2021-5-25-今日收获

上午：

照常八点起床
上午满课：操作系统、数据挖掘

下午：

写yolov4的主干特征网络：

class Resblock_body(nn.Module):def __init__(self, in_channels, out_channels):super(Resblock_body, self).__init__()self.out_channels = out_channelsself.conv1 = BasicConv(in_channels, out_channels, 3)self.conv2 = BasicConv(out_channels//2, out_channels//2, 3)self.conv3 = BasicConv(out_channels//2, out_channels//2, 3)self.conv4 = BasicConv(out_channels, out_channels, 1)self.maxpool = nn.MaxPool2d([2,2],[2,2])def forward(self, x):# 利用一个3x3卷积进行特征整合x = self.conv1(x)# 引出一个大的残差边routeroute = xc = self.out_channels# 对特征层的通道进行分割，取第二部分作为主干部分。x = torch.split(x, c//2, dim = 1)[1]# 对主干部分进行3x3卷积x = self.conv2(x)# 引出一个小的残差边route_1route1 = x# 对第主干部分进行3x3卷积x = self.conv3(x)# 主干部分与残差部分进行相接x = torch.cat([x,route1], dim = 1) # 对相接后的结果进行1x1卷积x = self.conv4(x)feat = xx = torch.cat([route, x], dim = 1)# 利用最大池化进行高和宽的压缩x = self.maxpool(x)return x,featclass CSPDarkNet(nn.Module):def __init__(self):super(CSPDarkNet, self).__init__()# 首先利用两次步长为2x2的3x3卷积进行高和宽的压缩# 416,416,3 -> 208,208,32 -> 104,104,64self.conv1 = BasicConv(3, 32, kernel_size=3, stride=2)self.conv2 = BasicConv(32, 64, kernel_size=3, stride=2)# 104,104,64 -> 52,52,128self.resblock_body1 =  Resblock_body(64, 64)# 52,52,128 -> 26,26,256self.resblock_body2 =  Resblock_body(128, 128)# 26,26,256 -> 13,13,512self.resblock_body3 =  Resblock_body(256, 256)# 13,13,512 -> 13,13,512self.conv3 = BasicConv(512, 512, kernel_size=3)self.num_features = 1# 进行权值初始化for m in self.modules():if isinstance(m, nn.Conv2d):n = m.kernel_size[0] * m.kernel_size[1] * m.out_channelsm.weight.data.normal_(0, math.sqrt(2. / n))elif isinstance(m, nn.BatchNorm2d):m.weight.data.fill_(1)m.bias.data.zero_()def forward(self, x):# 416,416,3 -> 208,208,32 -> 104,104,64x = self.conv1(x)x = self.conv2(x)# 104,104,64 -> 52,52,128x, _    = self.resblock_body1(x)# 52,52,128 -> 26,26,256x, _    = self.resblock_body2(x)# 26,26,256 -> x为13,13,512#           -> feat1为26,26,256x, feat1    = self.resblock_body3(x)# 13,13,512 -> 13,13,512x = self.conv3(x)feat2 = xreturn feat1,feat2def darknet53_tiny(pretrained, **kwargs):model = CSPDarkNet()if pretrained:if isinstance(pretrained, str):model.load_state_dict(torch.load(pretrained))else:raise Exception("darknet request a pretrained path. got [{}]".format(pretrained))return model

train.py

3. #-------------------------------------#
#       对数据集进行训练
#-------------------------------------#
import os
import numpy as np
import time
import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
from torch.utils.data import DataLoader
from utils.dataloader import yolo_dataset_collate, YoloDataset
from nets.yolo_training import YOLOLoss,Generator
from nets.yolo4_tiny import YoloBody
from tqdm import tqdm
from PIL import Imagedef get_lr(optimizer):for param_group in optimizer.param_groups:return param_group['lr']#---------------------------------------------------#
#   获得类和先验框
#---------------------------------------------------#
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_namesdef 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(',')]return np.array(anchors).reshape([-1,3,2])def fit_one_epoch(net,yolo_losses,epoch,epoch_size,epoch_size_val,gen,genval,Epoch,cuda):total_loss = 0val_loss = 0net.train()with tqdm(total=epoch_size,desc=f'Epoch {epoch + 1}/{Epoch}',postfix=dict,mininterval=0.3) as pbar:for iteration, batch in enumerate(gen):if iteration >= epoch_size:breakimages, targets = batch[0], batch[1]with torch.no_grad():#if cuda:images = Variable(torch.from_numpy(images).type(torch.FloatTensor)).cuda()targets = [Variable(torch.from_numpy(ann).type(torch.FloatTensor)) for ann in targets]#else:#images = Variable(torch.from_numpy(images).type(torch.FloatTensor))#targets = [Variable(torch.from_numpy(ann).type(torch.FloatTensor)) for ann in targets]#----------------------##   清零梯度#----------------------#optimizer.zero_grad()#----------------------##   前向传播#----------------------#outputs = net(images)losses = []num_pos_all = 0#----------------------##   计算损失#----------------------#for i in range(2):loss_item, num_pos = yolo_losses[i](outputs[i], targets)losses.append(loss_item)num_pos_all += num_posloss = sum(losses) / num_pos_all#----------------------##   反向传播#----------------------#loss.backward()optimizer.step()total_loss += loss.item()pbar.set_postfix(**{'total_loss': total_loss / (iteration + 1), 'lr'        : get_lr(optimizer)})pbar.update(1)net.eval()print('Start Validation')with tqdm(total=epoch_size_val, desc=f'Epoch {epoch + 1}/{Epoch}',postfix=dict,mininterval=0.3) as pbar:for iteration, batch in enumerate(genval):if iteration >= epoch_size_val:breakimages_val, targets_val = batch[0], batch[1]with torch.no_grad():if cuda:images_val = Variable(torch.from_numpy(images_val).type(torch.FloatTensor)).cuda()targets_val = [Variable(torch.from_numpy(ann).type(torch.FloatTensor)) for ann in targets_val]else:images_val = Variable(torch.from_numpy(images_val).type(torch.FloatTensor))targets_val = [Variable(torch.from_numpy(ann).type(torch.FloatTensor)) for ann in targets_val]optimizer.zero_grad()outputs = net(images_val)losses = []num_pos_all = 0for i in range(2):loss_item, num_pos = yolo_losses[i](outputs[i], targets_val)losses.append(loss_item)num_pos_all += num_posloss = sum(losses) / num_pos_allval_loss += loss.item()pbar.set_postfix(**{'total_loss': val_loss / (iteration + 1)})pbar.update(1)print('Finish Validation')print('Epoch:'+ str(epoch+1) + '/' + str(Epoch))print('Total Loss: %.4f || Val Loss: %.4f ' % (total_loss/(epoch_size+1),val_loss/(epoch_size_val+1)))print('Saving state, iter:', str(epoch+1))torch.save(model.state_dict(), 'logs/Epoch%d-Total_Loss%.4f-Val_Loss%.4f.pth'%((epoch+1),total_loss/(epoch_size+1),val_loss/(epoch_size_val+1)))if __name__ == "__main__":cuda = TrueUse_Data_Loader = Truenormalize = Falseinput_shape = (416,416)anchors_path = 'model_data/yolo_anchors.txt'classes_path = 'model_data/new_classes1.txt'   #----------------------------------------------------##   获取classes和anchor#----------------------------------------------------#class_names = get_classes(classes_path)anchors = get_anchors(anchors_path)num_classes = len(class_names)mosaic = FalseCosine_lr = Falsesmoooth_label = 0#------------------------------------------------------##   创建yolo模型#   训练前一定要修改classes_path和对应的txt文件#------------------------------------------------------#model = YoloBody(len(anchors[0]), num_classes)#------------------------------------------------------##   权值文件#------------------------------------------------------#model_path = "model_data/yolov4_tiny_weights_coco.pth"# 加快模型训练的效率print('Loading weights into state dict...')device = torch.device('cuda')model_dict = model.state_dict()pretrained_dict = torch.load(model_path, map_location=device)pretrained_dict = {k: v for k, v in pretrained_dict.items() if np.shape(model_dict[k]) ==  np.shape(v)}model_dict.update(pretrained_dict)model.load_state_dict(model_dict)print('Finished!')net = model.train()if cuda:net = torch.nn.DataParallel(model)cudnn.benchmark = Truenet = net.cuda()# 建立loss函数yolo_losses = []for i in range(2):yolo_losses.append(YOLOLoss(np.reshape(anchors,[-1,2]),num_classes, \(input_shape[1], input_shape[0]), smoooth_label, cuda, normalize))#----------------------------------------------------##   获得图片路径和标签#----------------------------------------------------#annotation_path = '2007_train.txt'val_split = 0.0with 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)num_train = len(lines) - num_valif True:lr = 1e-3Batch_size = 32Init_Epoch = 0Freeze_Epoch = 50#----------------------------------------------------------------------------##   我在实际测试时，发现optimizer的weight_decay起到了反作用，#   所以去除掉了weight_decay，大家也可以开起来试试，一般是weight_decay=5e-4#----------------------------------------------------------------------------#optimizer = optim.Adam(net.parameters(),lr)if Cosine_lr:lr_scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=5, eta_min=1e-5)else:lr_scheduler = optim.lr_scheduler.StepLR(optimizer,step_size=1,gamma=0.92)if Use_Data_Loader:train_dataset = YoloDataset(lines[:num_train], (input_shape[0], input_shape[1]), mosaic=mosaic, is_train=True)val_dataset = YoloDataset(lines[num_train:], (input_shape[0], input_shape[1]), mosaic=False, is_train=False)gen = DataLoader(train_dataset, shuffle=False, batch_size=Batch_size, num_workers=2, pin_memory=True,drop_last=True, collate_fn=yolo_dataset_collate)gen_val = DataLoader(val_dataset, shuffle=False, batch_size=Batch_size, num_workers=2,pin_memory=True, drop_last=True, collate_fn=yolo_dataset_collate)else:gen = Generator(Batch_size, lines[:num_train],(input_shape[0], input_shape[1])).generate(train=True, mosaic = mosaic)gen_val = Generator(Batch_size, lines[num_train:],(input_shape[0], input_shape[1])).generate(train=False, mosaic = mosaic)epoch_size = max(1, num_train//Batch_size)epoch_size_val = num_val//Batch_size#------------------------------------##   冻结一定部分训练#------------------------------------#for param in model.backbone.parameters():param.requires_grad = Falsefor epoch in range(Init_Epoch,Freeze_Epoch):fit_one_epoch(net,yolo_losses,epoch,epoch_size,epoch_size_val,gen,gen_val,Freeze_Epoch,cuda)lr_scheduler.step()if True:lr = 1e-4Batch_size = 16Freeze_Epoch = 50Unfreeze_Epoch = 100#----------------------------------------------------------------------------##   我在实际测试时，发现optimizer的weight_decay起到了反作用，#   所以去除掉了weight_decay，大家也可以开起来试试，一般是weight_decay=5e-4#----------------------------------------------------------------------------#optimizer = optim.Adam(net.parameters(),lr)if Cosine_lr:lr_scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=5, eta_min=1e-5)else:lr_scheduler = optim.lr_scheduler.StepLR(optimizer,step_size=1,gamma=0.92)if Use_Data_Loader:train_dataset = YoloDataset(lines[:num_train], (input_shape[0], input_shape[1]), mosaic=mosaic, is_train=True)val_dataset = YoloDataset(lines[num_train:], (input_shape[0], input_shape[1]), mosaic=False, is_train=False)gen = DataLoader(train_dataset, shuffle=False, batch_size=Batch_size, num_workers=2, pin_memory=True,drop_last=True, collate_fn=yolo_dataset_collate)gen_val = DataLoader(val_dataset, shuffle=False, batch_size=Batch_size, num_workers=2,pin_memory=True, drop_last=True, collate_fn=yolo_dataset_collate)else:gen = Generator(Batch_size, lines[:num_train],(input_shape[0], input_shape[1])).generate(train=True, mosaic = mosaic)gen_val = Generator(Batch_size, lines[num_train:],(input_shape[0], input_shape[1])).generate(train=False, mosaic = mosaic)epoch_size = max(1, num_train//Batch_size)epoch_size_val = num_val//Batch_sizefor param in model.backbone.parameters():param.requires_grad = Truefor epoch in range(Freeze_Epoch,Unfreeze_Epoch):fit_one_epoch(net,yolo_losses,epoch,epoch_size,epoch_size_val,gen,gen_val,Unfreeze_Epoch,cuda)lr_scheduler.step()

调整参数
这个就不放了，因为要比赛，提前放出来对团队不尊重。

晚上：

软工大作业的demo写完了
查看yolov4代码，看不懂的搜索
开会、确定下次的团队内容，我已经把表打出来了

自我控制：

克制住调侃学弟的冲动，因为我感觉那样不好，像挖苦一样
说话越发有礼貌，向大佬请教问题，他还挺愿意教我的
另一个大佬帮我搭建了VPN，接下来就看我了！

总结：

这两天直播很好，注意力很集中的了，然后现在明确明天的学习内容：

看高学长给的学习内容
潘老师给的pytorch手机离线运行课题研究

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2021-5-25-今日收获

上午：

下午：

晚上：

自我控制：

总结：

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