pytorch分类和回归：阿里天池宠物年龄预测
dog年龄预测
阿里天池宠物年龄预测
https://tianchi.aliyun.com/competition/
实验了多种方法，最终成绩并不是特别好，比赛结束后如果有更好的思路，欢迎指教。
论文Deep expectation of real and apparent age from a single image without facial landmarks
直接回归分段分类分段求概率，求加权期望，其实相当于回归。三种方法的表现
其他方法：
拟合分布，而不是one-hot排序的方式数据均衡性参考：
https://github.com/NICE-FUTURE/predict-gender-and-age-from-camera/tree/master
分类的损失函数
1.多分类交叉熵损失函数：torch.nn.CrossEntropyLoss() = log_softmax + nll_loss
详细介绍：https://zhuanlan.zhihu.com/p/159477597
2.KLDiv Loss： 分布差异描述分布的差异，如果分类的目标不是one-hot而是soft-label的时候可以用
https://zhuanlan.zhihu.com/p/340088331
3.facenet 三元组损失函数https://github.com/kvsnoufal/Pytorch-FaceNet-DogDataset
timm and torchvision
https://datawhalechina.github.io/thorough-pytorch/index.html
https://datawhalechina.github.io/thorough-pytorch/%E7%AC%AC%E5%85%AD%E7%AB%A0/6.3%20%E6%A8%A1%E5%9E%8B%E5%BE%AE%E8%B0%83-timm.html
import torchvision.models as models resnet18 = models.resnet18() # resnet18 = models.resnet18(pretrained=False) 等价于与上面的表达式 alexnet = models.alexnet() vgg16 = models.vgg16() squeezenet = models.squeezenet1_0() densenet = models.densenet161() inception = models.inception_v3() googlenet = models.googlenet() shufflenet = models.shufflenet_v2_x1_0() mobilenet_v2 = models.mobilenet_v2() mobilenet_v3_large = models.mobilenet_v3_large() mobilenet_v3_small = models.mobilenet_v3_small() resnext50_32x4d = models.resnext50_32x4d() wide_resnet50_2 = models.wide_resnet50_2() mnasnet = models.mnasnet1_0() 12345678910111213141516
尝试一：分类模型，按年龄分为1-191个类别
主要参考：dog品种分类
利用交叉熵损失，然后当成一个分类模型来训练
import glob import os import cv2 import numpy as np import torch from torch import nn, optim from torchvision import datasets,transforms from torch.utils.data import DataLoader import torch import torchvision.models as models from PIL import Image import torchvision.transforms as transforms from tqdm import tqdm from dog_age2 import Net def VGG16_predict(img_path): ''' Use pre-trained VGG-16 model to obtain index corresponding to predicted ImageNet class for image at specified path Args: img_path: path to an image Returns: Index corresponding to VGG-16 model's prediction ''' # define VGG16 model VGG16 = models.vgg16(pretrained=True) # check if CUDA is available use_cuda = torch.cuda.is_available() # move model to GPU if CUDA is available if use_cuda: VGG16 = VGG16.cuda() ## Load and pre-process an image from the given img_path ## Return the *index* of the predicted class for that image # Image Resize to 256 image = Image.open(img_path) mean = [0.485, 0.456, 0.406] std = [0.229, 0.224, 0.225] image_transforms = transforms.Compose([transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize(mean, std)]) image_tensor = image_transforms(image) image_tensor.unsqueeze_(0) if use_cuda: image_tensor = image_tensor.cuda() output = VGG16(image_tensor) _, classes = torch.max(output, dim=1) return classes.item() # predicted class index ### returns "True" if a dog is detected in the image stored at img_path def dog_detector(img_path): ## TODO: Complete the function. class_dog=VGG16_predict(img_path) return class_dog >= 151 and class_dog <=268 # true/false def resnet50_predict(img_path): resnet50 = models.resnet50(pretrained=True) use_cuda = torch.cuda.is_available() if use_cuda: resnet50.cuda() image = Image.open(img_path) mean=[0.485, 0.456, 0.406] std=[0.229, 0.224, 0.225] image_transforms = transforms.Compose([transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize(mean,std)]) image_tensor = image_transforms(image) image_tensor.unsqueeze_(0) if use_cuda: image_tensor=image_tensor.cuda() resnet50.eval() output = resnet50(image_tensor) _,classes = torch.max(output,dim=1) return classes.item() def resnet50_dog_detector(image_path): class_idx = resnet50_predict(image_path) return class_idx >= 151 and class_idx <=268 def get_train_set_info(dir): dog_files_train = glob.glob(dir + '*.jpg') mean = np.array([0.,0.,0.]) std = np.array([0.,0.,0.]) for i in tqdm(range(len(dog_files_train))): image=cv2.imread(dog_files_train[i]) image = cv2.cvtColor(image,cv2.COLOR_BGR2RGB) image = image/255.0 mean[0] += np.mean(image[:,:,0]) mean[1] += np.mean(image[:,:,1]) mean[2] += np.mean(image[:,:,2]) std[0] += np.std(image[:,:,0]) std[1] += np.std(image[:,:,1]) std[2] += np.std(image[:,:,2]) mean = mean/len(dog_files_train) std = std/len(dog_files_train) return mean,std from PIL import ImageFile def train(n_epochs, loaders, model, optimizer, criterion, use_cuda, save_path): """returns trained model""" # initialize tracker for minimum validation loss valid_loss_min = np.Inf for epoch in range(1, n_epochs + 1): # initialize variables to monitor training and validation loss train_loss = 0.0 valid_loss = 0.0 ################### # train the model # ################### model.train() for batch_idx, (data, target) in enumerate(tqdm(loaders['train'])): # move to GPU if use_cuda: data, target = data.cuda(), target.cuda() ## find the loss and update the model parameters accordingly ## record the average training loss, using something like ## train_loss = train_loss + ((1 / (batch_idx + 1)) * (loss.data - train_loss)) optimizer.zero_grad() output = model(data) loss = criterion(output, target) loss.backward() optimizer.step() train_loss = train_loss + ((1 / (batch_idx + 1)) * (loss.data - train_loss)) ###################### # validate the model # ###################### correct = 0. correct2 = 0 correct3 = 0 correct4 = 0 total = 0. model.eval() for batch_idx, (data, target) in enumerate(tqdm(loaders['valid'])): # move to GPU if use_cuda: data, target = data.cuda(), target.cuda() ## update the average validation loss output = model(data) loss = criterion(output, target) valid_loss = valid_loss + ((1 / (batch_idx + 1)) * (loss.data - valid_loss)) pred = output.data.max(1, keepdim=True)[1] # compare predictions to true label correct += np.sum(np.squeeze(pred.eq(target.data.view_as(pred))).cpu().numpy()) correct2 += np.sum(np.squeeze(np.abs(pred.cpu().numpy() - (target.data.view_as(pred).cpu().numpy())) < 5)) correct3 += np.sum(np.squeeze(np.abs(pred.cpu().numpy() - (target.data.view_as(pred).cpu().numpy())) < 10)) correct4 += np.sum(np.squeeze(np.abs(pred.cpu().numpy() - (target.data.view_as(pred).cpu().numpy())))) total += data.size(0) # print training/validation statistics print('Epoch: {} tTraining Loss: {:.6f} tValidation Loss: {:.6f}'.format( epoch, train_loss, valid_loss )) print('Test Accuracy: %2d%% (%2d/%2d)' % ( 100. * correct / total, correct, total)) print('Test Accuracy: %2d%% (%2d/%2d)' % ( 100. * correct2 / total, correct2, total)) print('Test Accuracy: %2d%% (%2d/%2d)' % ( 100. * correct3 / total, correct3, total)) print('Test Accuracy: %2d' % ( correct4 / total)) ## TODO: save the model if validation loss has decreased if valid_loss_min > valid_loss: print('Saving Model...') valid_loss_min = valid_loss torch.save(model.state_dict(), save_path) # return trained model return model if __name__ == "__main__": # 1. vgg16 和 resnet50 的识别能力 dir = r'D:committrainsettrainset' # dog_files = glob.glob(dir + '*.jpg') # # dog_files_short = dog_files[:100] # # dog_percentage_dog = 0 # dog_percentage_dog2 = 0 # for i in tqdm(range(100)): # dog_percentage_dog += int(dog_detector(dog_files_short[i])) # dog_percentage_dog2 += int(resnet50_dog_detector(dog_files_short[i])) # # print(' Dog Percentage in Dog Dataset:{}% {} %'.format( dog_percentage_dog, dog_percentage_dog2)) # 98%, 97% # 2. 训练数据的均值和方差 # mean, std = get_train_set_info(dir) # print(mean, std) # [0.595504 0.54956806 0.51172713] [0.2101685 0.21753638 0.22078435] # 3. 训练 mean_train_set = [0.595504, 0.54956806, 0.51172713] std_train_set = [0.2101685, 0.21753638, 0.22078435] train_dir = r'D:committrainsettrainset2' valid_dir = r'D:commitvalsetvalset2' test_dir = r'D:commitvalsetvalset2' train_transforms = transforms.Compose([transforms.Resize([256, 256]), transforms.ColorJitter(brightness=0.5, contrast=0.2, saturation=0.2, hue=0.1), transforms.RandomHorizontalFlip(p=0.5), transforms.ToTensor(), transforms.Normalize(mean_train_set, std_train_set)]) valid_test_transforms = transforms.Compose([transforms.Resize([256, 256]), #transforms.CenterCrop(256), transforms.ToTensor(), transforms.Normalize(mean_train_set, std_train_set)]) train_dataset = datasets.ImageFolder(train_dir, transform=train_transforms) valid_dataset = datasets.ImageFolder(valid_dir, transform=valid_test_transforms) #test_dataset = datasets.ImageFolder(test_dir, transform=valid_test_transforms) # num_workers=8, pin_memory=True 很重要，训练速度明显 trainloader = DataLoader(train_dataset, batch_size=32, shuffle=True, num_workers=16, pin_memory=True) validloader = DataLoader(valid_dataset, batch_size=32, shuffle=False,num_workers=8, pin_memory=True) #testloader = DataLoader(test_dataset, batch_size=32, shuffle=False) loaders_scratch = {} loaders_scratch['train'] = trainloader loaders_scratch['valid'] = validloader #loaders_scratch['test'] = testloader use_cuda = torch.cuda.is_available() # instantiate the CNN num_class = 191 # model_scratch = Net(num_class) model_scratch = models.resnet50(pretrained=True) for param in model_scratch.parameters(): param.requires_grad = True # model_scratch.classifier = nn.Sequential(nn.Linear(1024, 512), # nn.ReLU(), # nn.Dropout(0.2), # nn.Linear(512, 133)) # # model_scratch.load_state_dict(torch.load('model_transfer.pt', map_location='cuda:0')) model_scratch.classifier = nn.Sequential(nn.Linear(1024, 512), nn.ReLU(), nn.Dropout(0.2), nn.Linear(512, num_class)) # move tensors to GPU if CUDA is available if use_cuda: model_scratch.cuda() criterion_scratch = nn.CrossEntropyLoss() optimizer_scratch = optim.Adam(model_scratch.parameters(), lr=0.0005) print('training !') # epoch ImageFile.LOAD_TRUNCATED_IMAGES = True model_scratch = train(100, loaders_scratch, model_scratch, optimizer_scratch, criterion_scratch, use_cuda, 'model_scratch2.pt') # load the model that got the best validation accuracy # model_scratch.load_state_dict(torch.load('model_scratch.pt')) 123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181182183184185186187188189190191192193194195196197198199200201202203204205206207208209210211212213214215216217218219220221222223224225226227228229230231232233234235236237238239240241242243244245246247248249250251252253254255256257258259260261262263264265266
效果不好。
尝试二：回归模型
model.py
import torch import torch.nn as nn from torchinfo import summary import timm class base_net(nn.Module): def __init__(self, input_features, num_features=64): super().__init__() self.num_features = num_features self.conv = nn.Sequential( nn.Conv2d(input_features, num_features, kernel_size=3, padding=3//2), #nn.BatchNorm2d(num_features), nn.ReLU(inplace=True), nn.Conv2d(num_features, num_features*2, kernel_size=3, padding=3//2), #nn.BatchNorm2d(num_features*2), nn.ReLU(inplace=True), nn.Conv2d(num_features*2, num_features, kernel_size=3, padding=3 // 2), #nn.BatchNorm2d(num_features), nn.ReLU(inplace=True), nn.Conv2d(num_features, num_features, kernel_size=3, padding=3 // 2), #nn.BatchNorm2d(num_features), nn.ReLU(inplace=True), nn.Conv2d(num_features, num_features, kernel_size=3, padding=3//2), ) def forward(self, x): x = self.conv(x) return x class Predictor(nn.Module): """ The header to predict age (regression branch) """ def __init__(self, num_features, num_classes=1): super().__init__() self.conv = nn.Sequential( nn.Conv2d(num_features, num_features // 4, kernel_size=3, padding=3 // 2), nn.BatchNorm2d(num_features // 4), nn.ReLU(inplace=True), nn.Dropout(0.5), nn.Conv2d(num_features // 4, num_features // 8, kernel_size=3, padding=3 // 2), nn.BatchNorm2d(num_features // 8), nn.ReLU(inplace=True), nn.Dropout(0.5), nn.Conv2d(num_features // 8, num_features // 16, kernel_size=3, padding=3 // 2), ) self.gap = nn.AdaptiveAvgPool2d(1) self.fc = nn.Conv2d(num_features//16, num_classes, kernel_size=1, bias=True) #self.dp = nn.Dropout(0.5) def forward(self, x): x = self.conv(x) x = self.gap(x) #x = self.dp(x) x = self.fc(x) x = x.squeeze(-1).squeeze(-1).squeeze(-1) return x class Classifier(nn.Module): """ The header to predict gender (classification branch) """ def __init__(self, num_features, num_classes=100): super().__init__() self.conv = nn.Sequential( nn.Conv2d(num_features, num_features // 4, kernel_size=3, padding=3 // 2), nn.BatchNorm2d(num_features // 4), nn.ReLU(inplace=True), nn.Dropout(0.5), nn.Conv2d(num_features // 4, num_features // 8, kernel_size=3, padding=3 // 2), nn.BatchNorm2d(num_features // 8), nn.ReLU(inplace=True), nn.Dropout(0.5), nn.Conv2d(num_features // 8, num_features // 16, kernel_size=3, padding=3 // 2), ) self.gap = nn.AdaptiveAvgPool2d(1) self.fc = nn.Conv2d(num_features//16, num_classes, kernel_size=1, bias=True) self.dp = nn.Dropout(0.4) def forward(self, x): x = self.conv(x) x = self.gap(x) x = self.dp(x) x = self.fc(x) x = x.squeeze(-1).squeeze(-1) # x = nn.functional.softmax(x, dim=1) return x #https://github.com/NICE-FUTURE/predict-gender-and-age-from-camera/tree/master class Model(nn.Module): """ A model to predict age and gender """ def __init__(self, timm_pretrained=True): super().__init__() self.backbone = timm.create_model("resnet18", pretrained=timm_pretrained) self.predictor = Predictor(self.backbone.num_features) # self.classifier = Classifier(self.backbone.num_features) def forward(self, x): x = self.backbone.forward_features(x) # shape: B, D, H, W age = self.predictor(x) #gender = self.classifier(x) return age class Model2(nn.Module): """ A model to predict age and gender """ def __init__(self, timm_pretrained=True): super().__init__() self.backbone = timm.create_model("resnet18", pretrained=timm_pretrained) #base_net(3, 64) # # self.predictor = Predictor(self.backbone.num_features) self.classifier = Classifier(self.backbone.num_features) # 100类概率 def forward(self, x): x = self.backbone.forward_features(x) # shape: B, D, H, W #x = self.backbone.forward(x) # shape: B, D, H, W prob = self.classifier(x) #gender = self.classifier(x) return prob class Model3(nn.Module): """ A model to predict age and gender """ def __init__(self, timm_pretrained=False): super().__init__() self.backbone = base_net(3, 64) # timm.create_model("resnet18", pretrained=timm_pretrained) # # self.predictor = Predictor(self.backbone.num_features) self.classifier = Classifier(self.backbone.num_features) # 100类概率 def forward(self, x): #x = self.backbone.forward_features(x) # shape: B, D, H, W x = self.backbone.forward(x) # shape: B, D, H, W prob = self.classifier(x) #gender = self.classifier(x) return prob if __name__ == "__main__": device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') # device = 'cpu' print(device) modelviz = Model2().to(device) # 打印模型结构 print(modelviz) summary(modelviz, input_size=(2, 3, 256, 256), col_names=["kernel_size", "output_size", "num_params", "mult_adds"]) # for p in modelviz.parameters(): # if p.requires_grad: # print(p.shape) input = torch.rand(2, 3, 256, 256).to(device) out = modelviz(input) from ptflops import get_model_complexity_info macs, params = get_model_complexity_info(modelviz, (3, 256, 256), verbose=True, print_per_layer_stat=True) print(macs, params) params = float(params[:-3]) macs = float(macs[:-4]) print(macs * 2, params) # 8个图像的 FLOPs, 这里的结果 和 其他方法应该一致 print('out:', out.shape, out) 123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177
训练模型：
import glob import os.path import cv2 import numpy as np import rawpy import torch import torch.optim as optim from torch import nn from torch.utils.data import DataLoader from torchvision import transforms from tqdm import tqdm from datasets import BatchDataset from model import Model, Model2 import torchvision if __name__ == "__main__": # 1.当前版本信息 print(torch.__version__) print(torch.version.cuda) print(torch.backends.cudnn.version()) print(torch.cuda.get_device_name(0)) np.random.seed(0) torch.manual_seed(0) torch.cuda.manual_seed_all(0) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False # 2. 设置device信息 和 创建model # os.environ['CUDA_VISIBLE_DEVICES'] = '2,3' # device = torch.device("cuda:2" if torch.cuda.is_available() else "cpu") model = Model() gpus = [2,3] model = nn.DataParallel(model, device_ids=gpus) device = torch.device('cuda:2') model = model.cuda(device=gpus[0]) # 3. dataset 和 data loader, num_workers设置线程数目，pin_memory设置固定内存 img_size = 256 transform1 = transforms.Compose([ transforms.ToTensor(), transforms.Resize([img_size, img_size]), transforms.RandomHorizontalFlip(p=0.5), transforms.ColorJitter(brightness=0.4, contrast=0.2, saturation=0.2, hue=0.1), #transforms.RandomPerspective(distortion_scale=0.6, p=1.0), transforms.RandomRotation(degrees=(-90, 90)), ]) transform2 = transforms.Compose([ transforms.ToTensor(), transforms.Resize([img_size, img_size]), ]) train_dataset = BatchDataset('train', transform1) train_dataset_loader = DataLoader(train_dataset, batch_size=32*4, shuffle=True, num_workers=8, pin_memory=True) eval_dataset = BatchDataset('eval', transform2) eval_dataset_loader = DataLoader(eval_dataset, batch_size=8, shuffle=True, num_workers=8, pin_memory=True) print('load dataset !', len(train_dataset), len(eval_dataset)) # 4. 损失函数 和 优化器 age_criterion = nn.MSELoss() gender_criterion = nn.CrossEntropyLoss().to(device) loss_fn = nn.L1Loss().to(device) loss_fn2 = nn.SmoothL1Loss().to(device) learning_rate = 1 * 1e-4 #optimizer = optim.Adam(model.parameters(), lr=learning_rate) optimizer = optim.SGD(model.parameters(), lr=learning_rate, momentum=0.9) lr_step = 50 scheduler = torch.optim.lr_scheduler.StepLR(optimizer, lr_step, gamma=0.5) # 5. hyper para 设置 epochs = 800 save_epoch = 100 save_model_dir = 'saved_model_age' eval_epoch = 100 save_sample_dir = 'saved_sample_age' if not os.path.exists(save_model_dir): os.makedirs(save_model_dir) # 6. 是否恢复模型 resume = 1 last_epoch = 12 if resume and last_epoch > 1: model.load_state_dict(torch.load( save_model_dir + '/checkpoint_%04d.pth' % (last_epoch), map_location=device)) print('resume ' , save_model_dir + '/checkpoint_%04d.pth' % (last_epoch)) # 7. 训练epoch f1 = open('traininfo1.txt', 'a') f2 = open('evalinfo1.txt', 'a') for epoch in range(last_epoch + 1, epochs + 1): print('current epoch:', epoch, 'current lr:', optimizer.state_dict()['param_groups'][0]['lr']) if epoch < last_epoch + 101: save_epoch = 2 eval_epoch = 2 else: save_epoch = 10 eval_epoch = 10 # 8. train loop model.train() g_loss = [] g_mae = [] for data in tqdm(train_dataset_loader): image, age, filename = data # print(image.shape, age, filename) image = image.to(device) age = age.to(device) pred_age = model(image) #print(image.shape, pred_age.shape) loss = loss_fn(age, pred_age) #loss = age_criterion(age, pred_age) #print('dd:', age.detach().cpu().numpy().reshape(-1), pred_age.detach().cpu().numpy().reshape(-1)) optimizer.zero_grad() loss.backward() optimizer.step() # training result g_loss.append(loss.item()) mae = np.sum(np.abs(age.detach().cpu().numpy().reshape(-1) - pred_age.detach().cpu().numpy().reshape(-1))) / len(age) g_mae.append(mae) #print( loss.item(), mae) #print(len(g_loss), len(g_mae)) mean_loss = np.mean(np.array(g_loss)) mean_mae = np.mean(np.array(g_mae)) print(f'epoch{epoch:04d} ,train loss: {mean_loss},train mae: {mean_mae}') f1.write("%d, %.6f, %.4fn" % (epoch, mean_loss, mean_mae)) # 9. save model if epoch % save_epoch == 0: save_model_path = os.path.join(save_model_dir, f'checkpoint_{epoch:04d}.pth') torch.save(model.state_dict(), save_model_path) # 10. eval test and save some samples if needed if epoch % eval_epoch == 0: model.eval() maes = [] with torch.no_grad(): for data in tqdm(eval_dataset_loader): image, age, filename = data image = image.to(device) age = age.to(device) out = model(image) mae = loss_fn(out, age) #print( age.detach().cpu().numpy().reshape(-1), out.detach().cpu().numpy().reshape(-1), mae.item()) maes.append(mae.item()) print('eval dataset mae: ', np.array(maes).mean()) f2.write("%d, %.6fn" % (epoch, np.array(maes).mean())) scheduler.step() # 更新学习率 123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168
效果还可以，eval mae 可以达到22左右
尝试三：分类模型
1)为了应对数据不均衡，五个年龄何为一组数据，这样每组取50个得到一个epoch的dataset进行训练
2)分类得到的是概率，除了交叉熵损失，再加上一个期望损失
效果不佳
尝试四：KLDivLoss 拟合年龄分布
除了 age的 mae损失
还有 年龄分布损失， 利用KLDivLoss来实现。
比如 label =21 的年龄设置分布为 21周边的年龄不为0，其他为0
prob = model(image) pred_age = torch.sum(prob * torch.arange(0, 100).reshape(1, -1).to(device), axis=1) * 2 + 1 #print(prob.shape, label.shape) loss1 = loss_kl(prob.log(), label) # label是一个分布 loss2 = loss_fn(age, pred_age) loss = loss1 + loss2 / 10 1234567
尝试五：首先提取狗的face,然后再训练模型
由于直接训练很容易过拟合，因此怀疑图片中的其他特征干扰了模型训练，因此提取狗face后进行训练效果会不会好一些，如何提取狗face呢？
主要利用下面的仓库
https://github.com/metinozkan/DogAndCat-Face-Opencv
import glob import os import cv2 files = glob.glob(r'D:committestsettestset' + '*.jpg') for file in files: #file = r'D:committrainsettrainset 2e5218a80b44139ab07c547e1d6c4b9.jpg' img=cv2.imread(file)#picture path height, width, channel = img.shape yuz_cascade=cv2.CascadeClassifier('dog_face.xml')#used haarcascade Classifier #kedi_cascade=cv2.CascadeClassifier('haarcascade_frontalcatface.xml path')#used haarcascade Classifier griton = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)#Picture conversion gray tone with haarcascade it = yuz_cascade.detectMultiScale(griton,1.1,4)#search for the object you want in photos #kedi=kedi_cascade.detectMultiScale(griton,1.1,4) kopeksay=0#increases the number of found objects kedisay=0 #objects in the rectangle wh = 0 i = 0 if len(it) == 0: x, y, w, h = 0,0,width,height print(file, 'not changed ') else: for (x, y, w, h) in it: if w* h > wh: wh = w*h j = i i += 1 (x, y, w, h) = it[j] T = 20 # save img2 = img[ max(y-T, 0): min(y + h+T, height), max(x-T, 0) : min(x + w + T,width)] cv2.imwrite(os.path.join(r'D:committestsettestset3', os.path.basename(file)), img2) # show show_fig = 0 if show_fig: cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 3) cv2.rectangle(img, (max(x-T, 0), max(y-T, 0)), (min(x + w + T,width) , min(y + h+T, height)), (0, 255, 255), 3) kopeksay=kopeksay+1 # for (x, y, w, h) in kedi: # cv2.rectangle(img, (x, y), (x + w,y + h), (0, 10, 0), 3) # kedisay=kedisay+1 print("kopek->",kopeksay)#number of found objects print("kedi-->",kedisay) cv2.imshow('yuzler', img) cv2.waitKey(0) cv2.destroyAllWindows() 123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
最后主要根据二和五进行优化。prediect
import glob import os.path import cv2 import numpy as np import rawpy import torch import torch.optim as optim from PIL import Image from torch import nn from torch.utils.data import DataLoader from torchvision import transforms from tqdm import tqdm from skimage.metrics import peak_signal_noise_ratio as compare_psnr from skimage.metrics import structural_similarity as compare_ssim from datasets import BatchDataset, get_images from model import UNetSeeInDark, Model, Model2 if __name__ == "__main__": img_size = 256 transform2 = transforms.Compose([ transforms.ToTensor(), transforms.Resize([img_size, img_size]), ]) model = Model() gpus = [0] # model = nn.DataParallel(model, device_ids=gpus) device = torch.device('cuda:0') print(device) m_path = 'saved_model_age/checkpoint_0014.pth' #m_path = 'saved_model_res18_reg/checkpoint_0010.pth' checkpoint = torch.load(m_path, map_location=device) model.load_state_dict({k.replace('module.', ''): v for k, v in checkpoint.items()}) #model.load_state_dict(torch.load(m_path, map_location=device)) model = model.cuda(device=gpus[0]) model.eval() files = glob.glob("testsettestset*.jpg") # image_dir = 'valsetvalset' # file_txt = 'annotationsannotationsval.txt' # files = get_images(image_dir, file_txt) print(len(files)) f = open('predict_res50_14.txt', 'w') st = '' ret = [] for file in files: # file, label = file image = Image.open(file).convert('RGB') # image = cv2.imread(file, 1).astype(np.float32) / 255 image = np.array(image) input = transform2(image).unsqueeze(0).to(device) #print(input.shape) out = model(input) out = out.detach().cpu().numpy().reshape(-1) pred_age = out[0] #pred_age = np.sum(out * np.arange(0, 100).reshape(1, -1)) * 2 + 1 #print(int(label), pred_age, np.abs(pred_age -int(label))) #ret.append([int(label), pred_age, pred_age -int(label), np.abs(pred_age -int(label))]) #print(out) st = os.path.basename(file)+'t%.2fn' % (pred_age.item()) f.write(st) # ret = np.array(ret) # print(ret) # print(np.mean(ret, axis=0)) #np.savetxt('ret54.txt', ret+2, fmt='%.1f', delimiter=' ') 123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778
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