1.训练源码地址

2.代码解析地址

""" Retrain the YOLO model for your own dataset. """ import numpy as np import keras.backend as K from keras.layers import Input, Lambda from keras.models import Model from keras.optimizers import Adam from keras.callbacks import TensorBoard, ModelCheckpoint, ReduceLROnPlateau, EarlyStopping from yolo3.model import preprocess_true_boxes, yolo_body, tiny_yolo_body, yolo_loss from yolo3.utils import get_random_data def _main(): annotation_path = '2028_train.txt' log_dir = 'logs/000/' classes_path = 'model_data/voc_classes.txt' anchors_path = 'model_data/yolo_anchors.txt' class_names = get_classes(classes_path) num_classes = len(class_names) anchors = get_anchors(anchors_path) input_shape = (416,416) # multiple of 32, hw is_tiny_version = len(anchors)==6 # default setting if is_tiny_version: model = create_tiny_model(input_shape, anchors, num_classes, freeze_body=2, weights_path='model_data/tiny_yolo_weights.h5') else: model = create_model(input_shape, anchors, num_classes, freeze_body=2, weights_path='model_data/yolo_weights.h5') # make sure you know what you freeze # freeze_body=2, weights_path='model_data/yolo_weights.h5') logging = TensorBoard(log_dir=log_dir) # 1. # filename：字符串，保存模型的路径 # 2. # monitor：需要监视的值，val_acc或这val_loss # 3. # verbose：信息展示模式，0 # 为不打印输出信息，1 # 打印 # 4. # save_best_only：当设置为True时，将只保存在验证集上性能最好的模型 # 5. # mode：‘auto’，‘min’，‘max’之一，在save_best_only = True时决定性能最佳模型的评判准则，例如，当监测值为val_acc时，模式应为max，当检测值为val_loss时，模式应为min。在auto模式下，评价准则由被监测值的名字自动推断。 # 6. # save_weights_only：若设置为True，则只保存模型权重，否则将保存整个模型（包括模型结构，配置信息等） # 7. # period：CheckPoint之间的间隔的epoch数 checkpoint = ModelCheckpoint(log_dir + 'ep{epoch:03d}-loss{loss:.3f}-val_loss{val_loss:.3f}.h5', monitor='val_loss', save_weights_only=True, save_best_only=True, period=3) reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=3, verbose=1) early_stopping = EarlyStopping(monitor='val_loss', min_delta=0, patience=10, verbose=1) val_split = 0.1 #训练和验证的比例 with open(annotation_path) as f: lines = f.readlines() np.random.seed(10101) np.random.shuffle(lines) np.random.seed(None) num_val = int(len(lines)*val_split) #验证集数量 num_train = len(lines) - num_val #训练集数量 # Train with frozen layers first, to get a stable loss. # Adjust num epochs to your dataset. This step is enough to obtain a not bad model. ''' 第1阶段，冻结部分网络，只训练底层权重。 优化器使用最常见的Adam。 损失函数，直接使用模型的输出y_pred，忽略真值y_true 其中，关于损失函数yolo_loss，以及y_true和y_pred： 把y_true当成输入，作为模型的多输入，把loss封装为层（即Lambda层），作为模型的输出； 在模型中，最终输出的y_pred就是loss； 在编译（compile）时，将loss设置为y_pred即可，无视y_true； 在训练时，随意添加一个符合结构的y_true即可。 ''' if True: model.compile(optimizer=Adam(lr=1e-3), loss={ # use custom yolo_loss Lambda layer.使用定制的 yolo_loss Lambda层 'yolo_loss': lambda y_true, y_pred: y_pred}) #损失函数 # 模型fit数据，使用数据生成包装器（data_generator_wrapper），按批次生成训练和验证数据。最终，模型model存储权重。实现如下： batch_size = 4 print('Train on {} samples, val on {} samples, with batch size {}.'.format(num_train, num_val, batch_size)) model.fit_generator(data_generator_wrapper(lines[:num_train], batch_size, input_shape, anchors, num_classes), steps_per_epoch=max(1, num_train//batch_size), validation_data=data_generator_wrapper(lines[num_train:], batch_size, input_shape, anchors, num_classes), validation_steps=max(1, num_val//batch_size), epochs=50, initial_epoch=0, callbacks=[logging, checkpoint]) # 存储最终的去权重，再训练过程中，也通过回调存储 model.save_weights(log_dir + 'trained_weights_stage_1.h5') # Unfreeze and continue training, to fine-tune. # Train longer if the result is not good. if True: for i in range(len(model.layers)): model.layers[i].trainable = True model.compile(optimizer=Adam(lr=1e-4), loss={'yolo_loss': lambda y_true, y_pred: y_pred}) # recompile to apply the change print('Unfreeze all of the layers.') #需要调小点 32的batch size 1080ti 32G带不动 ''' 第2阶段的模型fit数据，与第1阶段类似，从第50个epoch开始，一直训练到第100个epoch，当触发条件时，则提前终止。额外增加了两个回调reduce_lr和early_stopping，用于控制训练提取终止的时机： reduce_lr：当评价指标不在提升时，减少学习率，每次减少10%（factor），当验证损失值（val_loss），持续3次未减少（patience）时，则终止训练。 early_stopping：当验证集损失值，连续增加小于0（min_delta）时，持续10个epoch（patience），则终止训练。 ''' batch_size = 32 # note that more GPU memory is required after unfreezing the body print('Train on {} samples, val on {} samples, with batch size {}.'.format(num_train, num_val, batch_size)) model.fit_generator(data_generator_wrapper(lines[:num_train], batch_size, input_shape, anchors, num_classes), steps_per_epoch=max(1, num_train//batch_size), validation_data=data_generator_wrapper(lines[num_train:], batch_size, input_shape, anchors, num_classes), validation_steps=max(1, num_val//batch_size), epochs=100, initial_epoch=50, callbacks=[logging, checkpoint, reduce_lr, early_stopping]) model.save_weights(log_dir + 'trained_weights_final.h5') # Further training if needed. def get_classes(classes_path): '''loads the classes''' with open(classes_path) as f: class_names = f.readlines() class_names = [c.strip() for c in class_names] return class_names def get_anchors(anchors_path): '''loads the anchors from a file''' with open(anchors_path) as f: anchors = f.readline() anchors = [float(x) for x in anchors.split(',')] # print(np.array(anchors).reshape(-1, 2)) ''' [[ 10. 13.] [ 16. 30.] [ 33. 23.] [ 30. 61.] [ 62. 45.] [ 59. 119.] [116. 90.] [156. 198.] [373. 326.]] ''' return np.array(anchors).reshape(-1, 2) #freeze_body 1是冻结DarkNet53的层 2是冻结全部，只保留最后3层 #网络最后的3层： #3个1x1的卷积层（代替全连接层），用于将3个尺度的特征图，转化为3个尺度的预测值 #weights_path 预训练权重文件 def create_model(input_shape, anchors, num_classes, load_pretrained=True, freeze_body=2, weights_path='model_data/yolo_weights.h5'): '''create the training model''' K.clear_session() # get a new session image_input = Input(shape=(None, None, 3)) h, w = input_shape num_anchors = len(anchors) y_true = [Input(shape=(h//{0:32, 1:16, 2:8}[l], w//{0:32, 1:16, 2:8}[l], \ num_anchors//3, num_classes+5)) for l in range(3)] model_body = yolo_body(image_input, num_anchors//3, num_classes) print('Create YOLOv3 model with {} anchors and {} classes.'.format(num_anchors, num_classes)) if load_pretrained: model_body.load_weights(weights_path, by_name=True, skip_mismatch=True) print('Load weights {}.'.format(weights_path)) if freeze_body in [1, 2]: # Freeze darknet53 body or freeze all but 3 output layers. num = (185, len(model_body.layers)-3)[freeze_body-1] for i in range(num): model_body.layers[i].trainable = False print('Freeze the first {} layers of total {} layers.'.format(num, len(model_body.layers))) model_loss = Lambda(yolo_loss, output_shape=(1,), name='yolo_loss', arguments={'anchors': anchors, 'num_classes': num_classes, 'ignore_thresh': 0.5})( [*model_body.output, *y_true]) model = Model([model_body.input, *y_true], model_loss) return model def create_tiny_model(input_shape, anchors, num_classes, load_pretrained=True, freeze_body=2, weights_path='model_data/tiny_yolo_weights.h5'): '''create the training model, for Tiny YOLOv3''' K.clear_session() # get a new session image_input = Input(shape=(None, None, 3)) h, w = input_shape num_anchors = len(anchors) y_true = [Input(shape=(h//{0:32, 1:16}[l], w//{0:32, 1:16}[l], \ num_anchors//2, num_classes+5)) for l in range(2)] model_body = tiny_yolo_body(image_input, num_anchors//2, num_classes) print('Create Tiny YOLOv3 model with {} anchors and {} classes.'.format(num_anchors, num_classes)) if load_pretrained: model_body.load_weights(weights_path, by_name=True, skip_mismatch=True) print('Load weights {}.'.format(weights_path)) if freeze_body in [1, 2]: # Freeze the darknet body or freeze all but 2 output layers. num = (20, len(model_body.layers)-2)[freeze_body-1] for i in range(num): model_body.layers[i].trainable = False print('Freeze the first {} layers of total {} layers.'.format(num, len(model_body.layers))) model_loss = Lambda(yolo_loss, output_shape=(1,), name='yolo_loss', arguments={'anchors': anchors, 'num_classes': num_classes, 'ignore_thresh': 0.7})( [*model_body.output, *y_true]) model = Model([model_body.input, *y_true], model_loss) return model def data_generator(annotation_lines, batch_size, input_shape, anchors, num_classes): '''data generator for fit_generator''' n = len(annotation_lines) i = 0 while True: image_data = [] box_data = [] for b in range(batch_size): if i==0: np.random.shuffle(annotation_lines) #随机抽取 shuffle-洗牌 image, box = get_random_data(annotation_lines[i], input_shape, random=True) image_data.append(image) box_data.append(box) i = (i+1) % n image_data = np.array(image_data) box_data = np.array(box_data) y_true = preprocess_true_boxes(box_data, input_shape, anchors, num_classes) yield [image_data, *y_true], np.zeros(batch_size) def data_generator_wrapper(annotation_lines, batch_size, input_shape, anchors, num_classes): n = len(annotation_lines) if n==0 or batch_size<=0: return None return data_generator(annotation_lines, batch_size, input_shape, anchors, num_classes) if __name__ == '__main__': _main()