"""Pytorch impl of Aligned Xception 41, 65, 71 This is a correct, from scratch impl of Aligned Xception (Deeplab) models compatible with TF weights at https://github.com/tensorflow/models/blob/master/research/deeplab/g3doc/model_zoo.md Hacked together by / Copyright 2020 Ross Wightman """ from collections import OrderedDict import torch.nn as nn import torch.nn.functional as F from timm.data import IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD from .helpers import build_model_with_cfg from .layers import ClassifierHead, ConvBnAct, create_conv2d from .layers.helpers import to_3tuple from .registry import register_model __all__ = ['XceptionAligned'] def _cfg(url='', **kwargs): return { 'url': url, 'num_classes': 1000, 'input_size': (3, 299, 299), 'pool_size': (10, 10), 'crop_pct': 0.903, 'interpolation': 'bicubic', 'mean': IMAGENET_INCEPTION_MEAN, 'std': IMAGENET_INCEPTION_STD, 'first_conv': 'stem.0.conv', 'classifier': 'head.fc', **kwargs } default_cfgs = dict( xception41=_cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_xception_41-e6439c97.pth'), xception65=_cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_xception_65-c9ae96e8.pth'), xception71=_cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_xception_71-8eec7df1.pth'), ) class SeparableConv2d(nn.Module): def __init__( self, inplanes, planes, kernel_size=3, stride=1, dilation=1, padding='', act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, norm_kwargs=None): super(SeparableConv2d, self).__init__() norm_kwargs = norm_kwargs if norm_kwargs is not None else {} self.kernel_size = kernel_size self.dilation = dilation # depthwise convolution self.conv_dw = create_conv2d( inplanes, inplanes, kernel_size, stride=stride, padding=padding, dilation=dilation, depthwise=True) self.bn_dw = norm_layer(inplanes, **norm_kwargs) if act_layer is not None: self.act_dw = act_layer(inplace=True) else: self.act_dw = None # pointwise convolution self.conv_pw = create_conv2d(inplanes, planes, kernel_size=1) self.bn_pw = norm_layer(planes, **norm_kwargs) if act_layer is not None: self.act_pw = act_layer(inplace=True) else: self.act_pw = None def forward(self, x): x = self.conv_dw(x) x = self.bn_dw(x) if self.act_dw is not None: x = self.act_dw(x) x = self.conv_pw(x) x = self.bn_pw(x) if self.act_pw is not None: x = self.act_pw(x) return x class XceptionModule(nn.Module): def __init__( self, in_chs, out_chs, stride=1, dilation=1, pad_type='', start_with_relu=True, no_skip=False, act_layer=nn.ReLU, norm_layer=None, norm_kwargs=None): super(XceptionModule, self).__init__() norm_kwargs = norm_kwargs if norm_kwargs is not None else {} out_chs = to_3tuple(out_chs) self.in_channels = in_chs self.out_channels = out_chs[-1] self.no_skip = no_skip if not no_skip and (self.out_channels != self.in_channels or stride != 1): self.shortcut = ConvBnAct( in_chs, self.out_channels, 1, stride=stride, norm_layer=norm_layer, norm_kwargs=norm_kwargs, act_layer=None) else: self.shortcut = None separable_act_layer = None if start_with_relu else act_layer self.stack = nn.Sequential() for i in range(3): if start_with_relu: self.stack.add_module(f'act{i + 1}', nn.ReLU(inplace=i > 0)) self.stack.add_module(f'conv{i + 1}', SeparableConv2d( in_chs, out_chs[i], 3, stride=stride if i == 2 else 1, dilation=dilation, padding=pad_type, act_layer=separable_act_layer, norm_layer=norm_layer, norm_kwargs=norm_kwargs)) in_chs = out_chs[i] def forward(self, x): skip = x x = self.stack(x) if self.shortcut is not None: skip = self.shortcut(skip) if not self.no_skip: x = x + skip return x class XceptionAligned(nn.Module): """Modified Aligned Xception """ def __init__(self, block_cfg, num_classes=1000, in_chans=3, output_stride=32, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, norm_kwargs=None, drop_rate=0., global_pool='avg'): super(XceptionAligned, self).__init__() self.num_classes = num_classes self.drop_rate = drop_rate assert output_stride in (8, 16, 32) norm_kwargs = norm_kwargs if norm_kwargs is not None else {} layer_args = dict(act_layer=act_layer, norm_layer=norm_layer, norm_kwargs=norm_kwargs) self.stem = nn.Sequential(*[ ConvBnAct(in_chans, 32, kernel_size=3, stride=2, **layer_args), ConvBnAct(32, 64, kernel_size=3, stride=1, **layer_args) ]) curr_dilation = 1 curr_stride = 2 self.feature_info = [] self.blocks = nn.Sequential() for i, b in enumerate(block_cfg): b['dilation'] = curr_dilation if b['stride'] > 1: self.feature_info += [dict( num_chs=to_3tuple(b['out_chs'])[-2], reduction=curr_stride, module=f'blocks.{i}.stack.act3')] next_stride = curr_stride * b['stride'] if next_stride > output_stride: curr_dilation *= b['stride'] b['stride'] = 1 else: curr_stride = next_stride self.blocks.add_module(str(i), XceptionModule(**b, **layer_args)) self.num_features = self.blocks[-1].out_channels self.feature_info += [dict( num_chs=self.num_features, reduction=curr_stride, module='blocks.' + str(len(self.blocks) - 1))] self.head = ClassifierHead( in_chs=self.num_features, num_classes=num_classes, pool_type=global_pool, drop_rate=drop_rate) 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.blocks(x) return x def forward(self, x): x = self.forward_features(x) x = self.head(x) return x def _xception(variant, pretrained=False, **kwargs): return build_model_with_cfg( XceptionAligned, variant, pretrained, default_cfg=default_cfgs[variant], feature_cfg=dict(flatten_sequential=True, feature_cls='hook'), **kwargs) @register_model def xception41(pretrained=False, **kwargs): """ Modified Aligned Xception-41 """ block_cfg = [ # entry flow dict(in_chs=64, out_chs=128, stride=2), dict(in_chs=128, out_chs=256, stride=2), dict(in_chs=256, out_chs=728, stride=2), # middle flow *([dict(in_chs=728, out_chs=728, stride=1)] * 8), # exit flow dict(in_chs=728, out_chs=(728, 1024, 1024), stride=2), dict(in_chs=1024, out_chs=(1536, 1536, 2048), stride=1, no_skip=True, start_with_relu=False), ] model_args = dict(block_cfg=block_cfg, norm_kwargs=dict(eps=.001, momentum=.1), **kwargs) return _xception('xception41', pretrained=pretrained, **model_args) @register_model def xception65(pretrained=False, **kwargs): """ Modified Aligned Xception-65 """ block_cfg = [ # entry flow dict(in_chs=64, out_chs=128, stride=2), dict(in_chs=128, out_chs=256, stride=2), dict(in_chs=256, out_chs=728, stride=2), # middle flow *([dict(in_chs=728, out_chs=728, stride=1)] * 16), # exit flow dict(in_chs=728, out_chs=(728, 1024, 1024), stride=2), dict(in_chs=1024, out_chs=(1536, 1536, 2048), stride=1, no_skip=True, start_with_relu=False), ] model_args = dict(block_cfg=block_cfg, norm_kwargs=dict(eps=.001, momentum=.1), **kwargs) return _xception('xception65', pretrained=pretrained, **model_args) @register_model def xception71(pretrained=False, **kwargs): """ Modified Aligned Xception-71 """ block_cfg = [ # entry flow dict(in_chs=64, out_chs=128, stride=2), dict(in_chs=128, out_chs=256, stride=1), dict(in_chs=256, out_chs=256, stride=2), dict(in_chs=256, out_chs=728, stride=1), dict(in_chs=728, out_chs=728, stride=2), # middle flow *([dict(in_chs=728, out_chs=728, stride=1)] * 16), # exit flow dict(in_chs=728, out_chs=(728, 1024, 1024), stride=2), dict(in_chs=1024, out_chs=(1536, 1536, 2048), stride=1, no_skip=True, start_with_relu=False), ] model_args = dict(block_cfg=block_cfg, norm_kwargs=dict(eps=.001, momentum=.1), **kwargs) return _xception('xception71', pretrained=pretrained, **model_args)