import datetime import os import torch import matplotlib matplotlib.use('Agg') import scipy.signal from matplotlib import pyplot as plt from torch.utils.tensorboard import SummaryWriter import shutil import numpy as np from PIL import Image from tqdm import tqdm from .utils import cvtColor, preprocess_input, resize_image 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, anchors, anchors_mask, 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.anchors = anchors self.anchors_mask = anchors_mask 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.bbox_util = DecodeBox(self.anchors, self.num_classes, (self.input_shape[0], self.input_shape[1]), self.anchors_mask) 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") 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", encoding='utf-8') image_shape = np.array(np.shape(image)[0:2]) #---------------------------------------------------------# # 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) #---------------------------------------------------------# # 给图像增加灰条，实现不失真的resize # 也可以直接resize进行识别 #---------------------------------------------------------# image_data = resize_image(image, (self.input_shape[1], self.input_shape[0]), self.letterbox_image) #---------------------------------------------------------# # 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() #---------------------------------------------------------# # 将图像输入网络当中进行预测！ #---------------------------------------------------------# outputs = self.net(images) outputs = self.bbox_util.decode_box(outputs) #---------------------------------------------------------# # 将预测框进行堆叠，然后进行非极大抑制 #---------------------------------------------------------# results = self.bbox_util.non_max_suppression(torch.cat(outputs, 1), self.num_classes, self.input_shape, image_shape, self.letterbox_image, conf_thres = self.confidence, nms_thres = self.nms_iou) if results[0] is None: return top_label = np.array(results[0][:, 6], dtype = 'int32') top_conf = results[0][:, 4] * results[0][:, 5] 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, model_eval): if epoch % self.period == 0 and self.eval_flag: self.net = model_eval 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)