讯飞AI开发者大赛

编程入门行业动态更新时间:2024-10-24 18:21:01

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讯飞AI开发者大赛

各框架Baseline地址

Kears and Pytorch：
Paddle: =1

本项目给出Paddle在组网时应用Paddlehub模型。目前网上均未找到此种方法，所以这里给出本人所采用的一种方法。

本项目基于阿水大佬的代码，采用Paddle实现。

Paddle代码解析

加载数据，处理

%cd /home/aistudio
!unzip ./data/data100388/Datawhale_人脸情绪识别_数据集.zip
%cd Datawhale_人脸情绪识别_数据集
!nohup unzip train.zip
!nohup unzip test.zip

导入模块

import paddle
import paddle.nn as nn
import cv2
from PIL import  Image
import numpy as np
import pandas as pd
import glob
from sklearn.model_selection import  train_test_split, StratifiedKFold, KFold
import time
import paddle.vision.transforms as transforms
import warnings
import paddlehub
# 忽略警告输出
warnings.filterwarnings("ignore")
paddle.set_device('gpu')

设置Dataset，自定义读取数据集

train_jpg = glob.glob('/home/aistudio/Datawhale_人脸情绪识别_数据集/train/*/*')
np.random.shuffle(train_jpg)
train_jpg = np.array(train_jpg)
class QRDataset(paddle.io.Dataset):def __init__(self, img_path, transform=None):self.img_path = img_pathif transform is not None:self.transform = transformelse:self.transform = Nonedef __getitem__(self, index):start_time = time.time()img = Image.open(self.img_path[index]).convert('RGB')lbl_dict = {'angry': 0,'disgusted': 1,'fearful': 2,'happy': 3,'neutral': 4,'sad': 5,'surprised': 6}if self.transform is not None:img = self.transform(img)if 'test' in self.img_path[index]:return img, paddle.to_tensor(np.array(0))else:lbl_int = lbl_dict[self.img_path[index].split('/')[-2]]return img, paddle.to_tensor(np.array(lbl_int))def __len__(self):return len(self.img_path)

模型组网

from EfficientNet_B1 import EfficientNet_B1class XunFeiNet(nn.Layer):def __init__(self):super(XunFeiNet, self).__init__()#efmodel = paddlehub.Module(name = "efficientnetb1_imagenet")self.model = EfficientNet_B1()self.fc = nn.Linear(1000, 7)def forward(self, img):out = self.model(img)out = self.fc(out)return out

对于EfficientNet_B1模块，是在本地建一个Python文件，具体需要的到下列Github路径中寻找：.1/modules/image/classification/efficientnetb1_imagenet/module.py

设置验证、预测、训练代码，利用基础API

def validate(val_loader, model, criterion):model.eval()acc1 = []with paddle.no_grad():end = time.time()for i, (input_, target) in enumerate(val_loader):   output = model(input_)target = paddle.unsqueeze(paddle.to_tensor(target), 1)loss = criterion(output, target)#loss = nn.functional.sigmoid_focal_loss(output, target)acc1.append(paddle.metric.accuracy(output, target))print(' * Val Acc@1 {0}'.format(np.mean(acc1)))return np.mean(acc1)def predict(test_loader, model, tta=10):model.eval()test_pred_tta = Nonefor _ in range(tta):test_pred = []with paddle.no_grad():end = time.time()for i, (input_, target) in enumerate(test_loader):output = model(input_)target = paddle.unsqueeze(paddle.to_tensor(target), 1)output = output.numpy()test_pred.append(output)test_pred = np.vstack(test_pred)if test_pred_tta is None:test_pred_tta = test_predelse:test_pred_tta += test_predreturn test_pred_ttadef train(train_loader, model, criterion, optimizer, epoch):model.train()end = time.time()acc1 = []for i, (input_, target) in enumerate(train_loader):output = model(input_)target = paddle.unsqueeze(paddle.to_tensor(target), 1)loss = criterion(output, target)#loss = nn.functional.sigmoid_focal_loss(output, target)acc1.append(paddle.metric.accuracy(output, target))loss.backward()optimizer.step()optimizer.clear_grad()if i % 100 == 0:print('Train: {0}'.format(np.mean(acc1)))

设置交叉验证训练，此处只用一折

skf = KFold(n_splits=5, random_state=233, shuffle=True)
for flod_idx, (train_idx, val_idx) in enumerate(skf.split(train_jpg, train_jpg)):train_loader = paddle.io.DataLoader(QRDataset(train_jpg[train_idx][:],transforms.Compose([transforms.ColorJitter(hue=.05, saturation=.05),transforms.RandomHorizontalFlip(),transforms.RandomVerticalFlip(),transforms.Resize((196, 196)),transforms.ToTensor(),transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])), batch_size=128, shuffle=True, num_workers=0, use_buffer_reader=True)val_loader = paddle.io.DataLoader(QRDataset(train_jpg[val_idx][:1000],transforms.Compose([transforms.Resize((196, 196)),# transforms.Resize((124, 124)),# transforms.RandomCrop((88, 88)),transforms.ToTensor(),transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])), batch_size=48, shuffle=False, num_workers=0,use_buffer_reader=True)model = XunFeiNet()'''model = paddlehub.Module(name='resnet50_vd_imagenet_ssld',label_list=["fearful", "surprised", "happy", "disgusted", "angry", "sad", "neutral"],load_checkpoint=None)'''criterion = nn.CrossEntropyLoss()optimizer = paddle.optimizer.SGD(0.01, parameters = model.parameters())best_acc = 0.0for epoch in range(100):print('\nEpoch: ', epoch)train(train_loader, model, criterion, optimizer, epoch)val_acc = validate(val_loader, model, criterion)if val_acc > best_acc:best_acc = val_accpaddle.save(model.state_dict(), './resnet18_fold{0}.pdparams'.format(flod_idx))break

预测生成结果

import glob
import numpy as np
import paddle
import time
from PIL import  Image
import paddle.vision.transforms as transforms
import paddlehub
import pandas as pdclass QRDataset(paddle.io.Dataset):def __init__(self, img_path, transform=None):self.img_path = img_pathif transform is not None:self.transform = transformelse:self.transform = Nonedef __getitem__(self, index):start_time = time.time()img = Image.open(self.img_path[index]).convert('RGB')lbl_dict = {'angry': 0,'disgusted': 1,'fearful': 2,'happy': 3,'neutral': 4,'sad': 5,'surprised': 6}if self.transform is not None:img = self.transform(img)if 'test' in self.img_path[index]:return img, paddle.to_tensor(np.array(0))else:lbl_int = lbl_dict[self.img_path[index].split('/')[-2]]return img, paddle.to_tensor(np.array(lbl_int))def __len__(self):return len(self.img_path)def predict(test_loader, model, tta=10):model.eval()test_pred_tta = Nonefor _ in range(tta):test_pred = []with paddle.no_grad():end = time.time()for i, (input_, target) in enumerate(test_loader):output = model(input_)[0]target = paddle.unsqueeze(paddle.to_tensor(target), 1)output = output.numpy()test_pred.append(output)test_pred = np.vstack(test_pred)if test_pred_tta is None:test_pred_tta = test_predelse:test_pred_tta += test_predreturn test_pred_ttatest_jpg = glob.glob('./Datawhale_人脸情绪识别_数据集/test/*')
test_jpg = np.array(test_jpg)
test_jpg.sort()test_loader = paddle.io.DataLoader(QRDataset(test_jpg,transforms.Compose([transforms.RandomHorizontalFlip(),transforms.RandomVerticalFlip(),transforms.ToTensor(),transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])), batch_size=50, shuffle=False, num_workers=5
)model = paddlehub.Module(name='resnet50_vd_imagenet_ssld',label_list=["fearful", "surprised", "happy", "disgusted", "angry", "sad", "neutral"],load_checkpoint='resnet18_fold0.pdparams')
test_pred = predict(test_loader, model, 5)cls_name = np.array(['angry', 'disgusted', 'fearful', 'happy','neutral', 'sad', 'surprised'])
submit_df = pd.DataFrame({'name': test_jpg, 'label': cls_name[test_pred.argmax(1)]})
submit_df['name'] = submit_df['name'].apply(lambda x: x.split('/')[-1])
submit_df = submit_df.sort_values(by='name')
submit_df.to_csv('paddle_submit.csv', index=None)