faster-rcnn/utils/callbacks.py

import os

import matplotlib
import torch

matplotlib.use('Agg')
from matplotlib import pyplot as plt
import scipy.signal

import shutil
import numpy as np
from PIL import Image
from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdm

from .utils import cvtColor, resize_image, preprocess_input, get_new_img_size
from .utils_bbox import DecodeBox
from .utils_map import get_coco_map, get_map

class LossHistory():
    def __init__(self, log_dir, model, input_shape):
        self.log_dir    = log_dir
        self.losses     = []
        self.val_loss   = []
        
        os.makedirs(self.log_dir)
        self.writer     = SummaryWriter(self.log_dir)
        # try:
        #     dummy_input     = torch.randn(2, 3, input_shape[0], input_shape[1])
        #     self.writer.add_graph(model, dummy_input)
        # except:
        #     pass

    def append_loss(self, epoch, loss, val_loss):
        if not os.path.exists(self.log_dir):
            os.makedirs(self.log_dir)

        self.losses.append(loss)
        self.val_loss.append(val_loss)

        with open(os.path.join(self.log_dir, "epoch_loss.txt"), 'a') as f:
            f.write(str(loss))
            f.write("\n")
        with open(os.path.join(self.log_dir, "epoch_val_loss.txt"), 'a') as f:
            f.write(str(val_loss))
            f.write("\n")

        self.writer.add_scalar('loss', loss, epoch)
        self.writer.add_scalar('val_loss', val_loss, epoch)
        self.loss_plot()

    def loss_plot(self):
        iters = range(len(self.losses))

        plt.figure()
        plt.plot(iters, self.losses, 'red', linewidth = 2, label='train loss')
        plt.plot(iters, self.val_loss, 'coral', linewidth = 2, label='val loss')
        try:
            if len(self.losses) < 25:
                num = 5
            else:
                num = 15
            
            plt.plot(iters, scipy.signal.savgol_filter(self.losses, num, 3), 'green', linestyle = '--', linewidth = 2, label='smooth train loss')
            plt.plot(iters, scipy.signal.savgol_filter(self.val_loss, num, 3), '#8B4513', linestyle = '--', linewidth = 2, label='smooth val loss')
        except:
            pass

        plt.grid(True)
        plt.xlabel('Epoch')
        plt.ylabel('Loss')
        plt.legend(loc="upper right")

        plt.savefig(os.path.join(self.log_dir, "epoch_loss.png"))

        plt.cla()
        plt.close("all")

class EvalCallback():
    def __init__(self, net, input_shape, class_names, num_classes, val_lines, log_dir, cuda, \
            map_out_path=".temp_map_out", max_boxes=100, confidence=0.05, nms_iou=0.5, letterbox_image=True, MINOVERLAP=0.5, eval_flag=True, period=1):
        super(EvalCallback, self).__init__()
        
        self.net                = net
        self.input_shape        = input_shape
        self.class_names        = class_names
        self.num_classes        = num_classes
        self.val_lines          = val_lines
        self.log_dir            = log_dir
        self.cuda               = cuda
        self.map_out_path       = map_out_path
        self.max_boxes          = max_boxes
        self.confidence         = confidence
        self.nms_iou            = nms_iou
        self.letterbox_image    = letterbox_image
        self.MINOVERLAP         = MINOVERLAP
        self.eval_flag          = eval_flag
        self.period             = period
        
        self.std    = torch.Tensor([0.1, 0.1, 0.2, 0.2]).repeat(self.num_classes + 1)[None]
        if self.cuda:
            self.std    = self.std.cuda()
        self.bbox_util  = DecodeBox(self.std, self.num_classes)

        self.maps       = [0]
        self.epoches    = [0]
        if self.eval_flag:
            with open(os.path.join(self.log_dir, "epoch_map.txt"), 'a') as f:
                f.write(str(0))
                f.write("\n")

    #---------------------------------------------------#
    #   检测图片
    #---------------------------------------------------#
    def get_map_txt(self, image_id, image, class_names, map_out_path):
        f = open(os.path.join(map_out_path, "detection-results/"+image_id+".txt"),"w")
        #---------------------------------------------------#
        #   计算输入图片的高和宽
        #---------------------------------------------------#
        image_shape = np.array(np.shape(image)[0:2])
        input_shape = get_new_img_size(image_shape[0], image_shape[1])
        #---------------------------------------------------------#
        #   在这里将图像转换成RGB图像，防止灰度图在预测时报错。
        #   代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB
        #---------------------------------------------------------#
        image       = cvtColor(image)
        
        #---------------------------------------------------------#
        #   给原图像进行resize，resize到短边为600的大小上
        #---------------------------------------------------------#
        image_data  = resize_image(image, [input_shape[1], input_shape[0]])
        #---------------------------------------------------------#
        #   添加上batch_size维度
        #---------------------------------------------------------#
        image_data  = np.expand_dims(np.transpose(preprocess_input(np.array(image_data, dtype='float32')), (2, 0, 1)), 0)

        with torch.no_grad():
            images = torch.from_numpy(image_data)
            if self.cuda:
                images = images.cuda()

            roi_cls_locs, roi_scores, rois, _ = self.net(images)
            #-------------------------------------------------------------#
            #   利用classifier的预测结果对建议框进行解码，获得预测框
            #-------------------------------------------------------------#
            results = self.bbox_util.forward(roi_cls_locs, roi_scores, rois, image_shape, input_shape, 
                                                    nms_iou = self.nms_iou, confidence = self.confidence)
            #--------------------------------------#
            #   如果没有检测到物体，则返回原图
            #--------------------------------------#
            if len(results[0]) <= 0:
                return 

            top_label   = np.array(results[0][:, 5], dtype = 'int32')
            top_conf    = results[0][:, 4]
            top_boxes   = results[0][:, :4]

        top_100     = np.argsort(top_conf)[::-1][:self.max_boxes]
        top_boxes   = top_boxes[top_100]
        top_conf    = top_conf[top_100]
        top_label   = top_label[top_100]

        for i, c in list(enumerate(top_label)):
            predicted_class = self.class_names[int(c)]
            box             = top_boxes[i]
            score           = str(top_conf[i])

            top, left, bottom, right = box
            if predicted_class not in class_names:
                continue

            f.write("%s %s %s %s %s %s\n" % (predicted_class, score[:6], str(int(left)), str(int(top)), str(int(right)),str(int(bottom))))

        f.close()
        return 
    
    def on_epoch_end(self, epoch):
        if epoch % self.period == 0 and self.eval_flag:
            if not os.path.exists(self.map_out_path):
                os.makedirs(self.map_out_path)
            if not os.path.exists(os.path.join(self.map_out_path, "ground-truth")):
                os.makedirs(os.path.join(self.map_out_path, "ground-truth"))
            if not os.path.exists(os.path.join(self.map_out_path, "detection-results")):
                os.makedirs(os.path.join(self.map_out_path, "detection-results"))
            print("Get map.")
            for annotation_line in tqdm(self.val_lines):
                line        = annotation_line.split()
                image_id    = os.path.basename(line[0]).split('.')[0]
                #------------------------------#
                #   读取图像并转换成RGB图像
                #------------------------------#
                image       = Image.open(line[0])
                #------------------------------#
                #   获得预测框
                #------------------------------#
                gt_boxes    = np.array([np.array(list(map(int,box.split(',')))) for box in line[1:]])
                #------------------------------#
                #   获得预测txt
                #------------------------------#
                self.get_map_txt(image_id, image, self.class_names, self.map_out_path)
                
                #------------------------------#
                #   获得真实框txt
                #------------------------------#
                with open(os.path.join(self.map_out_path, "ground-truth/"+image_id+".txt"), "w") as new_f:
                    for box in gt_boxes:
                        left, top, right, bottom, obj = box
                        obj_name = self.class_names[obj]
                        new_f.write("%s %s %s %s %s\n" % (obj_name, left, top, right, bottom))
                        
            print("Calculate Map.")
            try:
                temp_map = get_coco_map(class_names = self.class_names, path = self.map_out_path)[1]
            except:
                temp_map = get_map(self.MINOVERLAP, False, path = self.map_out_path)
            self.maps.append(temp_map)
            self.epoches.append(epoch)

            with open(os.path.join(self.log_dir, "epoch_map.txt"), 'a') as f:
                f.write(str(temp_map))
                f.write("\n")
            
            plt.figure()
            plt.plot(self.epoches, self.maps, 'red', linewidth = 2, label='train map')

            plt.grid(True)
            plt.xlabel('Epoch')
            plt.ylabel('Map %s'%str(self.MINOVERLAP))
            plt.title('A Map Curve')
            plt.legend(loc="upper right")

            plt.savefig(os.path.join(self.log_dir, "epoch_map.png"))
            plt.cla()
            plt.close("all")

            print("Get map done.")
            shutil.rmtree(self.map_out_path)