# -------------------------------------# # 对数据集进行训练 # -------------------------------------# import datetime import os import random import numpy as np import torch import torch.backends.cudnn as cudnn import torch.distributed as dist import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader from data.dataloader_for_IRSTD_UAV import seqDataset, dataset_collate from model.TDCNet.TDCNetwork import TDCNetwork from model.nets.yolo_training import (ModelEMA, YOLOLoss, get_lr_scheduler, set_optimizer_lr, weights_init) from utils.callbacks import EvalCallback, LossHistory from utils.utils import get_classes, show_config from utils.utils_fit import fit_one_epoch if __name__ == "__main__": os.environ["CUDA_VISIBLE_DEVICES"] = "0" # ---------------------------------# # num_frame 输入帧数 # ---------------------------------# num_frame = 5 # ---------------------------------# # Cuda 是否使用Cuda # 没有GPU可以设置成False # ---------------------------------# Cuda = True # ---------------------------------------------------------------------# # distributed 用于指定是否使用单机多卡分布式运行 # 终端指令仅支持Ubuntu。CUDA_VISIBLE_DEVICES用于在Ubuntu下指定显卡。 # Windows系统下默认使用DP模式调用所有显卡，不支持DDP。 # DP模式： # 设置 distributed = False # 在终端中输入 CUDA_VISIBLE_DEVICES=0,1 python train.py # DDP模式： # 设置 distributed = True # 在终端中输入 CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 train.py # ---------------------------------------------------------------------# distributed = False # ---------------------------------------------------------------------# # sync_bn 是否使用sync_bn，DDP模式多卡可用 # ---------------------------------------------------------------------# sync_bn = False # ---------------------------------------------------------------------# # fp16 是否使用混合精度训练 # 可减少约一半的显存、需要pytorch1.7.1以上 # ---------------------------------------------------------------------# fp16 = False # ---------------------------------------------------------------------# # classes_path 指向model_data下的txt，与自己训练的数据集相关 # 训练前一定要修改classes_path，使其对应自己的数据集 # ---------------------------------------------------------------------# classes_path = 'model_data/classes.txt' model_path = '' input_shape = [640, 640] # ----------------------------------------------------------------------------------------------------------------------------# # 训练分为两个阶段，分别是冻结阶段和解冻阶段。设置冻结阶段是为了满足机器性能不足的同学的训练需求。 # 冻结训练需要的显存较小，显卡非常差的情况下，可设置Freeze_Epoch等于UnFreeze_Epoch，Freeze_Train = True，此时仅仅进行冻结训练。 # # 在此提供若干参数设置建议，各位训练者根据自己的需求进行灵活调整： # （一）从整个模型的预训练权重开始训练： # Adam： # Init_Epoch = 0，Freeze_Epoch = 50，UnFreeze_Epoch = 100，Freeze_Train = True，optimizer_type = 'adam'，Init_lr = 1e-3，weight_decay = 0。（冻结） # Init_Epoch = 0，UnFreeze_Epoch = 100，Freeze_Train = False，optimizer_type = 'adam'，Init_lr = 1e-3，weight_decay = 0。（不冻结） # SGD： # Init_Epoch = 0，Freeze_Epoch = 50，UnFreeze_Epoch = 300，Freeze_Train = True，optimizer_type = 'sgd'，Init_lr = 1e-2，weight_decay = 5e-4。（冻结） # Init_Epoch = 0，UnFreeze_Epoch = 300，Freeze_Train = False，optimizer_type = 'sgd'，Init_lr = 1e-2，weight_decay = 5e-4。（不冻结） # 其中：UnFreeze_Epoch可以在100-300之间调整。 # （二）从0开始训练： # Init_Epoch = 0，UnFreeze_Epoch >= 300，Unfreeze_batch_size >= 16，Freeze_Train = False（不冻结训练） # 其中：UnFreeze_Epoch尽量不小于300。optimizer_type = 'sgd'，Init_lr = 1e-2，mosaic = True。 # （三）batch_size的设置： # 在显卡能够接受的范围内，以大为好。显存不足与数据集大小无关，提示显存不足（OOM或者CUDA out of memory）请调小batch_size。 # 受到BatchNorm层影响，batch_size最小为2，不能为1。 # 正常情况下Freeze_batch_size建议为Unfreeze_batch_size的1-2倍。不建议设置的差距过大，因为关系到学习率的自动调整。 # ----------------------------------------------------------------------------------------------------------------------------# # ------------------------------------------------------------------# # 冻结阶段训练参数 # 此时模型的主干被冻结了，特征提取网络不发生改变 # 占用的显存较小，仅对网络进行微调 # Init_Epoch 模型当前开始的训练世代，其值可以大于Freeze_Epoch，如设置： # Init_Epoch = 60、Freeze_Epoch = 50、UnFreeze_Epoch = 100 # 会跳过冻结阶段，直接从60代开始，并调整对应的学习率。 # （断点续练时使用） # Freeze_Epoch 模型冻结训练的Freeze_Epoch # (当Freeze_Train=False时失效) # Freeze_batch_size 模型冻结训练的batch_size # (当Freeze_Train=False时失效) # ------------------------------------------------------------------# Init_Epoch = 0 Freeze_Epoch = 100 Freeze_batch_size = 4 # ------------------------------------------------------------------# # 解冻阶段训练参数 # 此时模型的主干不被冻结了，特征提取网络会发生改变 # 占用的显存较大，网络所有的参数都会发生改变 # UnFreeze_Epoch 模型总共训练的epoch # SGD需要更长的时间收敛，因此设置较大的UnFreeze_Epoch # Adam可以使用相对较小的UnFreeze_Epoch # Unfreeze_batch_size 模型在解冻后的batch_size # ------------------------------------------------------------------# UnFreeze_Epoch = 100 Unfreeze_batch_size = 4 # ------------------------------------------------------------------# # Freeze_Train 是否进行冻结训练 # 默认先冻结主干训练后解冻训练。 # ------------------------------------------------------------------# Freeze_Train = False # ------------------------------------------------------------------# # 其它训练参数：学习率、优化器、学习率下降有关 # ------------------------------------------------------------------# # ------------------------------------------------------------------# # Init_lr 模型的最大学习率 # Min_lr 模型的最小学习率，默认为最大学习率的0.01 # ------------------------------------------------------------------# Init_lr = 1e-3 Min_lr = Init_lr * 0.01 # ------------------------------------------------------------------# # optimizer_type 使用到的优化器种类，可选的有adam、sgd # 当使用Adam优化器时建议设置 Init_lr=1e-3 # 当使用SGD优化器时建议设置 Init_lr=1e-2 # momentum 优化器内部使用到的momentum参数 # weight_decay 权值衰减，可防止过拟合 # adam会导致weight_decay错误，使用adam时建议设置为0。 # ------------------------------------------------------------------# optimizer_type = "adam" momentum = 0.937 weight_decay = 1e-4 # ------------------------------------------------------------------# # lr_decay_type 使用到的学习率下降方式，可选的有step、cos # ------------------------------------------------------------------# lr_decay_type = "cos" # ------------------------------------------------------------------# # save_period 多少个epoch保存一次权值 # ------------------------------------------------------------------# save_period = 10 # ------------------------------------------------------------------# # save_dir 权值与日志文件保存的文件夹 # ------------------------------------------------------------------# save_dir = f'logs/TDCNet_epoch_{UnFreeze_Epoch}_batch_{Unfreeze_batch_size}_optim_{optimizer_type}_lr_{Init_lr}_T_{num_frame}' # ------------------------------------------------------------------# # eval_flag 是否在训练时进行评估，评估对象为验证集 # 安装pycocotools库后，评估体验更佳。 # eval_period 代表多少个epoch评估一次，不建议频繁的评估 # 评估需要消耗较多的时间，频繁评估会导致训练非常慢 # 此处获得的mAP会与get_map.py获得的会有所不同，原因有二： # （一）此处获得的mAP为验证集的mAP。 # （二）此处设置评估参数较为保守，目的是加快评估速度。 # ------------------------------------------------------------------# eval_flag = True eval_period = 200 # ------------------------------------------------------------------# # num_workers 用于设置是否使用多线程读取数据 # 开启后会加快数据读取速度，但是会占用更多内存 # 内存较小的电脑可以设置为2或者0 # ------------------------------------------------------------------# num_workers = 8 # ----------------------------------------------------# # 获得图片路径和标签 # ----------------------------------------------------# DATA_PATH = "/Dataset/IRSTD-UAV/" train_annotation_path = "/Dataset/IRSTD-UAV/train.txt" val_annotation_path = "/Dataset/IRSTD-UAV/val.txt" # ------------------------------------------------------# # 设置用到的显卡 # ------------------------------------------------------# ngpus_per_node = torch.cuda.device_count() if distributed: dist.init_process_group(backend="nccl") local_rank = int(os.environ["LOCAL_RANK"]) rank = int(os.environ["RANK"]) device = torch.device("cuda", local_rank) if local_rank == 0: print(f"[{os.getpid()}] (rank = {rank}, local_rank = {local_rank}) training...") print("Gpu Device Count : ", ngpus_per_node) else: device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') local_rank = 0 rank = 0 # ------------------------------------------------------# # 设置随机种子 # ------------------------------------------------------# seed = 42 torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) np.random.seed(seed) random.seed(seed) torch.backends.cudnn.deterministic = True class_names, num_classes = get_classes(classes_path) model = TDCNetwork(num_classes=1, num_frame=num_frame) weights_init(model) if model_path != '': if local_rank == 0: print('Load weights {}.'.format(model_path)) # ------------------------------------------------------# # 根据预训练权重的Key和模型的Key进行加载 # ------------------------------------------------------# model_dict = model.state_dict() # pdb.set_trace() pretrained_dict = torch.load(model_path, map_location=device) load_key, no_load_key, temp_dict = [], [], {} for k, v in pretrained_dict.items(): if k in model_dict.keys() and np.shape(model_dict[k]) == np.shape(v): temp_dict[k] = v load_key.append(k) else: no_load_key.append(k) model_dict.update(temp_dict) model.load_state_dict(model_dict) # ------------------------------------------------------# # 显示没有匹配上的Key # ------------------------------------------------------# if local_rank == 0: print("\nSuccessful Load Key:", str(load_key)[:500], "……\nSuccessful Load Key Num:", len(load_key)) print("\nFail To Load Key:", str(no_load_key)[:500], "……\nFail To Load Key num:", len(no_load_key)) print("\n\033[1;33;44m温馨提示，head部分没有载入是正常现象，Backbone部分没有载入是错误的。\033[0m") # ----------------------# # 获得损失函数 # ----------------------# yolo_loss = YOLOLoss(num_classes, fp16, strides=[8]) # ----------------------# # 记录Loss # ----------------------# if local_rank == 0: time_str = datetime.datetime.strftime(datetime.datetime.now(), '%Y_%m_%d_%H_%M_%S') log_dir = os.path.join(save_dir, "loss_" + str(time_str)) loss_history = LossHistory(log_dir, model, input_shape=input_shape) # pdb.set_trace() else: loss_history = None # ------------------------------------------------------------------# # torch 1.2不支持amp，建议使用torch 1.7.1及以上正确使用fp16 # 因此torch1.2这里显示"could not be resolve" # ------------------------------------------------------------------# if fp16: from torch.cuda.amp import GradScaler as GradScaler scaler = GradScaler() else: scaler = None model_train = model.train() # ----------------------------# # 多卡同步Bn # ----------------------------# if sync_bn and ngpus_per_node > 1 and distributed: model_train = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model_train) elif sync_bn: print("Sync_bn is not support in one gpu or not distributed.") if Cuda: if distributed: # ----------------------------# # 多卡平行运行 # ----------------------------# model_train = model_train.cuda(local_rank) model_train = torch.nn.parallel.DistributedDataParallel(model_train, device_ids=[local_rank], find_unused_parameters=True) else: model_train = torch.nn.DataParallel(model) cudnn.benchmark = True model_train = model_train.cuda() # ----------------------------# # 权值平滑 # ----------------------------# # pdb.set_trace() ema = ModelEMA(model_train) # ---------------------------# # 读取数据集对应的txt # ---------------------------# with open(train_annotation_path, encoding='utf-8') as f: train_lines = f.readlines() with open(val_annotation_path, encoding='utf-8') as f: val_lines = f.readlines() num_train = len(train_lines) num_val = len(val_lines) if local_rank == 0: show_config( classes_path=classes_path, model_path=model_path, input_shape=input_shape, \ Init_Epoch=Init_Epoch, Freeze_Epoch=Freeze_Epoch, UnFreeze_Epoch=UnFreeze_Epoch, Freeze_batch_size=Freeze_batch_size, Unfreeze_batch_size=Unfreeze_batch_size, Freeze_Train=Freeze_Train, \ Init_lr=Init_lr, Min_lr=Min_lr, optimizer_type=optimizer_type, momentum=momentum, lr_decay_type=lr_decay_type, \ save_period=save_period, save_dir=log_dir, num_workers=num_workers, num_train=num_train, num_val=num_val ) # ---------------------------------------------------------# # 总训练世代指的是遍历全部数据的总次数 # 总训练步长指的是梯度下降的总次数 # 每个训练世代包含若干训练步长，每个训练步长进行一次梯度下降。 # 此处仅建议最低训练世代，上不封顶，计算时只考虑了解冻部分 # ----------------------------------------------------------# wanted_step = 5e4 if optimizer_type == "sgd" else 1.5e4 total_step = num_train // Unfreeze_batch_size * UnFreeze_Epoch if total_step <= wanted_step: if num_train // Unfreeze_batch_size == 0: raise ValueError('数据集过小，无法进行训练，请扩充数据集。') wanted_epoch = wanted_step // (num_train // Unfreeze_batch_size) + 1 print("\n\033[1;33;44m[Warning] 使用%s优化器时，建议将训练总步长设置到%d以上。\033[0m" % (optimizer_type, wanted_step)) print("\033[1;33;44m[Warning] 本次运行的总训练数据量为%d，Unfreeze_batch_size为%d，共训练%d个Epoch，计算出总训练步长为%d。\033[0m" % (num_train, Unfreeze_batch_size, UnFreeze_Epoch, total_step)) print("\033[1;33;44m[Warning] 由于总训练步长为%d，小于建议总步长%d，建议设置总世代为%d。\033[0m" % (total_step, wanted_step, wanted_epoch)) # ------------------------------------------------------# # 主干特征提取网络特征通用，冻结训练可以加快训练速度 # 也可以在训练初期防止权值被破坏。 # Init_Epoch为起始世代 # Freeze_Epoch为冻结训练的世代 # UnFreeze_Epoch总训练世代 # 提示OOM或者显存不足请调小Batch_size # ------------------------------------------------------# if True: UnFreeze_flag = False # ------------------------------------# # 冻结一定部分训练 # ------------------------------------# if Freeze_Train: for param in model.backbone.parameters(): param.requires_grad = False for param in model.backbone_3d.parameters(): param.requires_grad = False # -------------------------------------------------------------------# # 如果不冻结训练的话，直接设置batch_size为Unfreeze_batch_size # -------------------------------------------------------------------# batch_size = Freeze_batch_size if Freeze_Train else Unfreeze_batch_size # -------------------------------------------------------------------# # 判断当前batch_size，自适应调整学习率 # -------------------------------------------------------------------# nbs = 64 lr_limit_max = 1e-3 if optimizer_type == 'adam' else 5e-2 lr_limit_min = 1e-5 if optimizer_type == 'adam' else 5e-4 Init_lr_fit = min(max(batch_size / nbs * Init_lr, lr_limit_min), lr_limit_max) Min_lr_fit = min(max(batch_size / nbs * Min_lr, lr_limit_min * 1e-2), lr_limit_max * 1e-2) # ---------------------------------------# # 根据optimizer_type选择优化器 # ---------------------------------------# pg0, pg1, pg2 = [], [], [] for k, v in model.named_modules(): if hasattr(v, "bias") and isinstance(v.bias, nn.Parameter): pg2.append(v.bias) if isinstance(v, nn.BatchNorm2d) or "bn" in k: pg0.append(v.weight) elif hasattr(v, "weight") and isinstance(v.weight, nn.Parameter): pg1.append(v.weight) optimizer = { 'adam': optim.Adam(pg0, Init_lr_fit, betas=(momentum, 0.999)), 'sgd': optim.SGD(pg0, Init_lr_fit, momentum=momentum, nesterov=True) }[optimizer_type] optimizer.add_param_group({"params": pg1, "weight_decay": weight_decay}) optimizer.add_param_group({"params": pg2}) # ---------------------------------------# # 获得学习率下降的公式 # ---------------------------------------# lr_scheduler_func = get_lr_scheduler(lr_decay_type, Init_lr_fit, Min_lr_fit, UnFreeze_Epoch) # ---------------------------------------# # 判断每一个世代的长度 # ---------------------------------------# epoch_step = num_train // batch_size epoch_step_val = num_val // batch_size if epoch_step == 0 or epoch_step_val == 0: raise ValueError("数据集过小，无法继续进行训练，请扩充数据集。") if ema: ema.updates = epoch_step * Init_Epoch train_dataset = seqDataset(train_annotation_path, input_shape[0], num_frame, 'train') # 5 val_dataset = seqDataset(val_annotation_path, input_shape[0], num_frame, 'val') # 5 if distributed: train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset, shuffle=True, ) val_sampler = torch.utils.data.distributed.DistributedSampler(val_dataset, shuffle=False, ) batch_size = batch_size // ngpus_per_node shuffle = False else: train_sampler = None val_sampler = None shuffle = True gen = DataLoader(train_dataset, shuffle=shuffle, batch_size=batch_size, num_workers=num_workers, pin_memory=True, drop_last=True, collate_fn=dataset_collate, sampler=train_sampler) gen_val = DataLoader(val_dataset, shuffle=shuffle, batch_size=batch_size, num_workers=num_workers, pin_memory=True, drop_last=True, collate_fn=dataset_collate, sampler=val_sampler) # ----------------------# # 记录eval的map曲线 # ----------------------# if local_rank == 0: eval_callback = EvalCallback(model, input_shape, class_names, num_classes, val_lines, log_dir, Cuda, \ eval_flag=eval_flag, period=eval_period) else: eval_callback = None # ---------------------------------------# # 开始模型训练 # ---------------------------------------# for epoch in range(Init_Epoch, UnFreeze_Epoch): # ---------------------------------------# # 如果模型有冻结学习部分 # 则解冻，并设置参数 # ---------------------------------------# if epoch >= Freeze_Epoch and not UnFreeze_flag and Freeze_Train: batch_size = Unfreeze_batch_size # -------------------------------------------------------------------# # 判断当前batch_size，自适应调整学习率 # -------------------------------------------------------------------# nbs = 64 lr_limit_max = 1e-3 if optimizer_type == 'adam' else 5e-2 lr_limit_min = 1e-5 if optimizer_type == 'adam' else 5e-4 Init_lr_fit = min(max(batch_size / nbs * Init_lr, lr_limit_min), lr_limit_max) Min_lr_fit = min(max(batch_size / nbs * Min_lr, lr_limit_min * 1e-2), lr_limit_max * 1e-2) # ---------------------------------------# # 获得学习率下降的公式 # ---------------------------------------# lr_scheduler_func = get_lr_scheduler(lr_decay_type, Init_lr_fit, Min_lr_fit, UnFreeze_Epoch) for param in model.backbone.parameters(): param.requires_grad = True for param in model.backbone_3d.parameters(): param.requires_grad = True epoch_step = num_train // batch_size epoch_step_val = num_val // batch_size if epoch_step == 0 or epoch_step_val == 0: raise ValueError("数据集过小，无法继续进行训练，请扩充数据集。") if distributed: batch_size = batch_size // ngpus_per_node if ema: ema.updates = epoch_step * epoch gen = DataLoader(train_dataset, shuffle=shuffle, batch_size=batch_size, num_workers=num_workers, pin_memory=True, drop_last=True, collate_fn=dataset_collate, sampler=train_sampler) gen_val = DataLoader(val_dataset, shuffle=shuffle, batch_size=batch_size, num_workers=num_workers, pin_memory=True, drop_last=True, collate_fn=dataset_collate, sampler=val_sampler) UnFreeze_flag = True gen.dataset.epoch_now = epoch gen_val.dataset.epoch_now = epoch if distributed: train_sampler.set_epoch(epoch) set_optimizer_lr(optimizer, lr_scheduler_func, epoch) # pdb.set_trace() fit_one_epoch(model_train, model, ema, yolo_loss, loss_history, eval_callback, optimizer, epoch, epoch_step, epoch_step_val, gen, gen_val, UnFreeze_Epoch, Cuda, fp16, scaler, save_period, log_dir, local_rank) if distributed: dist.barrier() if local_rank == 0: loss_history.writer.close()