深度學(xué)習(xí)（23）——YOLO系列（2）

yolo-V3完整項(xiàng)目請?jiān)煸LJane的GitHub：在這里等你哦

1. model

model.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

import matplotlib.pyplot as plt
import matplotlib.patches as patches


def create_modules(module_defs):
    """
    Constructs module list of layer blocks from module configuration in module_defs
    """
    hyperparams = module_defs.pop(0)
    output_filters = [int(hyperparams["channels"])]
    module_list = nn.ModuleList()
    for module_i, module_def in enumerate(module_defs):
        modules = nn.Sequential()

        if module_def["type"] == "convolutional":
            bn = int(module_def["batch_normalize"])
            filters = int(module_def["filters"])
            kernel_size = int(module_def["size"])
            pad = (kernel_size - 1) // 2
            modules.add_module(
                f"conv_{module_i}",
                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":
                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)

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

        elif module_def["type"] == "route": # 輸入1：26*26*256 輸入2：26*26*128  輸出：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())

        elif module_def["type"] == "shortcut":
            filters = output_filters[1:][int(module_def["from"])]
            modules.add_module(f"shortcut_{module_i}", EmptyLayer())

        elif module_def["type"] == "yolo":
            anchor_idxs = [int(x) for x in module_def["mask"].split(",")]
            # Extract anchors
            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"])
            # Define detection layer
            yolo_layer = YOLOLayer(anchors, num_classes, img_size)
            modules.add_module(f"yolo_{module_i}", yolo_layer)
        # Register module list and number of output filters
        module_list.append(modules)
        output_filters.append(filters)

    return hyperparams, module_list


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

    def forward(self, x):
        x = F.interpolate(x, scale_factor=self.scale_factor, mode=self.mode)
        return x


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

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


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
        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

    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
        # Calculate offsets for each grid
        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)
        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
        print (x.shape)
        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
        num_samples = x.size(0)
        grid_size = x.size(2)

        prediction = (
            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)
        # Get outputs
        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:])  # Cls pred.

        # If grid size does not match current we compute new offsets
        if grid_size != self.grid_size:
            self.compute_grid_offsets(grid_size, cuda=x.is_cuda) #相對位置得到對應(yīng)的絕對位置比如之前的位置是0.5,0.5變?yōu)?11.5，11.5這樣的

        # Add offset and scale with anchors #特征圖中的實(shí)際位置
        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

        output = torch.cat( 
            (
                pred_boxes.view(num_samples, -1, 4) * self.stride, #還原到原始圖中
                pred_conf.view(num_samples, -1, 1),
                pred_cls.view(num_samples, -1, self.num_classes),
            ),
            -1,
        )

        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：真實(shí)值與最匹配的anchor的IOU得分值 class_mask：分類正確的索引  obj_mask：目標(biāo)框所在位置的最好anchor置為1 noobj_mask obj_mask那里置0，還有計(jì)算的iou大于閾值的也置0，其他都為1 tx, ty, tw, th, 對應(yīng)的對于該大小的特征圖的xywh目標(biāo)值也就是我們需要擬合的值 tconf 目標(biāo)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]) # 只計(jì)算有目標(biāo)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 #有物體越接近1越好 沒物體的越接近0越好
            loss_cls = self.bce_loss(pred_cls[obj_mask], tcls[obj_mask]) #分類損失
            total_loss = loss_x + loss_y + loss_w + loss_h + loss_conf + loss_cls #總損失

            # 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


class Darknet(nn.Module):
    """YOLOv3 object detection model"""

    def __init__(self, config_path, img_size=416):
        super(Darknet, self).__init__()
        self.module_defs = parse_model_config(config_path)
        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)

    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)):
            if module_def["type"] in ["convolutional", "upsample", "maxpool"]:
                x = module(x)
            elif module_def["type"] == "route":
                x = torch.cat([layer_outputs[int(layer_i)] for layer_i in module_def["layers"].split(",")], 1)
            elif module_def["type"] == "shortcut": # 殘差連接（位相加）
                layer_i = int(module_def["from"])
                x = layer_outputs[-1] + layer_outputs[layer_i]
            elif module_def["type"] == "yolo":
                x, layer_loss = module[0](x, targets, img_dim)
                loss += layer_loss
                yolo_outputs.append(x)
            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()

一共三個yolo層
模型定義這一塊：叫做darknet，其中最重要的部分就是YOLO層。還有一個容易混淆的地方：route和shortcut層，前者是拼接，后者是殘差連接的位相加。

2. dataset

dataset.py

import glob
import random
import os
import sys
import numpy as np
from PIL import Image
import torch
import torch.nn.functional as F

from utils.augmentations import horisontal_flip
from torch.utils.data import Dataset
import torchvision.transforms as transforms

def pad_to_square(img, pad_value):
    c, h, w = img.shape
    dim_diff = np.abs(h - w)
    # (upper / left) padding and (lower / right) padding
    pad1, pad2 = dim_diff // 2, dim_diff - dim_diff // 2
    # Determine padding
    pad = (0, 0, pad1, pad2) if h <= w else (pad1, pad2, 0, 0)
    # Add padding
    img = F.pad(img, pad, "constant", value=pad_value)

    return img, pad


def resize(image, size):
    image = F.interpolate(image.unsqueeze(0), size=size, mode="nearest").squeeze(0)
    return image


def random_resize(images, min_size=288, max_size=448):
    new_size = random.sample(list(range(min_size, max_size + 1, 32)), 1)[0]
    images = F.interpolate(images, size=new_size, mode="nearest")
    return images


class ImageFolder(Dataset):
    def __init__(self, folder_path, img_size=416):
        self.files = sorted(glob.glob("%s/*.*" % folder_path))
        self.img_size = img_size

    def __getitem__(self, index):
        img_path = self.files[index % len(self.files)]
        # Extract image as PyTorch tensor
        img = transforms.ToTensor()(Image.open(img_path))
        # Pad to square resolution
        img, _ = pad_to_square(img, 0)
        # Resize
        img = resize(img, self.img_size)

        return img_path, img

    def __len__(self):
        return len(self.files)


class ListDataset(Dataset):
    def __init__(self, list_path, img_size=416, augment=True, multiscale=True, normalized_labels=True):
        with open(list_path, "r") as file:
            self.img_files = file.readlines()

        self.label_files = [
            path.replace("images", "labels").replace(".png", ".txt").replace(".jpg", ".txt")
            for path in self.img_files
        ]
        self.img_size = img_size
        self.max_objects = 100
        self.augment = augment
        self.multiscale = multiscale
        self.normalized_labels = normalized_labels
        self.min_size = self.img_size - 3 * 32
        self.max_size = self.img_size + 3 * 32
        self.batch_count = 0

    def __getitem__(self, index):

        # ---------
        #  Image
        # ---------

        img_path = self.img_files[index % len(self.img_files)].rstrip()
        img_path = r'../YOLOv3/data/coco' + img_path
        #print (img_path)
        # Extract image as PyTorch tensor
        img = transforms.ToTensor()(Image.open(img_path).convert('RGB'))

        # Handle images with less than three channels
        if len(img.shape) != 3:
            img = img.unsqueeze(0)
            img = img.expand((3, img.shape[1:]))

        _, h, w = img.shape
        h_factor, w_factor = (h, w) if self.normalized_labels else (1, 1)
        # Pad to square resolution
        img, pad = pad_to_square(img, 0)
        _, padded_h, padded_w = img.shape

        # ---------
        #  Label
        # ---------

        label_path = self.label_files[index % len(self.img_files)].rstrip()
        label_path = r'../YOLOv3/data/coco/labels' + label_path
        #print (label_path)

        targets = None
        if os.path.exists(label_path):
            boxes = torch.from_numpy(np.loadtxt(label_path).reshape(-1, 5))
            # Extract coordinates for unpadded + unscaled image
            x1 = w_factor * (boxes[:, 1] - boxes[:, 3] / 2)
            y1 = h_factor * (boxes[:, 2] - boxes[:, 4] / 2)
            x2 = w_factor * (boxes[:, 1] + boxes[:, 3] / 2)
            y2 = h_factor * (boxes[:, 2] + boxes[:, 4] / 2)
            # Adjust for added padding
            x1 += pad[0]
            y1 += pad[2]
            x2 += pad[1]
            y2 += pad[3]
            # Returns (x, y, w, h)
            boxes[:, 1] = ((x1 + x2) / 2) / padded_w
            boxes[:, 2] = ((y1 + y2) / 2) / padded_h
            boxes[:, 3] *= w_factor / padded_w
            boxes[:, 4] *= h_factor / padded_h

            targets = torch.zeros((len(boxes), 6))
            targets[:, 1:] = boxes

        # Apply augmentations
        if self.augment:
            if np.random.random() < 0.5:
                img, targets = horisontal_flip(img, targets)

        return img_path, img, targets

    def collate_fn(self, batch):
        paths, imgs, targets = list(zip(*batch))
        # Remove empty placeholder targets
        targets = [boxes for boxes in targets if boxes is not None]
        # Add sample index to targets
        for i, boxes in enumerate(targets):
            boxes[:, 0] = i
        targets = torch.cat(targets, 0)
        # Selects new image size every tenth batch
        if self.multiscale and self.batch_count % 10 == 0:
            self.img_size = random.choice(range(self.min_size, self.max_size + 1, 32))
        # Resize images to input shape
        imgs = torch.stack([resize(img, self.img_size) for img in imgs])
        self.batch_count += 1
        return paths, imgs, targets

    def __len__(self):
        return len(self.img_files)

dataset在test部分只用到ImageFolder，pad_to_square()，list_dataset在train中使用。

• pad_to_square()用于將長方形的圖片用0 值padding成正方形

3. utils

utils.py

from __future__ import division
import math
import time
import tqdm
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as patches


def to_cpu(tensor):
    return tensor.detach().cpu()


def load_classes(path):
    """
    Loads class labels at 'path'
    """
    fp = open(path, "r")
    names = fp.read().split("\n")[:-1]
    return names


def weights_init_normal(m):
    classname = m.__class__.__name__
    if classname.find("Conv") != -1:
        torch.nn.init.normal_(m.weight.data, 0.0, 0.02)
    elif classname.find("BatchNorm2d") != -1:
        torch.nn.init.normal_(m.weight.data, 1.0, 0.02)
        torch.nn.init.constant_(m.bias.data, 0.0)


def rescale_boxes(boxes, current_dim, original_shape):
    """ Rescales bounding boxes to the original shape """
    orig_h, orig_w = original_shape
    # The amount of padding that was added
    pad_x = max(orig_h - orig_w, 0) * (current_dim / max(original_shape))
    pad_y = max(orig_w - orig_h, 0) * (current_dim / max(original_shape))
    # Image height and width after padding is removed
    unpad_h = current_dim - pad_y
    unpad_w = current_dim - pad_x
    # Rescale bounding boxes to dimension of original image
    boxes[:, 0] = ((boxes[:, 0] - pad_x // 2) / unpad_w) * orig_w
    boxes[:, 1] = ((boxes[:, 1] - pad_y // 2) / unpad_h) * orig_h
    boxes[:, 2] = ((boxes[:, 2] - pad_x // 2) / unpad_w) * orig_w
    boxes[:, 3] = ((boxes[:, 3] - pad_y // 2) / unpad_h) * orig_h
    return boxes


def xywh2xyxy(x):# x,y是框的中心點(diǎn)不是左上角也不是右下角
    y = x.new(x.shape)
    y[..., 0] = x[..., 0] - x[..., 2] / 2
    y[..., 1] = x[..., 1] - x[..., 3] / 2
    y[..., 2] = x[..., 0] + x[..., 2] / 2
    y[..., 3] = x[..., 1] + x[..., 3] / 2
    return y


def ap_per_class(tp, conf, pred_cls, target_cls):
    """ Compute the average precision, given the recall and precision curves.
    Source: https://github.com/rafaelpadilla/Object-Detection-Metrics.
    # Arguments
        tp:    True positives (list).
        conf:  Objectness value from 0-1 (list).
        pred_cls: Predicted object classes (list).
        target_cls: True object classes (list).
    # Returns
        The average precision as computed in py-faster-rcnn.
    """

    # Sort by objectness
    i = np.argsort(-conf)
    tp, conf, pred_cls = tp[i], conf[i], pred_cls[i]

    # Find unique classes
    unique_classes = np.unique(target_cls)

    # Create Precision-Recall curve and compute AP for each class
    ap, p, r = [], [], []
    for c in tqdm.tqdm(unique_classes, desc="Computing AP"):
        i = pred_cls == c
        n_gt = (target_cls == c).sum()  # Number of ground truth objects
        n_p = i.sum()  # Number of predicted objects

        if n_p == 0 and n_gt == 0:
            continue
        elif n_p == 0 or n_gt == 0:
            ap.append(0)
            r.append(0)
            p.append(0)
        else:
            # Accumulate FPs and TPs
            fpc = (1 - tp[i]).cumsum()
            tpc = (tp[i]).cumsum()

            # Recall
            recall_curve = tpc / (n_gt + 1e-16)
            r.append(recall_curve[-1])

            # Precision
            precision_curve = tpc / (tpc + fpc)
            p.append(precision_curve[-1])

            # AP from recall-precision curve
            ap.append(compute_ap(recall_curve, precision_curve))

    # Compute F1 score (harmonic mean of precision and recall)
    p, r, ap = np.array(p), np.array(r), np.array(ap)
    f1 = 2 * p * r / (p + r + 1e-16)

    return p, r, ap, f1, unique_classes.astype("int32")


def compute_ap(recall, precision):
    """ Compute the average precision, given the recall and precision curves.
    Code originally from https://github.com/rbgirshick/py-faster-rcnn.

    # Arguments
        recall:    The recall curve (list).
        precision: The precision curve (list).
    # Returns
        The average precision as computed in py-faster-rcnn.
    """
    # correct AP calculation
    # first append sentinel values at the end
    mrec = np.concatenate(([0.0], recall, [1.0]))
    mpre = np.concatenate(([0.0], precision, [0.0]))

    # compute the precision envelope
    for i in range(mpre.size - 1, 0, -1):
        mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])

    # to calculate area under PR curve, look for points
    # where X axis (recall) changes value
    i = np.where(mrec[1:] != mrec[:-1])[0]

    # and sum (\Delta recall) * prec
    ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])
    return ap


def get_batch_statistics(outputs, targets, iou_threshold):
    """ Compute true positives, predicted scores and predicted labels per sample """
    batch_metrics = []
    for sample_i in range(len(outputs)):

        if outputs[sample_i] is None:
            continue

        output = outputs[sample_i]
        pred_boxes = output[:, :4]
        pred_scores = output[:, 4]
        pred_labels = output[:, -1]

        true_positives = np.zeros(pred_boxes.shape[0])

        annotations = targets[targets[:, 0] == sample_i][:, 1:]
        target_labels = annotations[:, 0] if len(annotations) else []
        if len(annotations):
            detected_boxes = []
            target_boxes = annotations[:, 1:]

            for pred_i, (pred_box, pred_label) in enumerate(zip(pred_boxes, pred_labels)):

                # If targets are found break
                if len(detected_boxes) == len(annotations):
                    break

                # Ignore if label is not one of the target labels
                if pred_label not in target_labels:
                    continue

                iou, box_index = bbox_iou(pred_box.unsqueeze(0), target_boxes).max(0)
                if iou >= iou_threshold and box_index not in detected_boxes:
                    true_positives[pred_i] = 1
                    detected_boxes += [box_index]
        batch_metrics.append([true_positives, pred_scores, pred_labels])
    return batch_metrics


def bbox_wh_iou(wh1, wh2):
    wh2 = wh2.t()
    w1, h1 = wh1[0], wh1[1]
    w2, h2 = wh2[0], wh2[1]
    inter_area = torch.min(w1, w2) * torch.min(h1, h2)
    union_area = (w1 * h1 + 1e-16) + w2 * h2 - inter_area
    return inter_area / union_area


def bbox_iou(box1, box2, x1y1x2y2=True):
    """
    Returns the IoU of two bounding boxes
    """
    if not x1y1x2y2:
        # Transform from center and width to exact coordinates
        b1_x1, b1_x2 = box1[:, 0] - box1[:, 2] / 2, box1[:, 0] + box1[:, 2] / 2
        b1_y1, b1_y2 = box1[:, 1] - box1[:, 3] / 2, box1[:, 1] + box1[:, 3] / 2
        b2_x1, b2_x2 = box2[:, 0] - box2[:, 2] / 2, box2[:, 0] + box2[:, 2] / 2
        b2_y1, b2_y2 = box2[:, 1] - box2[:, 3] / 2, box2[:, 1] + box2[:, 3] / 2
    else:
        # Get the coordinates of bounding boxes
        b1_x1, b1_y1, b1_x2, b1_y2 = box1[:, 0], box1[:, 1], box1[:, 2], box1[:, 3]
        b2_x1, b2_y1, b2_x2, b2_y2 = box2[:, 0], box2[:, 1], box2[:, 2], box2[:, 3]

    # get the corrdinates of the intersection rectangle
    inter_rect_x1 = torch.max(b1_x1, b2_x1)
    inter_rect_y1 = torch.max(b1_y1, b2_y1)
    inter_rect_x2 = torch.min(b1_x2, b2_x2)
    inter_rect_y2 = torch.min(b1_y2, b2_y2)
    # Intersection area
    inter_area = torch.clamp(inter_rect_x2 - inter_rect_x1 + 1, min=0) * torch.clamp(
        inter_rect_y2 - inter_rect_y1 + 1, min=0
    )
    # Union Area
    b1_area = (b1_x2 - b1_x1 + 1) * (b1_y2 - b1_y1 + 1)
    b2_area = (b2_x2 - b2_x1 + 1) * (b2_y2 - b2_y1 + 1)

    iou = inter_area / (b1_area + b2_area - inter_area + 1e-16)

    return iou


def non_max_suppression(prediction, conf_thres=0.5, nms_thres=0.4):
    """
    Removes detections with lower object confidence score than 'conf_thres' and performs
    Non-Maximum Suppression to further filter detections.
    Returns detections with shape:
        (x1, y1, x2, y2, object_conf, class_score, class_pred)
    """

    # From (center x, center y, width, height) to (x1, y1, x2, y2)
    prediction[..., :4] = xywh2xyxy(prediction[..., :4])
    output = [None for _ in range(len(prediction))]
    for image_i, image_pred in enumerate(prediction):
        # Filter out confidence scores below threshold
        image_pred = image_pred[image_pred[:, 4] >= conf_thres]
        # If none are remaining => process next image
        if not image_pred.size(0):
            continue
        # Object confidence times class confidence
        score = image_pred[:, 4] * image_pred[:, 5:].max(1)[0]
        # Sort by it
        image_pred = image_pred[(-score).argsort()]
        class_confs, class_preds = image_pred[:, 5:].max(1, keepdim=True)
        detections = torch.cat((image_pred[:, :5], class_confs.float(), class_preds.float()), 1)
        # Perform non-maximum suppression 極大值抑制
        keep_boxes = []
        while detections.size(0):
            large_overlap = bbox_iou(detections[0, :4].unsqueeze(0), detections[:, :4]) > nms_thres
            label_match = detections[0, -1] == detections[:, -1]
            # Indices of boxes with lower confidence scores, large IOUs and matching labels
            invalid = large_overlap & label_match
            weights = detections[invalid, 4:5]
            # Merge overlapping bboxes by order of confidence
            detections[0, :4] = (weights * detections[invalid, :4]).sum(0) / weights.sum()
            keep_boxes += [detections[0]]
            detections = detections[~invalid]
        if keep_boxes:
            output[image_i] = torch.stack(keep_boxes)

    return output


def build_targets(pred_boxes, pred_cls, target, anchors, ignore_thres):

    ByteTensor = torch.cuda.ByteTensor if pred_boxes.is_cuda else torch.ByteTensor
    FloatTensor = torch.cuda.FloatTensor if pred_boxes.is_cuda else torch.FloatTensor

    nB = pred_boxes.size(0) # batchsieze 4
    nA = pred_boxes.size(1) # 每個格子對應(yīng)了多少個anchor
    nC = pred_cls.size(-1)  # 類別的數(shù)量
    nG = pred_boxes.size(2) # gridsize

    # Output tensors
    obj_mask = ByteTensor(nB, nA, nG, nG).fill_(0)  # obj，anchor包含物體, 即為1，默認(rèn)為0 考慮前景
    noobj_mask = ByteTensor(nB, nA, nG, nG).fill_(1) # noobj, anchor不包含物體, 則為1，默認(rèn)為1 考慮背景
    class_mask = FloatTensor(nB, nA, nG, nG).fill_(0) # 類別掩膜，類別預(yù)測正確即為1，默認(rèn)全為0
    iou_scores = FloatTensor(nB, nA, nG, nG).fill_(0) # 預(yù)測框與真實(shí)框的iou得分
    tx = FloatTensor(nB, nA, nG, nG).fill_(0) # 真實(shí)框相對于網(wǎng)格的位置
    ty = FloatTensor(nB, nA, nG, nG).fill_(0)
    tw = FloatTensor(nB, nA, nG, nG).fill_(0) 
    th = FloatTensor(nB, nA, nG, nG).fill_(0)
    tcls = FloatTensor(nB, nA, nG, nG, nC).fill_(0)

    # Convert to position relative to box
    target_boxes = target[:, 2:6] * nG #target中的xywh都是0-1的，可以得到其在當(dāng)前gridsize上的xywh
    gxy = target_boxes[:, :2]
    gwh = target_boxes[:, 2:]
    # Get anchors with best iou
    ious = torch.stack([bbox_wh_iou(anchor, gwh) for anchor in anchors]) #每一種規(guī)格的anchor跟每個標(biāo)簽上的框的IOU得分
    print (ious.shape)
    best_ious, best_n = ious.max(0) # 得到其最高分以及哪種規(guī)格框和當(dāng)前目標(biāo)最相似
    # Separate target values
    b, target_labels = target[:, :2].long().t() # 真實(shí)框所對應(yīng)的batch，以及每個框所代表的實(shí)際類別
    gx, gy = gxy.t()
    gw, gh = gwh.t()
    gi, gj = gxy.long().t() #位置信息，向下取整了
    # Set masks
    obj_mask[b, best_n, gj, gi] = 1 # 實(shí)際包含物體的設(shè)置成1
    noobj_mask[b, best_n, gj, gi] = 0 # 相反

    # Set noobj mask to zero where iou exceeds ignore threshold
    for i, anchor_ious in enumerate(ious.t()): # IOU超過了指定的閾值就相當(dāng)于有物體了
        noobj_mask[b[i], anchor_ious > ignore_thres, gj[i], gi[i]] = 0

    # Coordinates
    tx[b, best_n, gj, gi] = gx - gx.floor() # 根據(jù)真實(shí)框所在位置，得到其相當(dāng)于網(wǎng)絡(luò)的位置
    ty[b, best_n, gj, gi] = gy - gy.floor()
    # Width and height
    tw[b, best_n, gj, gi] = torch.log(gw / anchors[best_n][:, 0] + 1e-16)
    th[b, best_n, gj, gi] = torch.log(gh / anchors[best_n][:, 1] + 1e-16)
    # One-hot encoding of label
    tcls[b, best_n, gj, gi, target_labels] = 1 #將真實(shí)框的標(biāo)簽轉(zhuǎn)換為one-hot編碼形式
    # Compute label correctness and iou at best anchor 計(jì)算預(yù)測的和真實(shí)一樣的索引
    class_mask[b, best_n, gj, gi] = (pred_cls[b, best_n, gj, gi].argmax(-1) == target_labels).float()
    iou_scores[b, best_n, gj, gi] = bbox_iou(pred_boxes[b, best_n, gj, gi], target_boxes, x1y1x2y2=False) #與真實(shí)框想匹配的預(yù)測框之間的iou值

    tconf = obj_mask.float() # 真實(shí)框的置信度，也就是1
    return iou_scores, class_mask, obj_mask, noobj_mask, tx, ty, tw, th, tcls, tconf

4. test/detect

detect.py

from __future__ import division

from models import *
from utils.utils import *
from utils.datasets import *

import os
import sys
import time
import datetime
import argparse

from PIL import Image

import torch
from torch.utils.data import DataLoader
from torchvision import datasets
from torch.autograd import Variable

import matplotlib.pyplot as plt
import matplotlib.patches as patches
from matplotlib.ticker import NullLocator

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--image_folder", type=str, default=r"..\data\samples", help="path to dataset")
    parser.add_argument("--model_def", type=str, default=r"..\config\yolov3.cfg",
                        help="path to model definition file")# 網(wǎng)絡(luò)結(jié)構(gòu)定義
    parser.add_argument("--weights_path", type=str, default=r"..\weights\yolov3.weights",
                        help="path to weights file") # 網(wǎng)絡(luò)權(quán)重加載
    parser.add_argument("--class_path", type=str, default=r"..\data\coco.names",
                        help="path to class label file") # classes name
    parser.add_argument("--conf_thres", type=float, default=0.8, help="object confidence threshold") # 置信度閾值
    parser.add_argument("--nms_thres", type=float, default=0.4, help="iou threshold for non-maximum suppression")
    parser.add_argument("--batch_size", type=int, default=1, help="size of the batches")
    parser.add_argument("--n_cpu", type=int, default=0, help="number of cpu threads to use during batch generation")
    parser.add_argument("--img_size", type=int, default=416, help="size of each image dimension")
    parser.add_argument("--checkpoint_model", type=str, help="path to checkpoint model")
    opt = parser.parse_args()
    print(opt)

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    os.makedirs("output", exist_ok=True)

    # Set up model
    model = Darknet(opt.model_def, img_size=opt.img_size).to(device)

    if opt.weights_path.endswith(".weights"):
        # Load darknet weights
        model.load_darknet_weights(opt.weights_path)
    else:
        # Load checkpoint weights
        model.load_state_dict(torch.load(opt.weights_path))

    model.eval()  # Set in evaluation mode

    dataloader = DataLoader(
        ImageFolder(opt.image_folder, img_size=opt.img_size),
        batch_size=opt.batch_size,
        shuffle=False,
        num_workers=opt.n_cpu,
    )

    classes = load_classes(opt.class_path)  # Extracts class labels from file

    Tensor = torch.cuda.FloatTensor if torch.cuda.is_available() else torch.FloatTensor

    imgs = []  # Stores image paths
    img_detections = []  # Stores detections for each image index

    print("\nPerforming object detection:")
    prev_time = time.time()
    for batch_i, (img_paths, input_imgs) in enumerate(dataloader):
        # Configure input
        input_imgs = Variable(input_imgs.type(Tensor))

        # Get detections
        with torch.no_grad():
            detections = model(input_imgs)
            detections = non_max_suppression(detections, opt.conf_thres, opt.nms_thres)

        # Log progress
        current_time = time.time()
        inference_time = datetime.timedelta(seconds=current_time - prev_time)
        prev_time = current_time
        print("\t+ Batch %d, Inference Time: %s" % (batch_i, inference_time))

        # Save image and detections
        imgs.extend(img_paths)
        img_detections.extend(detections)

    # Bounding-box colors
    cmap = plt.get_cmap("tab20b")
    colors = [cmap(i) for i in np.linspace(0, 1, 20)]

    print("\nSaving images:")
    # Iterate through images and save plot of detections
    for img_i, (path, detections) in enumerate(zip(imgs, img_detections)):

        print("(%d) Image: '%s'" % (img_i, path))

        # Create plot
        img = np.array(Image.open(path))
        plt.figure()
        fig, ax = plt.subplots(1)
        ax.imshow(img)

        # Draw bounding boxes and labels of detections
        if detections is not None:
            # Rescale boxes to original image
            detections = rescale_boxes(detections, opt.img_size, img.shape[:2])
            unique_labels = detections[:, -1].cpu().unique()
            n_cls_preds = len(unique_labels)
            bbox_colors = random.sample(colors, n_cls_preds)
            for x1, y1, x2, y2, conf, cls_conf, cls_pred in detections:
                print("\t+ Label: %s, Conf: %.5f" % (classes[int(cls_pred)], cls_conf.item()))

                box_w = x2 - x1
                box_h = y2 - y1

                color = bbox_colors[int(np.where(unique_labels == int(cls_pred))[0])]
                # Create a Rectangle patch
                bbox = patches.Rectangle((x1, y1), box_w, box_h, linewidth=2, edgecolor=color, facecolor="none")
                # Add the bbox to the plot
                ax.add_patch(bbox)
                # Add label
                plt.text(
                    x1,
                    y1,
                    s=classes[int(cls_pred)],
                    color="white",
                    verticalalignment="top",
                    bbox={"color": color, "pad": 0},
                )

        # Save generated image with detections
        plt.axis("off")
        plt.gca().xaxis.set_major_locator(NullLocator())
        plt.gca().yaxis.set_major_locator(NullLocator())
        filename = path.split("\\")[-1].split(".")[0]
        plt.savefig(rf"..\output\samples\{filename}.png", bbox_inches="tight", pad_inches=0.0)
        plt.close()

5. detect全過程

• 加載圖片，將圖片padding成正方形后作為模型的input
• 由淺及深將三個yolo層得到的特征cat到一起【（1,507,85）+（1,2028,85）+ （1,8112,85）】 = 【（1，10647，85）】
• model預(yù)測得到的10647個框進(jìn)入非極大值抑制去除小于閾值的框
• 把最終的框框保存，現(xiàn)在的框坐標(biāo)是相對于正方形的，要將其還原成原本的圖片尺寸下的坐標(biāo)進(jìn)行可視化
  我的測試結(jié)果：
  

今天先這些，去學(xué)新的啦，886文章來源地址http://www.zghlxwxcb.cn/news/detail-514748.html

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