More model feature extraction support, start to deprecate senet.py, dilations added to regnet, add proper aligned xception
Ross Wightman
5 years ago
parent
7729f40dca
commit
a66df5fb91
@ -1,30 +1,31 @@
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from .inception_v4 import *
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from .inception_resnet_v2 import *
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from .densenet import *
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from .densenet import *
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from .resnet import *
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from .dla import *
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from .dpn import *
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from .dpn import *
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from .senet import *
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from .xception import *
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from .nasnet import *
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from .pnasnet import *
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from .selecsls import *
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from .efficientnet import *
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from .efficientnet import *
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from .mobilenetv3 import *
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from .inception_v3 import *
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from .gluon_resnet import *
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from .gluon_resnet import *
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from .gluon_xception import *
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from .gluon_xception import *
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from .res2net import *
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from .dla import *
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from .hrnet import *
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from .hrnet import *
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from .inception_resnet_v2 import *
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from .inception_v3 import *
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from .inception_v4 import *
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from .mobilenetv3 import *
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from .nasnet import *
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from .pnasnet import *
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from .regnet import *
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from .res2net import *
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from .resnest import *
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from .resnet import *
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from .selecsls import *
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from .senet import *
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from .sknet import *
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from .sknet import *
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from .tresnet import *
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from .tresnet import *
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from .resnest import *
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from .regnet import *
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from .vovnet import *
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from .vovnet import *
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from .xception import *
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from .xception_aligned import *
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from .registry import *
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from .factory import create_model
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from .factory import create_model
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from .helpers import load_checkpoint, resume_checkpoint
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from .helpers import load_checkpoint, resume_checkpoint
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from .layers import TestTimePoolHead, apply_test_time_pool
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from .layers import TestTimePoolHead, apply_test_time_pool
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from .layers import convert_splitbn_model
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from .layers import convert_splitbn_model
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from .layers import is_scriptable, is_exportable, set_scriptable, set_exportable, is_no_jit, set_no_jit
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from .layers import is_scriptable, is_exportable, set_scriptable, set_exportable, is_no_jit, set_no_jit
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from .registry import *
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@ -0,0 +1,278 @@
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"""Pytorch impl of Aligned Xception
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This is a correct impl of Aligned Xception (Deeplab) models compatible with TF definition.
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Hacked together by Ross Wightman
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"""
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from collections import OrderedDict
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import torch.nn as nn
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import torch.nn.functional as F
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from timm.data import IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
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from .features import FeatureNet
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from .helpers import load_pretrained
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from .layers import SelectAdaptivePool2d, ConvBnAct, create_conv2d
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from .registry import register_model
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__all__ = ['XceptionAligned']
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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, 299, 299), 'pool_size': (10, 10),
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'crop_pct': 0.903, 'interpolation': 'bicubic',
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'mean': IMAGENET_INCEPTION_MEAN, 'std': IMAGENET_INCEPTION_STD,
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'first_conv': 'stem.0', 'classifier': 'head.fc',
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**kwargs
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}
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default_cfgs = dict(
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xception41=_cfg(url=''),
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xception65=_cfg(url=''),
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xception71=_cfg(url=''),
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)
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class SeparableConv2d(nn.Module):
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def __init__(
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self, inplanes, planes, kernel_size=3, stride=1, dilation=1, padding='',
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act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, norm_kwargs=None):
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super(SeparableConv2d, self).__init__()
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norm_kwargs = norm_kwargs if norm_kwargs is not None else {}
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self.kernel_size = kernel_size
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self.dilation = dilation
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# depthwise convolution
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self.conv_dw = create_conv2d(
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inplanes, inplanes, kernel_size, stride=stride,
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padding=padding, dilation=dilation, depthwise=True)
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self.bn_dw = norm_layer(inplanes, **norm_kwargs)
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if act_layer is not None:
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self.act_dw = act_layer(inplace=True)
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else:
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self.act_dw = None
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# pointwise convolution
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self.conv_pw = create_conv2d(inplanes, planes, kernel_size=1)
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self.bn_pw = norm_layer(planes, **norm_kwargs)
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if act_layer is not None:
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self.act_pw = act_layer(inplace=True)
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else:
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self.act_pw = None
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def forward(self, x):
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x = self.conv_dw(x)
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x = self.bn_dw(x)
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if self.act_dw is not None:
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x = self.act_dw(x)
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x = self.conv_pw(x)
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x = self.bn_pw(x)
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if self.act_pw is not None:
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x = self.act_pw(x)
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return x
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class XceptionModule(nn.Module):
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def __init__(
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self, in_chs, out_chs, stride=1, dilation=1, pad_type='',
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start_with_relu=True, no_skip=False, act_layer=nn.ReLU, norm_layer=None, norm_kwargs=None):
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super(XceptionModule, self).__init__()
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norm_kwargs = norm_kwargs if norm_kwargs is not None else {}
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if isinstance(out_chs, (list, tuple)):
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assert len(out_chs) == 3
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else:
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out_chs = (out_chs,) * 3
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self.in_channels = in_chs
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self.out_channels = out_chs[-1]
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self.no_skip = no_skip
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if not no_skip and (self.out_channels != self.in_channels or stride != 1):
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self.shortcut = ConvBnAct(
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in_chs, self.out_channels, 1, stride=stride,
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norm_layer=norm_layer, norm_kwargs=norm_kwargs, act_layer=None)
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else:
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self.shortcut = None
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separable_act_layer = None if start_with_relu else act_layer
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self.stack = nn.Sequential()
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for i in range(3):
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if start_with_relu:
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self.stack.add_module(f'act{i + 1}', nn.ReLU(inplace=i > 0))
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self.stack.add_module(f'conv{i + 1}', SeparableConv2d(
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in_chs, out_chs[i], 3, stride=stride if i == 2 else 1, dilation=dilation, padding=pad_type,
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act_layer=separable_act_layer, norm_layer=norm_layer, norm_kwargs=norm_kwargs))
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in_chs = out_chs[i]
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def forward(self, x):
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skip = x
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x = self.stack(x)
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if self.shortcut is not None:
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skip = self.shortcut(skip)
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if not self.no_skip:
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x = x + skip
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return x
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class ClassifierHead(nn.Module):
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"""Head."""
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def __init__(self, in_chs, num_classes, pool_type='avg', drop_rate=0.):
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super(ClassifierHead, self).__init__()
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self.drop_rate = drop_rate
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self.global_pool = SelectAdaptivePool2d(pool_type=pool_type)
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if num_classes > 0:
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self.fc = nn.Linear(in_chs, num_classes, bias=True)
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else:
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self.fc = nn.Identity()
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def forward(self, x):
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x = self.global_pool(x).flatten(1)
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if self.drop_rate:
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x = F.dropout(x, p=float(self.drop_rate), training=self.training)
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x = self.fc(x)
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return x
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class XceptionAligned(nn.Module):
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"""Modified Aligned Xception
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"""
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def __init__(self, block_cfg, num_classes=1000, in_chans=3, output_stride=32,
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act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, norm_kwargs=None, drop_rate=0., global_pool='avg'):
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super(XceptionAligned, self).__init__()
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self.num_classes = num_classes
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self.drop_rate = drop_rate
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assert output_stride in (8, 16, 32)
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norm_kwargs = norm_kwargs if norm_kwargs is not None else {}
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xtra_args = dict(act_layer=act_layer, norm_layer=norm_layer, norm_kwargs=norm_kwargs)
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self.stem = nn.Sequential(*[
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ConvBnAct(in_chans, 32, kernel_size=3, stride=2, **xtra_args),
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ConvBnAct(32, 64, kernel_size=3, stride=1, **xtra_args)
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])
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curr_dilation = 1
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curr_stride = 2
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self.feature_info = [dict(num_chs=64, reduction=curr_stride, module='stem.1')]
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self.blocks = nn.Sequential()
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for i, b in enumerate(block_cfg):
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feature_extract = False
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b['dilation'] = curr_dilation
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if b['stride'] > 1:
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feature_extract = True
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next_stride = curr_stride * b['stride']
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if next_stride > output_stride:
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curr_dilation *= b['stride']
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b['stride'] = 1
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else:
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curr_stride = next_stride
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self.blocks.add_module(str(i), XceptionModule(**b, **xtra_args))
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self.num_features = self.blocks[-1].out_channels
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if feature_extract:
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self.feature_info += [dict(
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num_chs=self.num_features, reduction=curr_stride, module=f'blocks.{i}.stack.act2')]
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self.feature_info += [dict(
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num_chs=self.num_features, reduction=curr_stride, module='blocks.' + str(len(self.blocks) - 1))]
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self.head = ClassifierHead(
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in_chs=self.num_features, num_classes=num_classes, pool_type=global_pool, drop_rate=drop_rate)
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def get_classifier(self):
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return self.head.fc
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def reset_classifier(self, num_classes, global_pool='avg'):
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self.head = ClassifierHead(self.num_features, num_classes, pool_type=global_pool, drop_rate=self.drop_rate)
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def forward_features(self, x):
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x = self.stem(x)
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x = self.blocks(x)
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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.head(x)
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return x
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def _xception(variant, pretrained=False, **kwargs):
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features = False
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out_indices = None
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if kwargs.pop('features_only', False):
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features = True
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kwargs.pop('num_classes', 0)
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out_indices = kwargs.pop('out_indices', (0, 1, 2, 3, 4))
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model = XceptionAligned(**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=kwargs.get('num_classes', 0),
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in_chans=kwargs.get('in_chans', 3),
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strict=not features)
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if features:
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model = FeatureNet(model, out_indices)
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return model
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@register_model
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def xception41(pretrained=False, **kwargs):
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""" Modified Aligned Xception-41
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"""
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block_cfg = [
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# entry flow
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dict(in_chs=64, out_chs=128, stride=2),
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dict(in_chs=128, out_chs=256, stride=2),
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dict(in_chs=256, out_chs=728, stride=2),
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# middle flow
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*([dict(in_chs=728, out_chs=728, stride=1)] * 8),
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# exit flow
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dict(in_chs=728, out_chs=(728, 1024, 1024), stride=2),
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dict(in_chs=1024, out_chs=(1536, 1536, 2048), stride=1, no_skip=True, start_with_relu=False),
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]
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model_args = dict(block_cfg=block_cfg, norm_kwargs=dict(eps=.001, momentum=.1), **kwargs)
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return _xception('xception41', pretrained=pretrained, **model_args)
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@register_model
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def xception65(pretrained=False, **kwargs):
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""" Modified Aligned Xception-65
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"""
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block_cfg = [
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# entry flow
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dict(in_chs=64, out_chs=128, stride=2),
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dict(in_chs=128, out_chs=256, stride=2),
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dict(in_chs=256, out_chs=728, stride=2),
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# middle flow
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*([dict(in_chs=728, out_chs=728, stride=1)] * 16),
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# exit flow
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dict(in_chs=728, out_chs=(728, 1024, 1024), stride=2),
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dict(in_chs=1024, out_chs=(1536, 1536, 2048), stride=1, no_skip=True, start_with_relu=False),
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]
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model_args = dict(block_cfg=block_cfg, norm_kwargs=dict(eps=.001, momentum=.1), **kwargs)
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return _xception('xception65', pretrained=pretrained, **model_args)
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@register_model
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def xception71(pretrained=False, **kwargs):
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""" Modified Aligned Xception-71
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"""
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block_cfg = [
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# entry flow
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dict(in_chs=64, out_chs=128, stride=2),
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dict(in_chs=128, out_chs=256, stride=1),
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dict(in_chs=256, out_chs=256, stride=2),
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dict(in_chs=256, out_chs=728, stride=1),
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dict(in_chs=728, out_chs=728, stride=2),
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# middle flow
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*([dict(in_chs=728, out_chs=728, stride=1)] * 16),
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# exit flow
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dict(in_chs=728, out_chs=(728, 1024, 1024), stride=2),
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dict(in_chs=1024, out_chs=(1536, 1536, 2048), stride=1, no_skip=True, start_with_relu=False),
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]
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model_args = dict(block_cfg=block_cfg, norm_kwargs=dict(eps=.001, momentum=.1), **kwargs)
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return _xception('xception71', pretrained=pretrained, **model_args)
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