from __future__ import absolute_import
from __future__ import division

__all__ = ['osnet_x1_0', 'osnet_x0_75', 'osnet_x0_5', 'osnet_x0_25', 'osnet_ibn_x1_0']

import torch
from torch import nn
from torch.nn import functional as F
import torchvision


##########
# Basic layers
##########
class ConvLayer(nn.Module):
    """Convolution layer (conv + bn + relu)."""
    
    def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, groups=1, IN=False):
        super(ConvLayer, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride,
                              padding=padding, bias=False, groups=groups)
        if IN:
            self.bn = nn.InstanceNorm2d(out_channels, affine=True)
        else:
            self.bn = nn.BatchNorm2d(out_channels)
        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 Conv1x1(nn.Module):
    """1x1 convolution + bn + relu."""
    
    def __init__(self, in_channels, out_channels, stride=1, groups=1):
        super(Conv1x1, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, 1, stride=stride, padding=0,
                              bias=False, groups=groups)
        self.bn = nn.BatchNorm2d(out_channels)
        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 Conv1x1Linear(nn.Module):
    """1x1 convolution + bn (w/o non-linearity)."""
    
    def __init__(self, in_channels, out_channels, stride=1):
        super(Conv1x1Linear, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, 1, stride=stride, padding=0, bias=False)
        self.bn = nn.BatchNorm2d(out_channels)

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


class Conv3x3(nn.Module):
    """3x3 convolution + bn + relu."""
    
    def __init__(self, in_channels, out_channels, stride=1, groups=1):
        super(Conv3x3, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, 3, stride=stride, padding=1,
                              bias=False, groups=groups)
        self.bn = nn.BatchNorm2d(out_channels)
        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 LightConv3x3(nn.Module):
    """Lightweight 3x3 convolution.

    1x1 (linear) + dw 3x3 (nonlinear).
    """
    
    def __init__(self, in_channels, out_channels):
        super(LightConv3x3, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, 1, stride=1, padding=0, bias=False)
        self.conv2 = nn.Conv2d(out_channels, out_channels, 3, stride=1, padding=1, bias=False, groups=out_channels)
        self.bn = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU(inplace=True)

    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        x = self.bn(x)
        x = self.relu(x)
        return x


##########
# Building blocks for omni-scale feature learning
##########
class ChannelGate(nn.Module):
    """A mini-network that generates channel-wise gates conditioned on input tensor."""

    def __init__(self, in_channels, num_gates=None, return_gates=False,
                 gate_activation='sigmoid', reduction=16, layer_norm=False):
        super(ChannelGate, self).__init__()
        if num_gates is None:
            num_gates = in_channels
        self.return_gates = return_gates
        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
        self.fc1 = nn.Conv2d(in_channels, in_channels//reduction, kernel_size=1, bias=True, padding=0)
        self.norm1 = None
        if layer_norm:
            self.norm1 = nn.LayerNorm((in_channels//reduction, 1, 1))
        self.relu = nn.ReLU(inplace=True)
        self.fc2 = nn.Conv2d(in_channels//reduction, num_gates, kernel_size=1, bias=True, padding=0)
        if gate_activation == 'sigmoid':
            self.gate_activation = nn.Sigmoid()
        elif gate_activation == 'relu':
            self.gate_activation = nn.ReLU(inplace=True)
        elif gate_activation == 'linear':
            self.gate_activation = None
        else:
            raise RuntimeError("Unknown gate activation: {}".format(gate_activation))

    def forward(self, x):
        input = x
        x = self.global_avgpool(x)
        x = self.fc1(x)
        if self.norm1 is not None:
            x = self.norm1(x)
        x = self.relu(x)
        x = self.fc2(x)
        if self.gate_activation is not None:
            x = self.gate_activation(x)
        if self.return_gates:
            return x
        return input * x


class OSBlock(nn.Module):
    """Omni-scale feature learning block."""
    
    def __init__(self, in_channels, out_channels, IN=False, bottleneck_reduction=4, **kwargs):
        super(OSBlock, self).__init__()
        mid_channels = out_channels // bottleneck_reduction
        self.conv1 = Conv1x1(in_channels, mid_channels)
        self.conv2a = LightConv3x3(mid_channels, mid_channels)
        self.conv2b = nn.Sequential(
            LightConv3x3(mid_channels, mid_channels),
            LightConv3x3(mid_channels, mid_channels),
        )
        self.conv2c = nn.Sequential(
            LightConv3x3(mid_channels, mid_channels),
            LightConv3x3(mid_channels, mid_channels),
            LightConv3x3(mid_channels, mid_channels),
        )
        self.conv2d = nn.Sequential(
            LightConv3x3(mid_channels, mid_channels),
            LightConv3x3(mid_channels, mid_channels),
            LightConv3x3(mid_channels, mid_channels),
            LightConv3x3(mid_channels, mid_channels),
        )
        self.gate = ChannelGate(mid_channels)
        self.conv3 = Conv1x1Linear(mid_channels, out_channels)
        self.downsample = None
        if in_channels != out_channels:
            self.downsample = Conv1x1Linear(in_channels, out_channels)
        self.IN = None
        if IN:
            self.IN = nn.InstanceNorm2d(out_channels, affine=True)

    def forward(self, x):
        residual = x
        x1 = self.conv1(x)
        x2a = self.conv2a(x1)
        x2b = self.conv2b(x1)
        x2c = self.conv2c(x1)
        x2d = self.conv2d(x1)
        x2 = self.gate(x2a) + self.gate(x2b) + self.gate(x2c) + self.gate(x2d)
        x3 = self.conv3(x2)
        if self.downsample is not None:
            residual = self.downsample(residual)
        out = x3 + residual
        if self.IN is not None:
            out = self.IN(out)
        return F.relu(out)


##########
# Network architecture
##########
class OSNet(nn.Module):
    """Omni-Scale Network.
    
    Reference:
        - Zhou et al. Omni-Scale Feature Learning for Person Re-Identification. ArXiv preprint, 2019.
          https://arxiv.org/abs/1905.00953
    """

    def __init__(self, num_classes, blocks, layers, channels, feature_dim=512, loss='softmax', IN=False, **kwargs):
        super(OSNet, self).__init__()
        num_blocks = len(blocks)
        assert num_blocks == len(layers)
        assert num_blocks == len(channels) - 1 
        self.loss = loss
        
        # convolutional backbone
        self.conv1 = ConvLayer(3, channels[0], 7, stride=2, padding=3, IN=IN)
        self.maxpool = nn.MaxPool2d(3, stride=2, padding=1)
        self.conv2 = self._make_layer(blocks[0], layers[0], channels[0], channels[1], reduce_spatial_size=True, IN=IN)
        self.conv3 = self._make_layer(blocks[1], layers[1], channels[1], channels[2], reduce_spatial_size=True)
        self.conv4 = self._make_layer(blocks[2], layers[2], channels[2], channels[3], reduce_spatial_size=False)
        self.conv5 = Conv1x1(channels[3], channels[3])
        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
        # fully connected layer
        self.fc = self._construct_fc_layer(feature_dim, channels[3], dropout_p=None)
        # identity classification layer
        self.classifier = nn.Linear(self.feature_dim, num_classes)
        
        self._init_params()

    def _make_layer(self, block, layer, in_channels, out_channels, reduce_spatial_size, IN=False):
        layers = []
        
        layers.append(block(in_channels, out_channels, IN=IN))
        for i in range(1, layer):
            layers.append(block(out_channels, out_channels, IN=IN))
        
        if reduce_spatial_size:
            layers.append(
                nn.Sequential(
                    Conv1x1(out_channels, out_channels),
                    nn.AvgPool2d(2, stride=2)
                )
            )
        
        return nn.Sequential(*layers)

    def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
        if fc_dims is None or fc_dims<0:
            self.feature_dim = input_dim
            return None
        
        if isinstance(fc_dims, int):
            fc_dims = [fc_dims]
        
        layers = []
        for dim in fc_dims:
            layers.append(nn.Linear(input_dim, dim))
            layers.append(nn.BatchNorm1d(dim))
            layers.append(nn.ReLU(inplace=True))
            if dropout_p is not None:
                layers.append(nn.Dropout(p=dropout_p))
            input_dim = dim
        
        self.feature_dim = fc_dims[-1]
        
        return nn.Sequential(*layers)

    def _init_params(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)
            
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
            
            elif isinstance(m, nn.BatchNorm1d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
            
            elif isinstance(m, nn.Linear):
                nn.init.normal_(m.weight, 0, 0.01)
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)

    def featuremaps(self, x):
        x = self.conv1(x)
        x = self.maxpool(x)
        x = self.conv2(x)
        x = self.conv3(x)
        x = self.conv4(x)
        x = self.conv5(x)
        return x

    def forward(self, x):
        x = self.featuremaps(x)
        v = self.global_avgpool(x)
        v = v.view(v.size(0), -1)
        if self.fc is not None:
            v = self.fc(v)
        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))


##########
# Instantiation
##########
def osnet_x1_0(num_classes=1000, loss='softmax', **kwargs):
    # standard size (width x1.0)
    return OSNet(num_classes, blocks=[OSBlock, OSBlock, OSBlock], layers=[2, 2, 2],
                 channels=[64, 256, 384, 512], loss=loss, **kwargs)


def osnet_x0_75(num_classes=1000, loss='softmax', **kwargs):
    # medium size (width x0.75)
    return OSNet(num_classes, blocks=[OSBlock, OSBlock, OSBlock], layers=[2, 2, 2],
                 channels=[48, 192, 288, 384], loss=loss, **kwargs)


def osnet_x0_5(num_classes=1000, loss='softmax', **kwargs):
    # tiny size (width x0.5)
    return OSNet(num_classes, blocks=[OSBlock, OSBlock, OSBlock], layers=[2, 2, 2],
                 channels=[32, 128, 192, 256], loss=loss, **kwargs)


def osnet_x0_25(num_classes=1000, loss='softmax', **kwargs):
    # very tiny size (width x0.25)
    return OSNet(num_classes, blocks=[OSBlock, OSBlock, OSBlock], layers=[2, 2, 2],
                 channels=[16, 64, 96, 128], loss=loss, **kwargs)


def osnet_ibn_x1_0(num_classes=1000, loss='softmax', **kwargs):
    # standard size (width x1.0) + IBN layer
    # Ref: Pan et al. Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net. ECCV, 2018.
    return OSNet(num_classes, blocks=[OSBlock, OSBlock, OSBlock], layers=[2, 2, 2],
                 channels=[64, 256, 384, 512], loss=loss, IN=True, **kwargs)