models.py

from __future__ import division

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np

from utils.parse_config import *
from utils.utils import build_targets, to_cpu, non_max_suppression




#创建我们需要的层以及各层的参数
#输出超参数以及网络配置参数模型
def create_modules(module_defs):
    """
    Constructs module list of layer blocks from module configuration in module_defs
    """
    #去掉module_defs模型中的超参数部分，留下网络结构参数部分
    #将去掉的部分赋值给hyperparams
    hyperparams = module_defs.pop(0)
    output_filters = [int(hyperparams["channels"])]
    #---------------------------------------------------
    #网络参数模型接口搭建
    #---------------------------------------------------
    #利用pytorch.nn.modulelist像list一样将网络模型列出，只是用于存储不同网络模块，并未定义网络
    #只有定义了forword，back才会实现
    #如果完全直接用 nn.Sequential，确实是可以的，但这么做的代价就是失去了部分灵活性，不能自己去定制 forward 函数里面的内容了
    #nn.Sequential可以使用OrderedDict对每层进行命名
    #modulelist不能接收参数（因为是无序的所以未设定可接收参数）
    module_list = nn.ModuleList()
    #遍历每一个网络层
    for module_i, module_def in enumerate(module_defs):
        #nn.Sequential跟modulelist一样但是它的里面是有顺序的（他有内部的forward函数，所以储存在这里面的网络层需要按顺序执行）
        #创建一个容器用于包装各层（因为每一个层都是一个组合）
        modules = nn.Sequential()
        if module_def["type"] == "convolutional":
            #对参数进行类型转换
            bn = int(module_def["batch_normalize"])
            filters = int(module_def["filters"])
            kernel_size = int(module_def["size"])
            pad = (kernel_size - 1) // 2
            #添加到容器中
            modules.add_module(
                #网络层数
                f"conv_{module_i}",
                #nn.conv2d代表卷积的创建
                nn.Conv2d(
                    in_channels=output_filters[-1],
                    out_channels=filters,
                    kernel_size=kernel_size,
                    stride=int(module_def["stride"]),
                    padding=pad,
                    bias=not bn,
                ),
            )
            if bn:
                modules.add_module(f"batch_norm_{module_i}", nn.BatchNorm2d(filters, momentum=0.9, eps=1e-5))
            if module_def["activation"] == "leaky":
                #使用leakyrelu作为激活函数
                modules.add_module(f"leaky_{module_i}", nn.LeakyReLU(0.1))

        elif module_def["type"] == "maxpool":
            kernel_size = int(module_def["size"])
            stride = int(module_def["stride"])
            if kernel_size == 2 and stride == 1:
                modules.add_module(f"_debug_padding_{module_i}", nn.ZeroPad2d((0, 1, 0, 1)))
            maxpool = nn.MaxPool2d(kernel_size=kernel_size, stride=stride, padding=int((kernel_size - 1) // 2))
            modules.add_module(f"maxpool_{module_i}", maxpool)

        #上采样
        elif module_def["type"] == "upsample":
            upsample = Upsample(scale_factor=int(module_def["stride"]), mode="nearest")
            modules.add_module(f"upsample_{module_i}", upsample)

        #进行拼接（上采样的时候如何将下放结果与上访结果拼接）
        #路由层
        elif module_def["type"] == "route": # 输入1：26*26*256 输入2：26*26*128  输出：26*26*（256+128）
            layers = [int(x) for x in module_def["layers"].split(",")]
            filters = sum([output_filters[1:][i] for i in layers])
            modules.add_module(f"route_{module_i}", EmptyLayer())

        #残参网络
        elif module_def["type"] == "shortcut":
            filters = output_filters[1:][int(module_def["from"])]
            #创建一个空的层，先占个位置
            modules.add_module(f"shortcut_{module_i}", EmptyLayer())

        elif module_def["type"] == "yolo":
            #取出这个yolo层所对应的anchorbox
            anchor_idxs = [int(x) for x in module_def["mask"].split(",")]
            anchors = [int(x) for x in module_def["anchors"].split(",")]
            anchors = [(anchors[i], anchors[i + 1]) for i in range(0, len(anchors), 2)]
            anchors = [anchors[i] for i in anchor_idxs]

            num_classes = int(module_def["classes"])
            img_size = int(hyperparams["height"])
            #构建yolo层（因为yolo层也相当于一个模块，所以需要定义一个类）
            yolo_layer = YOLOLayer(anchors, num_classes, img_size)
            modules.add_module(f"yolo_{module_i}", yolo_layer)
        # extend是添加另一个modulelist append是添加另一个module
        # 将此层添加到总体模型中
        module_list.append(modules)

        output_filters.append(filters)

    return hyperparams, module_list

#上采样
class Upsample(nn.Module):
    """ nn.Upsample is deprecated """

    def __init__(self, scale_factor, mode="nearest"):
        super(Upsample, self).__init__()
        self.scale_factor = scale_factor
        self.mode = mode
    #进行数组采样操作
    def forward(self, x):
        x = F.interpolate(x, scale_factor=self.scale_factor, mode=self.mode)
        return x

#空层
class EmptyLayer(nn.Module):
    """Placeholder for 'route' and 'shortcut' layers"""

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

#yolo层
#坐标变换，损失函数......
class YOLOLayer(nn.Module):
    """Detection layer"""

    def __init__(self, anchors, num_classes, img_dim=416):
        super(YOLOLayer, self).__init__()
        self.anchors = anchors
        self.num_anchors = len(anchors)
        self.num_classes = num_classes
        self.ignore_thres = 0.5
        #损失函数调用的函数
        self.mse_loss = nn.MSELoss()
        self.bce_loss = nn.BCELoss()
        self.obj_scale = 1
        self.noobj_scale = 100
        self.metrics = {}
        self.img_dim = img_dim
        self.grid_size = 0  # grid size

#计算预选框按比例的宽和高
    def compute_grid_offsets(self, grid_size, cuda=True):
        self.grid_size = grid_size
        g = self.grid_size
        FloatTensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor
        self.stride = self.img_dim / self.grid_size

        # 以左上角为原点建立坐标轴似的tensor数组，（所以相对图片的中心店位置就是所在各自数组量+x就可以了）
        self.grid_x = torch.arange(g).repeat(g, 1).view([1, 1, g, g]).type(FloatTensor)
        self.grid_y = torch.arange(g).repeat(g, 1).t().view([1, 1, g, g]).type(FloatTensor) 
        #将实际预选框值也转化为特征图的比例
        self.scaled_anchors = FloatTensor([(a_w / self.stride, a_h / self.stride) for a_w, a_h in self.anchors])
        self.anchor_w = self.scaled_anchors[:, 0:1].view((1, self.num_anchors, 1, 1))
        self.anchor_h = self.scaled_anchors[:, 1:2].view((1, self.num_anchors, 1, 1))

    def forward(self, x, targets=None, img_dim=None):
        # Tensors for cuda support
        #x的值[batch_size, 卷积核多少，N*N]，例子[3, 255, 13, 13]
        #print (x.shape)
        #指定我们使用GPU跑的还是用CPU跑的（将tensor的格式设置一下）
        #cuda是 GPU
        FloatTensor = torch.cuda.FloatTensor if x.is_cuda else torch.FloatTensor
        LongTensor = torch.cuda.LongTensor if x.is_cuda else torch.LongTensor
        ByteTensor = torch.cuda.ByteTensor if x.is_cuda else torch.ByteTensor


        self.img_dim = img_dim
        #取得batch
        num_samples = x.size(0)
        #取得图片大小（就是除以32的值）
        grid_size = x.size(2)
        #最终要预测的一个结果
        prediction = (
            #view修改维度，跟resize差不多
            #num_samples就是batch_size，num_anchors就是预选框的个数，self.numclasses+5(xy,wh,confidence+class),grid_size(网格的大小)
            #eg:[4,3,15,15,85]
            x.view(num_samples, self.num_anchors, self.num_classes + 5, grid_size, grid_size)
            .permute(0, 1, 3, 4, 2)
            .contiguous()
        )
        #print (prediction.shape)
        #预测的个点坐标(利用sigmoid函数)
        #x,y是相对于cell左上角点的位置
        x = torch.sigmoid(prediction[..., 0])  # Center x
        y = torch.sigmoid(prediction[..., 1])  # Center y
        w = prediction[..., 2]  # Width
        h = prediction[..., 3]  # Height
        pred_conf = torch.sigmoid(prediction[..., 4])  # Conf
        pred_cls = torch.sigmoid(prediction[..., 5:])  # class

        # 求预选框按比例的宽和高
        print(grid_size)
        if grid_size != self.grid_size:
            self.compute_grid_offsets(grid_size, cuda=x.is_cuda) #相对位置得到对应的绝对位置比如之前的位置是0.5,0.5变为 11.5，11.5这样的

        #特征图中的实际位置以及w，h
        #https://zhuanlan.zhihu.com/p/367395847
        pred_boxes = FloatTensor(prediction[..., :4].shape)
        pred_boxes[..., 0] = x.data + self.grid_x
        pred_boxes[..., 1] = y.data + self.grid_y
        pred_boxes[..., 2] = torch.exp(w.data) * self.anchor_w
        pred_boxes[..., 3] = torch.exp(h.data) * self.anchor_h
        #还原成实际图片的位置大小
        output = torch.cat( 
            (
                pred_boxes.view(num_samples, -1, 4) * self.stride, #还原到原始图中
                pred_conf.view(num_samples, -1, 1),
                pred_cls.view(num_samples, -1, self.num_classes),
            ),
            -1,
        )

        #计算损失值
        if targets is None:
            return output, 0
        else:
            iou_scores, class_mask, obj_mask, noobj_mask, tx, ty, tw, th, tcls, tconf = build_targets(
                pred_boxes=pred_boxes,
                pred_cls=pred_cls,
                target=targets,
                anchors=self.scaled_anchors,
                ignore_thres=self.ignore_thres,
            )
            # iou_scores：真实值与最匹配的anchor的IOU得分值 class_mask：分类正确的索引  obj_mask：目标框所在位置的最好anchor置为1 noobj_mask obj_mask那里置0，还有计算的iou大于阈值的也置0，其他都为1 tx, ty, tw, th, 对应的对于该大小的特征图的xywh目标值也就是我们需要拟合的值 tconf 目标置信度
            # Loss : Mask outputs to ignore non-existing objects (except with conf. loss)
            loss_x = self.mse_loss(x[obj_mask], tx[obj_mask]) # 只计算有目标的
            loss_y = self.mse_loss(y[obj_mask], ty[obj_mask])
            loss_w = self.mse_loss(w[obj_mask], tw[obj_mask])
            loss_h = self.mse_loss(h[obj_mask], th[obj_mask])
            loss_conf_obj = self.bce_loss(pred_conf[obj_mask], tconf[obj_mask]) 
            loss_conf_noobj = self.bce_loss(pred_conf[noobj_mask], tconf[noobj_mask])
            loss_conf = self.obj_scale * loss_conf_obj + self.noobj_scale * loss_conf_noobj #有物体越接近1越好 没物体的越接近0越好
            loss_cls = self.bce_loss(pred_cls[obj_mask], tcls[obj_mask]) #分类损失
            total_loss = loss_x + loss_y + loss_w + loss_h + loss_conf + loss_cls #总损失

            # Metrics
            cls_acc = 100 * class_mask[obj_mask].mean()
            conf_obj = pred_conf[obj_mask].mean()
            conf_noobj = pred_conf[noobj_mask].mean()
            conf50 = (pred_conf > 0.5).float()
            iou50 = (iou_scores > 0.5).float()
            iou75 = (iou_scores > 0.75).float()
            detected_mask = conf50 * class_mask * tconf
            precision = torch.sum(iou50 * detected_mask) / (conf50.sum() + 1e-16)
            recall50 = torch.sum(iou50 * detected_mask) / (obj_mask.sum() + 1e-16)
            recall75 = torch.sum(iou75 * detected_mask) / (obj_mask.sum() + 1e-16)

            self.metrics = {
                "loss": to_cpu(total_loss).item(),
                "x": to_cpu(loss_x).item(),
                "y": to_cpu(loss_y).item(),
                "w": to_cpu(loss_w).item(),
                "h": to_cpu(loss_h).item(),
                "conf": to_cpu(loss_conf).item(),
                "cls": to_cpu(loss_cls).item(),
                "cls_acc": to_cpu(cls_acc).item(),
                "recall50": to_cpu(recall50).item(),
                "recall75": to_cpu(recall75).item(),
                "precision": to_cpu(precision).item(),
                "conf_obj": to_cpu(conf_obj).item(),
                "conf_noobj": to_cpu(conf_noobj).item(),
                "grid_size": grid_size,
            }

            return output, total_loss

#搭建网络
class Darknet(nn.Module):
    """YOLOv3 object detection model"""
    #---------------------------------------------------------------
    #对网络搭建的参数以及结构，从yolov3.cfg模块中导入(special_model_1)
    #---------------------------------------------------------------
    def __init__(self, config_path, img_size=416):
        super(Darknet, self).__init__()
        #读入配置文件
        self.module_defs = parse_model_config(config_path)
        #创建模型
        #只是将不同模块储存起来作为网络的接口
        self.hyperparams, self.module_list = create_modules(self.module_defs)
        self.yolo_layers = [layer[0] for layer in self.module_list if hasattr(layer[0], "metrics")]
        self.img_size = img_size
        self.seen = 0
        self.header_info = np.array([0, 0, 0, self.seen, 0], dtype=np.int32)

    #正向传播过程
    def forward(self, x, targets=None):
        img_dim = x.shape[2]
        loss = 0
        layer_outputs, yolo_outputs = [], []
        for i, (module_def, module) in enumerate(zip(self.module_defs, self.module_list)):
            #如果是卷积，上采样，池化则直接用madule执行
            if module_def["type"] in ["convolutional", "upsample", "maxpool"]:
                x = module(x)
            #route层进行拼接,上下落着拼接
            elif module_def["type"] == "route":
                x = torch.cat([layer_outputs[int(layer_i)] for layer_i in module_def["layers"].split(",")], 1)
            #残参网络
            elif module_def["type"] == "shortcut":
                #取出与第几层做加法操作
                layer_i = int(module_def["from"])
                x = layer_outputs[-1] + layer_outputs[layer_i]
            elif module_def["type"] == "yolo":
                #进入yolo中的forward
                x, layer_loss = module[0](x, targets, img_dim)
                loss += layer_loss
                yolo_outputs.append(x)
            #将每次的img传入layer_outputs列表当中
            layer_outputs.append(x)
        yolo_outputs = to_cpu(torch.cat(yolo_outputs, 1))
        return yolo_outputs if targets is None else (loss, yolo_outputs)

    def load_darknet_weights(self, weights_path):
        """Parses and loads the weights stored in 'weights_path'"""

        # Open the weights file
        with open(weights_path, "rb") as f:
            header = np.fromfile(f, dtype=np.int32, count=5)  # First five are header values
            self.header_info = header  # Needed to write header when saving weights
            self.seen = header[3]  # number of images seen during training
            weights = np.fromfile(f, dtype=np.float32)  # The rest are weights

        # Establish cutoff for loading backbone weights
        cutoff = None
        if "darknet53.conv.74" in weights_path:
            cutoff = 75

        ptr = 0
        for i, (module_def, module) in enumerate(zip(self.module_defs, self.module_list)):
            if i == cutoff:
                break
            if module_def["type"] == "convolutional":
                conv_layer = module[0]
                if module_def["batch_normalize"]:
                    # Load BN bias, weights, running mean and running variance
                    bn_layer = module[1]
                    num_b = bn_layer.bias.numel()  # Number of biases
                    # Bias
                    bn_b = torch.from_numpy(weights[ptr : ptr + num_b]).view_as(bn_layer.bias)
                    bn_layer.bias.data.copy_(bn_b)
                    ptr += num_b
                    # Weight
                    bn_w = torch.from_numpy(weights[ptr : ptr + num_b]).view_as(bn_layer.weight)
                    bn_layer.weight.data.copy_(bn_w)
                    ptr += num_b
                    # Running Mean
                    bn_rm = torch.from_numpy(weights[ptr : ptr + num_b]).view_as(bn_layer.running_mean)
                    bn_layer.running_mean.data.copy_(bn_rm)
                    ptr += num_b
                    # Running Var
                    bn_rv = torch.from_numpy(weights[ptr : ptr + num_b]).view_as(bn_layer.running_var)
                    bn_layer.running_var.data.copy_(bn_rv)
                    ptr += num_b
                else:
                    # Load conv. bias
                    num_b = conv_layer.bias.numel()
                    conv_b = torch.from_numpy(weights[ptr : ptr + num_b]).view_as(conv_layer.bias)
                    conv_layer.bias.data.copy_(conv_b)
                    ptr += num_b
                # Load conv. weights
                num_w = conv_layer.weight.numel()
                conv_w = torch.from_numpy(weights[ptr : ptr + num_w]).view_as(conv_layer.weight)
                conv_layer.weight.data.copy_(conv_w)
                ptr += num_w

    def save_darknet_weights(self, path, cutoff=-1):
        """
            @:param path    - path of the new weights file
            @:param cutoff  - save layers between 0 and cutoff (cutoff = -1 -> all are saved)
        """
        fp = open(path, "wb")
        self.header_info[3] = self.seen
        self.header_info.tofile(fp)

        # Iterate through layers
        for i, (module_def, module) in enumerate(zip(self.module_defs[:cutoff], self.module_list[:cutoff])):
            if module_def["type"] == "convolutional":
                conv_layer = module[0]
                # If batch norm, load bn first
                if module_def["batch_normalize"]:
                    bn_layer = module[1]
                    bn_layer.bias.data.cpu().numpy().tofile(fp)
                    bn_layer.weight.data.cpu().numpy().tofile(fp)
                    bn_layer.running_mean.data.cpu().numpy().tofile(fp)
                    bn_layer.running_var.data.cpu().numpy().tofile(fp)
                # Load conv bias
                else:
                    conv_layer.bias.data.cpu().numpy().tofile(fp)
                # Load conv weights
                conv_layer.weight.data.cpu().numpy().tofile(fp)

        fp.close()