Source code for torchreid.models.inceptionv4

from __future__ import division, absolute_import
import torch
import torch.nn as nn
import torch.utils.model_zoo as model_zoo

__all__ = ['inceptionv4']
"""
Code imported from https://github.com/Cadene/pretrained-models.pytorch
"""

pretrained_settings = {
    'inceptionv4': {
        'imagenet': {
            'url':
            'http://data.lip6.fr/cadene/pretrainedmodels/inceptionv4-8e4777a0.pth',
            'input_space': 'RGB',
            'input_size': [3, 299, 299],
            'input_range': [0, 1],
            'mean': [0.5, 0.5, 0.5],
            'std': [0.5, 0.5, 0.5],
            'num_classes': 1000
        },
        'imagenet+background': {
            'url':
            'http://data.lip6.fr/cadene/pretrainedmodels/inceptionv4-8e4777a0.pth',
            'input_space': 'RGB',
            'input_size': [3, 299, 299],
            'input_range': [0, 1],
            'mean': [0.5, 0.5, 0.5],
            'std': [0.5, 0.5, 0.5],
            'num_classes': 1001
        }
    }
}


class BasicConv2d(nn.Module):

    def __init__(self, in_planes, out_planes, kernel_size, stride, padding=0):
        super(BasicConv2d, self).__init__()
        self.conv = nn.Conv2d(
            in_planes,
            out_planes,
            kernel_size=kernel_size,
            stride=stride,
            padding=padding,
            bias=False
        ) # verify bias false
        self.bn = nn.BatchNorm2d(
            out_planes,
            eps=0.001, # value found in tensorflow
            momentum=0.1, # default pytorch value
            affine=True
        )
        self.relu = nn.ReLU(inplace=True)

    def forward(self, x):
        x = self.conv(x)
        x = self.bn(x)
        x = self.relu(x)
        return x


class Mixed_3a(nn.Module):

    def __init__(self):
        super(Mixed_3a, self).__init__()
        self.maxpool = nn.MaxPool2d(3, stride=2)
        self.conv = BasicConv2d(64, 96, kernel_size=3, stride=2)

    def forward(self, x):
        x0 = self.maxpool(x)
        x1 = self.conv(x)
        out = torch.cat((x0, x1), 1)
        return out


class Mixed_4a(nn.Module):

    def __init__(self):
        super(Mixed_4a, self).__init__()

        self.branch0 = nn.Sequential(
            BasicConv2d(160, 64, kernel_size=1, stride=1),
            BasicConv2d(64, 96, kernel_size=3, stride=1)
        )

        self.branch1 = nn.Sequential(
            BasicConv2d(160, 64, kernel_size=1, stride=1),
            BasicConv2d(64, 64, kernel_size=(1, 7), stride=1, padding=(0, 3)),
            BasicConv2d(64, 64, kernel_size=(7, 1), stride=1, padding=(3, 0)),
            BasicConv2d(64, 96, kernel_size=(3, 3), stride=1)
        )

    def forward(self, x):
        x0 = self.branch0(x)
        x1 = self.branch1(x)
        out = torch.cat((x0, x1), 1)
        return out


class Mixed_5a(nn.Module):

    def __init__(self):
        super(Mixed_5a, self).__init__()
        self.conv = BasicConv2d(192, 192, kernel_size=3, stride=2)
        self.maxpool = nn.MaxPool2d(3, stride=2)

    def forward(self, x):
        x0 = self.conv(x)
        x1 = self.maxpool(x)
        out = torch.cat((x0, x1), 1)
        return out


class Inception_A(nn.Module):

    def __init__(self):
        super(Inception_A, self).__init__()
        self.branch0 = BasicConv2d(384, 96, kernel_size=1, stride=1)

        self.branch1 = nn.Sequential(
            BasicConv2d(384, 64, kernel_size=1, stride=1),
            BasicConv2d(64, 96, kernel_size=3, stride=1, padding=1)
        )

        self.branch2 = nn.Sequential(
            BasicConv2d(384, 64, kernel_size=1, stride=1),
            BasicConv2d(64, 96, kernel_size=3, stride=1, padding=1),
            BasicConv2d(96, 96, kernel_size=3, stride=1, padding=1)
        )

        self.branch3 = nn.Sequential(
            nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False),
            BasicConv2d(384, 96, kernel_size=1, stride=1)
        )

    def forward(self, x):
        x0 = self.branch0(x)
        x1 = self.branch1(x)
        x2 = self.branch2(x)
        x3 = self.branch3(x)
        out = torch.cat((x0, x1, x2, x3), 1)
        return out


class Reduction_A(nn.Module):

    def __init__(self):
        super(Reduction_A, self).__init__()
        self.branch0 = BasicConv2d(384, 384, kernel_size=3, stride=2)

        self.branch1 = nn.Sequential(
            BasicConv2d(384, 192, kernel_size=1, stride=1),
            BasicConv2d(192, 224, kernel_size=3, stride=1, padding=1),
            BasicConv2d(224, 256, kernel_size=3, stride=2)
        )

        self.branch2 = nn.MaxPool2d(3, stride=2)

    def forward(self, x):
        x0 = self.branch0(x)
        x1 = self.branch1(x)
        x2 = self.branch2(x)
        out = torch.cat((x0, x1, x2), 1)
        return out


class Inception_B(nn.Module):

    def __init__(self):
        super(Inception_B, self).__init__()
        self.branch0 = BasicConv2d(1024, 384, kernel_size=1, stride=1)

        self.branch1 = nn.Sequential(
            BasicConv2d(1024, 192, kernel_size=1, stride=1),
            BasicConv2d(
                192, 224, kernel_size=(1, 7), stride=1, padding=(0, 3)
            ),
            BasicConv2d(
                224, 256, kernel_size=(7, 1), stride=1, padding=(3, 0)
            )
        )

        self.branch2 = nn.Sequential(
            BasicConv2d(1024, 192, kernel_size=1, stride=1),
            BasicConv2d(
                192, 192, kernel_size=(7, 1), stride=1, padding=(3, 0)
            ),
            BasicConv2d(
                192, 224, kernel_size=(1, 7), stride=1, padding=(0, 3)
            ),
            BasicConv2d(
                224, 224, kernel_size=(7, 1), stride=1, padding=(3, 0)
            ),
            BasicConv2d(
                224, 256, kernel_size=(1, 7), stride=1, padding=(0, 3)
            )
        )

        self.branch3 = nn.Sequential(
            nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False),
            BasicConv2d(1024, 128, kernel_size=1, stride=1)
        )

    def forward(self, x):
        x0 = self.branch0(x)
        x1 = self.branch1(x)
        x2 = self.branch2(x)
        x3 = self.branch3(x)
        out = torch.cat((x0, x1, x2, x3), 1)
        return out


class Reduction_B(nn.Module):

    def __init__(self):
        super(Reduction_B, self).__init__()

        self.branch0 = nn.Sequential(
            BasicConv2d(1024, 192, kernel_size=1, stride=1),
            BasicConv2d(192, 192, kernel_size=3, stride=2)
        )

        self.branch1 = nn.Sequential(
            BasicConv2d(1024, 256, kernel_size=1, stride=1),
            BasicConv2d(
                256, 256, kernel_size=(1, 7), stride=1, padding=(0, 3)
            ),
            BasicConv2d(
                256, 320, kernel_size=(7, 1), stride=1, padding=(3, 0)
            ), BasicConv2d(320, 320, kernel_size=3, stride=2)
        )

        self.branch2 = nn.MaxPool2d(3, stride=2)

    def forward(self, x):
        x0 = self.branch0(x)
        x1 = self.branch1(x)
        x2 = self.branch2(x)
        out = torch.cat((x0, x1, x2), 1)
        return out


class Inception_C(nn.Module):

    def __init__(self):
        super(Inception_C, self).__init__()

        self.branch0 = BasicConv2d(1536, 256, kernel_size=1, stride=1)

        self.branch1_0 = BasicConv2d(1536, 384, kernel_size=1, stride=1)
        self.branch1_1a = BasicConv2d(
            384, 256, kernel_size=(1, 3), stride=1, padding=(0, 1)
        )
        self.branch1_1b = BasicConv2d(
            384, 256, kernel_size=(3, 1), stride=1, padding=(1, 0)
        )

        self.branch2_0 = BasicConv2d(1536, 384, kernel_size=1, stride=1)
        self.branch2_1 = BasicConv2d(
            384, 448, kernel_size=(3, 1), stride=1, padding=(1, 0)
        )
        self.branch2_2 = BasicConv2d(
            448, 512, kernel_size=(1, 3), stride=1, padding=(0, 1)
        )
        self.branch2_3a = BasicConv2d(
            512, 256, kernel_size=(1, 3), stride=1, padding=(0, 1)
        )
        self.branch2_3b = BasicConv2d(
            512, 256, kernel_size=(3, 1), stride=1, padding=(1, 0)
        )

        self.branch3 = nn.Sequential(
            nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False),
            BasicConv2d(1536, 256, kernel_size=1, stride=1)
        )

    def forward(self, x):
        x0 = self.branch0(x)

        x1_0 = self.branch1_0(x)
        x1_1a = self.branch1_1a(x1_0)
        x1_1b = self.branch1_1b(x1_0)
        x1 = torch.cat((x1_1a, x1_1b), 1)

        x2_0 = self.branch2_0(x)
        x2_1 = self.branch2_1(x2_0)
        x2_2 = self.branch2_2(x2_1)
        x2_3a = self.branch2_3a(x2_2)
        x2_3b = self.branch2_3b(x2_2)
        x2 = torch.cat((x2_3a, x2_3b), 1)

        x3 = self.branch3(x)

        out = torch.cat((x0, x1, x2, x3), 1)
        return out


[docs]class InceptionV4(nn.Module): """Inception-v4. Reference: Szegedy et al. Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning. AAAI 2017. Public keys: - ``inceptionv4``: InceptionV4. """ def __init__(self, num_classes, loss, **kwargs): super(InceptionV4, self).__init__() self.loss = loss self.features = nn.Sequential( BasicConv2d(3, 32, kernel_size=3, stride=2), BasicConv2d(32, 32, kernel_size=3, stride=1), BasicConv2d(32, 64, kernel_size=3, stride=1, padding=1), Mixed_3a(), Mixed_4a(), Mixed_5a(), Inception_A(), Inception_A(), Inception_A(), Inception_A(), Reduction_A(), # Mixed_6a Inception_B(), Inception_B(), Inception_B(), Inception_B(), Inception_B(), Inception_B(), Inception_B(), Reduction_B(), # Mixed_7a Inception_C(), Inception_C(), Inception_C() ) self.global_avgpool = nn.AdaptiveAvgPool2d(1) self.classifier = nn.Linear(1536, num_classes) def forward(self, x): f = self.features(x) v = self.global_avgpool(f) v = v.view(v.size(0), -1) if not self.training: return v y = self.classifier(v) if self.loss == 'softmax': return y elif self.loss == 'triplet': return y, v else: raise KeyError('Unsupported loss: {}'.format(self.loss))
def init_pretrained_weights(model, model_url): """Initializes model with pretrained weights. Layers that don't match with pretrained layers in name or size are kept unchanged. """ pretrain_dict = model_zoo.load_url(model_url) model_dict = model.state_dict() pretrain_dict = { k: v for k, v in pretrain_dict.items() if k in model_dict and model_dict[k].size() == v.size() } model_dict.update(pretrain_dict) model.load_state_dict(model_dict) def inceptionv4(num_classes, loss='softmax', pretrained=True, **kwargs): model = InceptionV4(num_classes, loss, **kwargs) if pretrained: model_url = pretrained_settings['inceptionv4']['imagenet']['url'] init_pretrained_weights(model, model_url) return model