<filename>model.py
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import models
import utils
import math
from torch.autograd import Variable
from typing import List

class FashionSimpleNet(nn.Module):

    """ Simple network"""

    def __init__(self):
        super().__init__()
        self.features = nn.Sequential(
            nn.Conv2d(1,32, kernel_size=3, padding=1), # 28
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2), # 14

            nn.Conv2d(32, 64, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2) # 7
        )
        self.classifier = nn.Sequential(
            nn.Dropout(),
            nn.Linear(64 * 7 * 7, 128),
            nn.ReLU(inplace=True),
            nn.Linear(128, 10)
        )

    def forward(self, x):
        x = self.features(x)
        x = x.view(x.size(0), 64 * 7 * 7)
        x = self.classifier(x)
        return x


class FashionComplexNet(nn.Module):

    """  Complex network"""

    def __init__(self):
        super().__init__()
        self.stem = nn.Sequential(
            nn.Conv2d(1,32, kernel_size=3, padding=1), # 28
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2), # 14
        )

        self.mid = nn.ModuleList()
        for i in range(10):
            self.mid.append(
                nn.Sequential(
                    nn.Conv2d(32,32, kernel_size=3, padding=1), # 14
                    nn.ReLU(inplace=True),
                )
            )

        self.tail = nn.Sequential(
            nn.Conv2d(32, 64, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2) # 7
        )
        self.classifier = nn.Sequential(
            nn.Dropout(),
            nn.Linear(64 * 7 * 7, 128),
            nn.ReLU(inplace=True),
            nn.Linear(128, 10)
        )

    def forward(self, x):
        x = self.stem(x)
        for layer in self.mid:
            x = layer(x)
        x = self.tail(x)
        x = x.view(x.size(0), 64 * 7 * 7)
        x = self.classifier(x)
        return x

    def forward_teach(self, x):
        feas = []
        feas.append(x)
        x = self.stem(x)
        feas.append(x)
        for layer in self.mid:
            x = layer(x)
            feas.append(x)
        x = self.tail(x)
        x = x.flatten(1)
        feas.append(x)
        x = self.classifier(x)
        feas.append(x)
        return feas[:-1], feas[1:]


class FashionComplexNetNas(nn.Module):

    """  Complex network"""

    def __init__(self):
        super().__init__()
        self.stem = nn.Sequential(
            nn.Conv2d(1,32, kernel_size=3, padding=1), # 28
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2), # 14
        )

        self.mid = nn.ModuleList()
        for i in range(10):
            self.mid.append(
                nn.ModuleList([
                    nn.Sequential(
                        nn.Conv2d(32,32, kernel_size=k, padding=(k-1)//2), # 14
                        nn.ReLU(inplace=True),
                    ) for k in (1,3,5)])
            )

        self.tail = nn.Sequential(
            nn.Conv2d(32, 64, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2) # 7
        )
        self.classifier = nn.Sequential(
            nn.Dropout(),
            nn.Linear(64 * 7 * 7, 128),
            nn.ReLU(inplace=True),
            nn.Linear(128, 10)
        )
        self.choice  = None
        self.nl = 10  # number of searchable layers
        self.nb = 3  # number of blocks per layer

    def set_choice(self, choice:list):
        assert len(choice) == self.nl
        assert max(choice) < self.nb
        self.choice = choice

    @property
    def random_choice(self) -> List[int]:
        return utils.random_choice(
            self.nb, self.nl)

    @property
    def random_shuffle(self) -> List[List[int]]:
        return utils.random_shuffle(
            self.nb, self.nl)

    def forward(self, x):
        x = self.stem(x)
        for layer, c in zip(self.mid, self.choice):
            x = layer[c](x)
        x = self.tail(x)
        x = x.view(x.size(0), 64 * 7 * 7)
        x = self.classifier(x)
        return x

    def sample(self, choice):
        mid = []
        for layer, c in zip(self.mid, choice):
            mid.append(layer[c])
        mid = nn.Sequential(*mid)

        class M(nn.Module):
            def __init__(selff):
                super().__init__()
                selff.mid = mid
                selff.stem = self.stem
                selff.tail = self.tail
                selff.classifier = self.classifier
            def forward(selff, x):
                return selff.classifier(selff.tail(selff.mid(selff.stem(x))).flatten(1))

        return M()


class FashionComplexNetDistillNas(FashionComplexNetNas):

    def forward_distill(self, teacher_input:List[torch.Tensor]):
        out = []
        Ks = []
        out.append(self.stem(teacher_input[0]))
        Ks.append(utils.flops_counter.get_model_parameters_number(self.stem))
        teacher_input = teacher_input[1:]
        for layer, c, i in zip(self.mid, self.choice, range(len(self.choice))):
            x = teacher_input[i]
            out.append( layer[c](x) )
            Ks.append(utils.flops_counter.get_model_parameters_number(layer[c]))
        i += 1
        out.append(self.tail(teacher_input[i]).flatten(1))
        Ks.append(utils.flops_counter.get_model_parameters_number(self.tail))
        i += 1
        out.append(self.classifier(teacher_input[i]))
        Ks.append(utils.flops_counter.get_model_parameters_number(self.classifier))

        return out, Ks


###############################################################################
#                                ResNet                                       #
###############################################################################

__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
           'resnet152']




def conv3x3(in_planes, out_planes, stride=1):
    "3x3 convolution with padding"
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=1, bias=False)


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                               padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * 4)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class ResNet(nn.Module):

    def __init__(self, block, layers, num_classes=10):
        self.inplanes = 64
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv2d(1, 64, kernel_size=3, stride=1, padding=1,
                               bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        # self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        # self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
        self.avgpool = nn.AvgPool2d(7)
        self.fc = nn.Linear(256 * block.expansion, num_classes)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes))

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        # x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        # x = self.layer4(x)

        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)

        return x


def resnet18(pretrained=False, **kwargs):
    """Constructs a ResNet-18 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
    return model


def resnet34(pretrained=False, **kwargs):
    """Constructs a ResNet-34 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))
    return model


def resnet50(pretrained=False, **kwargs):
    """Constructs a ResNet-50 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
    return model


def resnet101(pretrained=False, **kwargs):
    """Constructs a ResNet-101 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet101']))
    return model


def resnet152(pretrained=False, **kwargs):
    """Constructs a ResNet-152 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet152']))
    return model




if __name__ == '__main__':
    net = resnet18()
    print(net)
    y = net(Variable(torch.randn(1,1,28,28)))
    # size = utils.calculate_feature_size(net.features,(28,28))
    print(y)
