代碼逐一學(xué)習(xí).修改識別參數(shù),計(jì)算最優(yōu)識別
import os, sys, glob, shutil, json
os.environ["CUDA_VISIBLE_DEVICES"] ='0'
import cv2
from PILimport Image
import numpyas np
from tqdmimport tqdm, tqdm_notebook
import torch
torch.manual_seed(0)
torch.backends.cudnn.deterministic =False
torch.backends.cudnn.benchmark =True
import torchvision.modelsas models
import torchvision.transformsas transforms
import torchvision.datasetsas datasets
import torch.nnas nn
import torch.nn.functionalas F
import torch.optimas optim
from torch.autogradimport Variable
from torch.utils.data.datasetimport Dataset
class SVHNDataset(Dataset):#繼承自Dataset的類
? ? def __init__(self, img_path, img_label, transform=None):
self.img_path = img_path
self.img_label = img_label
if transformis not None:
self.transform = transform
else:
self.transform =None
? ? def __getitem__(self, index):
img = Image.open(self.img_path[index]).convert('RGB')
if self.transformis not None:
img =self.transform(img)
lbl = np.array(self.img_label[index],dtype=np.int)
lbl =list(lbl) + (5 -len(lbl)) * [10]
return img, torch.from_numpy(np.array(lbl[:5]))
def __len__(self):
return len(self.img_path)
train_path = glob.glob('C:\\Users\\Zhyang\\Desktop\\match\\mchar_train\\*.png')#glob.glob() 函數(shù),查找符合特定規(guī)則的文件路徑名
train_path.sort()#對List進(jìn)行排序.正序
train_json = json.load(open(r'C:\Users\Zhyang\Desktop\match\mchar_train.json'))
train_label = [train_json[x]['label']for xin train_json]#json中圖片對應(yīng)結(jié)果
# print(len(train_path), len(train_label))
train_loader = torch.utils.data.DataLoader(
SVHNDataset(train_path, train_label,
transforms.Compose([#
? ? ? ? ? ? ? ? ? ? transforms.Resize((64,128)),#h*w圖像變換 torchvision.transforms.Resize(size, interpolation=2)功能：重置圖像分辨率
? ? ? ? ? ? ? ? ? ? transforms.RandomCrop((60,120)),#隨機(jī)裁剪
? ? ? ? ? ? ? ? ? ? transforms.ColorJitter(0.3,0.3,0.2),#修改亮度樟插、對比度和飽和度：transforms.ColorJitter
? ? ? ? ? ? ? ? ? ? transforms.RandomRotation(45),#隨機(jī)旋轉(zhuǎn)
? ? ? ? ? ? ? ? ? ? transforms.ToTensor(),#圖像變換,轉(zhuǎn)為tensor闰渔，并歸一化至[0-1]
? ? ? ? ? ? ? ? ? ? transforms.Normalize([0.485,0.456,0.406], [0.229,0.224,0.225])#圖像變換,標(biāo)準(zhǔn)化
? ? ])),#dataset簇爆，#數(shù)據(jù)加載
? ? batch_size=40,#batch_size（int，optional） - 每個(gè)批次要加載的樣本數(shù)量（默認(rèn)值:)1脑慧。
? ? shuffle=True,#shuffle（bool什猖，optional） - 設(shè)置為True在每個(gè)重新調(diào)整數(shù)據(jù)（默認(rèn)值:) False野宜。
? ? num_workers=5,#num_workers（int乔妈，optional） - 用于數(shù)據(jù)加載的子進(jìn)程數(shù)。0表示數(shù)據(jù)將加載到主進(jìn)程中捆等。（默認(rèn)值：0）
)
val_path = glob.glob('C:\\Users\\Zhyang\\Desktop\\match\\mchar_val\\*.png')
val_path.sort()
val_json = json.load(open(r'C:\Users\Zhyang\Desktop\match\mchar_val.json'))
val_label = [val_json[x]['label']for xin val_json]
# print(len(val_path), len(val_label))
val_loader = torch.utils.data.DataLoader(
SVHNDataset(val_path, val_label,
transforms.Compose([
transforms.Resize((64,128)),
# h*w圖像變換 torchvision.transforms.Resize(size, interpolation=2)功能：重置圖像分辨率
? ? ? ? ? ? ? ? ? ? transforms.RandomCrop((60,120)),# 隨機(jī)裁剪
? ? ? ? ? ? ? ? ? ? transforms.ColorJitter(0.3,0.3,0.2),# 修改亮度滞造、對比度和飽和度：transforms.ColorJitter
? ? ? ? ? ? ? ? ? ? transforms.RandomRotation(45),# 隨機(jī)旋轉(zhuǎn)
? ? ? ? ? ? ? ? ? ? transforms.ToTensor(),# 圖像變換,轉(zhuǎn)為tensor，并歸一化至[0-1]
? ? ? ? ? ? ? ? ? ? transforms.Normalize([0.485,0.456,0.406], [0.229,0.224,0.225])# 圖像變換,標(biāo)準(zhǔn)化
? ? ? ? ? ? ? ? ])),# dataset栋烤，#數(shù)據(jù)加載
? ? batch_size=40,
shuffle=False,
num_workers=5,
)
class SVHN_Model1(nn.Module):
def __init__(self):
super(SVHN_Model1,self).__init__()
model_conv = models.resnet18(pretrained=True)
model_conv.avgpool = nn.AdaptiveAvgPool2d(1)
model_conv = nn.Sequential(*list(model_conv.children())[:-1])
self.cnn = model_conv
self.fc1 = nn.Linear(512,11)
self.fc2 = nn.Linear(512,11)
self.fc3 = nn.Linear(512,11)
self.fc4 = nn.Linear(512,11)
self.fc5 = nn.Linear(512,11)
def forward(self, img):
feat =self.cnn(img)
# print(feat.shape)
? ? ? ? feat = feat.view(feat.shape[0], -1)
c1 =self.fc1(feat)
c2 =self.fc2(feat)
c3 =self.fc3(feat)
c4 =self.fc4(feat)
c5 =self.fc5(feat)
return c1, c2, c3, c4, c5
def train(train_loader, model, criterion, optimizer,epoch):#模型谒养，準(zhǔn)則，優(yōu)化器班缎，時(shí)代
? ? # 切換模型為訓(xùn)練模式
? ? model.train()
train_loss = []
for i, (input, target)in enumerate(train_loader):#enumerate() 函數(shù)用于將一個(gè)可遍歷的數(shù)據(jù)對象(如列表蝴光、元組或字符串)組合為一個(gè)索引序列，同時(shí)列出數(shù)據(jù)和數(shù)據(jù)下標(biāo)达址，一般用在 for 循環(huán)當(dāng)中蔑祟。
? ? ? ? # print('train',i)
? ? ? ? if use_cuda:
input = input.cuda()
target = target.cuda()
target=target.long()
c0, c1, c2, c3, c4 = model(input)
loss = criterion(c0, target[:,0]) + \
criterion(c1, target[:,1]) + \
criterion(c2, target[:,2]) + \
criterion(c3, target[:,3]) + \
criterion(c4, target[:,4])
# loss /= 6
? ? ? ? optimizer.zero_grad()#意思是把梯度置零，也就是把loss關(guān)于weight的導(dǎo)數(shù)變成0.
? ? ? ? loss.backward()#利用backward()方法進(jìn)行梯度求解
? ? ? ? optimizer.step()#用了optimizer.step()沉唠，模型才會更新
? ? ? ? train_loss.append(loss.item())#把字典中每對key和value組成一個(gè)元組疆虚，并把這些元組放在列表中返回。
? ? return np.mean(train_loss)
def validate(val_loader, model, criterion):
# 切換模型為預(yù)測模型
? ? model.eval()
val_loss = []
# 不記錄模型梯度信息
? ? with torch.no_grad():
for i, (input, target)in enumerate(val_loader):
# print('validate',i)
? ? ? ? ? ? if use_cuda:
input = input.cuda()
target = target.cuda()
target = target.long()
c0, c1, c2, c3, c4 = model(input)
loss = criterion(c0, target[:,0]) + \
criterion(c1, target[:,1]) + \
criterion(c2, target[:,2]) + \
criterion(c3, target[:,3]) + \
criterion(c4, target[:,4])
# loss /= 6
? ? ? ? ? ? val_loss.append(loss.item())
return np.mean(val_loss)
def predict(test_loader, model, tta=10):
model.eval()
test_pred_tta =None
? ? # TTA 次數(shù)
? ? for _in range(tta):
test_pred = []
with torch.no_grad():
for i, (input, target)in enumerate(test_loader):
if use_cuda:
input = input.cuda()
c0, c1, c2, c3, c4 = model(input)
if use_cuda:
output = np.concatenate([
c0.data.cpu().numpy(),
c1.data.cpu().numpy(),
c2.data.cpu().numpy(),
c3.data.cpu().numpy(),
c4.data.cpu().numpy()],axis=1)
else:
output = np.concatenate([
c0.data.numpy(),
c1.data.numpy(),
c2.data.numpy(),
c3.data.numpy(),
c4.data.numpy()],axis=1)
test_pred.append(output)
test_pred = np.vstack(test_pred)
if test_pred_ttais None:
test_pred_tta = test_pred
else:
test_pred_tta += test_pred
return test_pred_tta
#生成模型
def mode_take():
best_loss=10
? ? for epochin range(10):
train_loss = train(train_loader, model, criterion, optimizer, epoch)
val_loss = validate(val_loader, model, criterion)
val_label = [''.join(map(str, x))for xin val_loader.dataset.img_label]
print('val_label',val_label[20])
val_predict_label = predict(val_loader, model,1)
val_predict_label = np.vstack([
val_predict_label[:, :11].argmax(1),
val_predict_label[:,11:22].argmax(1),
val_predict_label[:,22:33].argmax(1),
val_predict_label[:,33:44].argmax(1),
val_predict_label[:,44:55].argmax(1),
]).T
val_label_pred = []
for xin val_predict_label:
val_label_pred.append(''.join(map(str, x[x !=10])))
print('val_label_pred',val_label_pred[:20])
val_char_acc = np.mean(np.array(val_label_pred) == np.array(val_label))
print('Epoch: {0}, Train loss: {1} \t Val loss: {2}'.format(epoch, train_loss, val_loss))
print('Val Acc', val_char_acc)
# 記錄下驗(yàn)證集精度
? ? ? ? if val_loss < best_loss:
best_loss = val_loss
print('Find better model in Epoch {0}, saving model.'.format(epoch))
torch.save(model.state_dict(),'C:\\Users\\Zhyang\\Desktop\\match\\model.pt')
break
#預(yù)測并生成提交文件
def produce():
test_path = glob.glob('C:\\Users\\Zhyang\\Desktop\\match\\mchar_test_a\\*.png')
test_path.sort()
test_label = [[1]] *len(test_path)
print(len(test_path),len(test_label))
test_loader = torch.utils.data.DataLoader(
SVHNDataset(test_path, test_label,
transforms.Compose([
transforms.Resize((70,140)),
transforms.RandomCrop((60,120)),
transforms.ColorJitter(0.3,0.3,0.2),
transforms.RandomRotation(5),
transforms.ToTensor(),
transforms.Normalize([0.485,0.456,0.406], [0.229,0.224,0.225])
])),
batch_size=40,
shuffle=False,
num_workers=5,
)
# 加載保存的最優(yōu)模型
? ? model.load_state_dict(torch.load('C:\\Users\\Zhyang\\Desktop\\match\\model.pt'))
test_predict_label = predict(test_loader, model,1)
print(test_predict_label[:, :11].argmax(1))
print(test_predict_label.shape)
test_label = [''.join(map(str, x))for xin test_loader.dataset.img_label]
test_predict_label = np.vstack([#vstack(tup) 满葛，參數(shù)tup可以是元組径簿，列表，或者numpy數(shù)組嘀韧，返回結(jié)果為numpy的數(shù)組篇亭。
? ? ? ? test_predict_label[:, :11].argmax(1),
test_predict_label[:,11:22].argmax(1),
test_predict_label[:,22:33].argmax(1),
test_predict_label[:,33:44].argmax(1),
test_predict_label[:,44:55].argmax(1),
]).T
print('test_predict_label',test_predict_label)
test_label_pred = []
for xin test_predict_label:
test_label_pred.append(''.join(map(str, x[x !=10])))
print('test_label_pred ',test_label_pred )
print(type(test_label_pred))
import pandasas pd
df_submit = pd.read_csv('C:\\Users\\Zhyang\\Desktop\\match\\mchar_sample_submit_A.csv')
df_submit['file_code'] = test_label_pred
df_submit.to_csv('C:\\Users\\Zhyang\\Desktop\\match\\mchar_sample_submit_A.csv',index=None)
#訓(xùn)練與驗(yàn)證
if __name__ =='__main__':
model = SVHN_Model1()#
? ? criterion = nn.CrossEntropyLoss()#損失函數(shù)用
? ? optimizer = torch.optim.Adam(model.parameters(),0.001)# torch.optim.Adam實(shí)現(xiàn)Adam算法,model.parameters()獲取網(wǎng)絡(luò)的參數(shù)
? ? # params (iterable) – 待優(yōu)化參數(shù)的iterable或者是定義了參數(shù)組的dict,lr (float, 可選) – 學(xué)習(xí)率（默認(rèn)：1e-3）
? ? best_loss =1000.0
? ? # 是否使用GPU
? ? use_cuda =True
? ? if use_cuda:
model = model.cuda()
mode_take()
# produce()
