight, 1.)
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nn.init.constant_(m.bias, 0.)
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def get_classifier(self):
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return self.fc
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def reset_classifier(self, num_classes, global_pool='avg'):
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self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
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self.num_classes = num_classes
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del self.fc
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if num_classes:
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self.fc = nn.Linear(self.num_features * self.global_pool.feat_mult(), num_classes)
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else:
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self.fc = None
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def forward_features(self, x):
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x = self.stem(x)
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x = self.features([x])
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x = self.head(x[0])
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return x
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def forward(self, x):
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x = self.forward_features(x)
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x = self.global_pool(x).flatten(1)
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if self.drop_rate > 0.:
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x = F.dropout(x, p=self.drop_rate, training=self.training)
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x = self.fc(x)
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return x
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def _create_model(variant, pretrained, model_kwargs):
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cfg = {}
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if variant.startswith('selecsls42'):
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cfg['block'] = SelecSLSBlock
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# Define configuration of the network after the initial neck
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cfg['features'] = [
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# in_chs, skip_chs, mid_chs, out_chs, is_first, stride
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(32, 0, 64, 64, True, 2),
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(64, 64, 64, 128, False, 1),
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(128, 0, 144, 144, True, 2),
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(144, 144, 144, 288, False, 1),
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(288, 0, 304, 304, True, 2),
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(304, 304, 304, 480, False, 1),
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]
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# Head can be replaced with alternative configurations depending on the problem
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if variant == 'selecsls42b':
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cfg['head'] = [
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(480, 960, 3, 2),
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(960, 1024, 3, 1),
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(1024, 1280, 3, 2),
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(1280, 1024, 1, 1),
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]
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cfg['num_features'] = 1024
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else:
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cfg['head'] = [
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(480, 960, 3, 2),
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(960, 1024, 3, 1),
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(1024, 1024, 3, 2),
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(1024, 1280, 1, 1),
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]
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cfg['num_features'] = 1280
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elif variant.startswith('selecsls60'):
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cfg['block'] = SelecSLSBlock
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# Define configuration of the network after the initial neck
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cfg['features'] = [
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# in_chs, skip_chs, mid_chs, out_chs, is_first, stride
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(32, 0, 64, 64, True, 2),
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(64, 64, 64, 128, False, 1),
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(128, 0, 128, 128, True, 2),
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(128, 128, 128, 128, False, 1),
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(128, 128, 128, 288, False, 1),
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(288, 0, 288, 288, True, 2),
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(288, 288, 288, 288, False, 1),
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(288, 288, 288, 288, False, 1),
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(288, 288, 288, 416, False, 1),
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]
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# Head can be replaced with alternative configurations depending on the problem
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if variant == 'selecsls60b':
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cfg['head'] = [
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(416, 756, 3, 2),
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(756, 1024, 3, 1),
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(1024, 1280, 3, 2),
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(1280, 1024, 1, 1),
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]
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cfg['num_features'] = 1024
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else:
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cfg['head'] = [
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(416, 756, 3, 2),
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(756, 1024, 3, 1),
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(1024, 1024, 3, 2),
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(1024, 1280, 1, 1),
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]
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cfg['num_features'] = 1280
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elif variant == 'selecsls84':
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cfg['block'] = SelecSLSBlock
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# Define configuration of the network after the initial neck
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cfg['features'] = [
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# in_chs, skip_chs, mid_chs, out_chs, is_first, stride
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(32, 0, 64, 64, True, 2),
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(64, 64, 64, 144, False, 1),
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(144, 0, 144, 144, True, 2),
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(144, 144, 144, 144, False, 1),
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(144, 144, 144, 144, False, 1),
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(144, 144, 144, 144, False, 1),
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(144, 144, 144, 304, False, 1),
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(304, 0, 304, 304, True, 2),
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(304, 304, 304, 304, False, 1),
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(304, 304, 304, 304, False, 1),
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(304, 304, 304, 304, False, 1),
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(304, 304, 304, 304, False, 1),
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(304, 304, 304, 512, False, 1),
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]
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# Head can be replaced with alternative configurations depending on the problem
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cfg['head'] = [
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(512, 960, 3, 2),
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(960, 1024, 3, 1),
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(1024, 1024, 3, 2),
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(1024, 1280, 3, 1),
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]
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cfg['num_features'] = 1280
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else:
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raise ValueError('Invalid net configuration ' + variant + ' !!!')
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model = SelecSLS(cfg, **model_kwargs)
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model.default_cfg = default_cfgs[variant]
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if pretrained:
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load_pretrained(
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model,
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num_classes=model_kwargs.get('num_classes', 0),
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in_chans=model_kwargs.get('in_chans', 3),
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strict=True)
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return model
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@register_model
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def selecsls42(pretrained=False, **kwargs):
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"""Constructs a SelecSLS42 model.
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"""
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return _create_model('selecsls42', pretrained, kwargs)
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@register_model
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def selecsls42b(pretrained=False, **kwargs):
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"""Constructs a SelecSLS42_B model.
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"""
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return _create_model('selecsls42b', pretrained, kwargs)
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@register_model
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def selecsls60(pretrained=False, **kwargs):
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"""Constructs a SelecSLS60 model.
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"""
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return _create_model('selecsls60', pretrained, kwargs)
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@register_model
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def selecsls60b(pretrained=False, **kwargs):
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"""Constructs a SelecSLS60_B model.
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"""
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return _create_model('selecsls60b', pretrained, kwargs)
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@register_model
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def selecsls84(pretrained=False, **kwargs):
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"""Constructs a SelecSLS84 model.
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"""
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return _create_model('selecsls84', pretrained, kwargs)
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