pytorch-image-models/timm/models/xception.py

"""
Ported to pytorch thanks to [tstandley](https://github.com/tstandley/Xception-PyTorch)

@author: tstandley
Adapted by cadene

Creates an Xception Model as defined in:

Francois Chollet
Xception: Deep Learning with Depthwise Separable Convolutions
https://arxiv.org/pdf/1610.02357.pdf

This weights ported from the Keras implementation. Achieves the following performance on the validation set:

Loss:0.9173 Prec@1:78.892 Prec@5:94.292

REMEMBER to set your image size to 3x299x299 for both test and validation

normalize = transforms.Normalize(mean=[0.5, 0.5, 0.5],
                                  std=[0.5, 0.5, 0.5])

The resize parameter of the validation transform should be 333, and make sure to center crop at 299x299
"""
import torch.jit
import torch.nn as nn
import torch.nn.functional as F

from timm.layers import create_classifier
from ._builder import build_model_with_cfg
from ._registry import register_model

__all__ = ['Xception']

default_cfgs = {
    'xception': {
        'url': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-cadene/xception-43020ad28.pth',
        'input_size': (3, 299, 299),
        'pool_size': (10, 10),
        'crop_pct': 0.8975,
        'interpolation': 'bicubic',
        'mean': (0.5, 0.5, 0.5),
        'std': (0.5, 0.5, 0.5),
        'num_classes': 1000,
        'first_conv': 'conv1',
        'classifier': 'fc'
        # The resize parameter of the validation transform should be 333, and make sure to center crop at 299x299
    }
}


class SeparableConv2d(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, padding=0, dilation=1):
        super(SeparableConv2d, self).__init__()

        self.conv1 = nn.Conv2d(
            in_channels, in_channels, kernel_size, stride, padding, dilation, groups=in_channels, bias=False)
        self.pointwise = nn.Conv2d(in_channels, out_channels, 1, 1, 0, 1, 1, bias=False)

    def forward(self, x):
        x = self.conv1(x)
        x = self.pointwise(x)
        return x


class Block(nn.Module):
    def __init__(self, in_channels, out_channels, reps, strides=1, start_with_relu=True, grow_first=True):
        super(Block, self).__init__()

        if out_channels != in_channels or strides != 1:
            self.skip = nn.Conv2d(in_channels, out_channels, 1, stride=strides, bias=False)
            self.skipbn = nn.BatchNorm2d(out_channels)
        else:
            self.skip = None

        rep = []
        for i in range(reps):
            if grow_first:
                inc = in_channels if i == 0 else out_channels
                outc = out_channels
            else:
                inc = in_channels
                outc = in_channels if i < (reps - 1) else out_channels
            rep.append(nn.ReLU(inplace=True))
            rep.append(SeparableConv2d(inc, outc, 3, stride=1, padding=1))
            rep.append(nn.BatchNorm2d(outc))

        if not start_with_relu:
            rep = rep[1:]
        else:
            rep[0] = nn.ReLU(inplace=False)

        if strides != 1:
            rep.append(nn.MaxPool2d(3, strides, 1))
        self.rep = nn.Sequential(*rep)

    def forward(self, inp):
        x = self.rep(inp)

        if self.skip is not None:
            skip = self.skip(inp)
            skip = self.skipbn(skip)
        else:
            skip = inp

        x += skip
        return x


class Xception(nn.Module):
    """
    Xception optimized for the ImageNet dataset, as specified in
    https://arxiv.org/pdf/1610.02357.pdf
    """

    def __init__(self, num_classes=1000, in_chans=3, drop_rate=0., global_pool='avg'):
        """ Constructor
        Args:
            num_classes: number of classes
        """
        super(Xception, self).__init__()
        self.drop_rate = drop_rate
        self.global_pool = global_pool
        self.num_classes = num_classes
        self.num_features = 2048

        self.conv1 = nn.Conv2d(in_chans, 32, 3, 2, 0, bias=False)
        self.bn1 = nn.BatchNorm2d(32)
        self.act1 = nn.ReLU(inplace=True)

        self.conv2 = nn.Conv2d(32, 64, 3, bias=False)
        self.bn2 = nn.BatchNorm2d(64)
        self.act2 = nn.ReLU(inplace=True)

        self.block1 = Block(64, 128, 2, 2, start_with_relu=False)
        self.block2 = Block(128, 256, 2, 2)
        self.block3 = Block(256, 728, 2, 2)

        self.block4 = Block(728, 728, 3, 1)
        self.block5 = Block(728, 728, 3, 1)
        self.block6 = Block(728, 728, 3, 1)
        self.block7 = Block(728, 728, 3, 1)

        self.block8 = Block(728, 728, 3, 1)
        self.block9 = Block(728, 728, 3, 1)
        self.block10 = Block(728, 728, 3, 1)
        self.block11 = Block(728, 728, 3, 1)

        self.block12 = Block(728, 1024, 2, 2, grow_first=False)

        self.conv3 = SeparableConv2d(1024, 1536, 3, 1, 1)
        self.bn3 = nn.BatchNorm2d(1536)
        self.act3 = nn.ReLU(inplace=True)

        self.conv4 = SeparableConv2d(1536, self.num_features, 3, 1, 1)
        self.bn4 = nn.BatchNorm2d(self.num_features)
        self.act4 = nn.ReLU(inplace=True)
        self.feature_info = [
            dict(num_chs=64, reduction=2, module='act2'),
            dict(num_chs=128, reduction=4, module='block2.rep.0'),
            dict(num_chs=256, reduction=8, module='block3.rep.0'),
            dict(num_chs=728, reduction=16, module='block12.rep.0'),
            dict(num_chs=2048, reduction=32, module='act4'),
        ]

        self.global_pool, self.fc = create_classifier(self.num_features, self.num_classes, pool_type=global_pool)

        # #------- init weights --------
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    @torch.jit.ignore
    def group_matcher(self, coarse=False):
        return dict(
            stem=r'^conv[12]|bn[12]',
            blocks=[
                (r'^block(\d+)', None),
                (r'^conv[34]|bn[34]', (99,)),
            ],
        )

    @torch.jit.ignore
    def set_grad_checkpointing(self, enable=True):
        assert not enable, "gradient checkpointing not supported"

    @torch.jit.ignore
    def get_classifier(self):
        return self.fc

    def reset_classifier(self, num_classes, global_pool='avg'):
        self.num_classes = num_classes
        self.global_pool, self.fc = create_classifier(self.num_features, self.num_classes, pool_type=global_pool)

    def forward_features(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.act1(x)

        x = self.conv2(x)
        x = self.bn2(x)
        x = self.act2(x)

        x = self.block1(x)
        x = self.block2(x)
        x = self.block3(x)
        x = self.block4(x)
        x = self.block5(x)
        x = self.block6(x)
        x = self.block7(x)
        x = self.block8(x)
        x = self.block9(x)
        x = self.block10(x)
        x = self.block11(x)
        x = self.block12(x)

        x = self.conv3(x)
        x = self.bn3(x)
        x = self.act3(x)

        x = self.conv4(x)
        x = self.bn4(x)
        x = self.act4(x)
        return x

    def forward_head(self, x, pre_logits: bool = False):
        x = self.global_pool(x)
        if self.drop_rate:
            F.dropout(x, self.drop_rate, training=self.training)
        return x if pre_logits else self.fc(x)

    def forward(self, x):
        x = self.forward_features(x)
        x = self.forward_head(x)
        return x


def _xception(variant, pretrained=False, **kwargs):
    return build_model_with_cfg(
        Xception, variant, pretrained,
        feature_cfg=dict(feature_cls='hook'),
        **kwargs)


@register_model
def xception(pretrained=False, **kwargs):
    return _xception('xception', pretrained=pretrained, **kwargs)