"""EfficientNet in PyTorch.
Paper: "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks".
"""
import torch
import torch.nn as nn
import torch.nn.functional as F


class Block(nn.Module):
    """expand + depthwise + pointwise + squeeze-excitation"""

    def __init__(self, in_planes, out_planes, expansion, stride):
        super(Block, self).__init__()
        self.stride = stride

        planes = expansion * in_planes
        self.conv1 = nn.Conv2d(
            in_planes, planes, kernel_size=1, stride=1, padding=0, bias=False
        )
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(
            planes,
            planes,
            kernel_size=3,
            stride=stride,
            padding=1,
            groups=planes,
            bias=False,
        )
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(
            planes, out_planes, kernel_size=1, stride=1, padding=0, bias=False
        )
        self.bn3 = nn.BatchNorm2d(out_planes)

        self.shortcut = nn.Sequential()
        if stride == 1 and in_planes != out_planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(
                    in_planes,
                    out_planes,
                    kernel_size=1,
                    stride=1,
                    padding=0,
                    bias=False,
                ),
                nn.BatchNorm2d(out_planes),
            )

        # SE layers
        self.fc1 = nn.Conv2d(out_planes, out_planes // 16, kernel_size=1)
        self.fc2 = nn.Conv2d(out_planes // 16, out_planes, kernel_size=1)

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = F.relu(self.bn2(self.conv2(out)))
        out = self.bn3(self.conv3(out))
        shortcut = self.shortcut(x) if self.stride == 1 else out
        # Squeeze-Excitation
        w = F.avg_pool2d(out, out.size(2))
        w = F.relu(self.fc1(w))
        w = self.fc2(w).sigmoid()
        out = out * w + shortcut
        return out


class EfficientNet(nn.Module):
    def __init__(self, cfg, num_classes=10, features=False):
        super(EfficientNet, self).__init__()
        self.cfg = cfg
        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(32)
        self.layers = self._make_layers(in_planes=32)
        self.linear = nn.Linear(cfg[-1][1], num_classes)
        self.features = features

    def _make_layers(self, in_planes):
        layers = []
        for expansion, out_planes, num_blocks, stride in self.cfg:
            strides = [stride] + [1] * (num_blocks - 1)
            for stride in strides:
                layers.append(Block(in_planes, out_planes, expansion, stride))
                in_planes = out_planes
        return nn.Sequential(*layers)

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.layers(out)
        out = out.view(out.size(0), -1)

        if self.features:
            return out

        else:
            out = self.linear(out)
            return out


def EfficientNetB0(num_classes=10, features=False):
    # (expansion, out_planes, num_blocks, stride)
    cfg = [
        (1, 16, 1, 2),
        (6, 24, 2, 1),
        (6, 40, 2, 2),
        (6, 80, 3, 2),
        (6, 112, 3, 1),
        (6, 192, 4, 2),
        (6, 320, 1, 2),
    ]
    return EfficientNet(cfg, num_classes, features)
