import os
import torch
import torchvision
from torch import nn
from d2l import torch as d2l

# 整理数据集
def reorg_dog_data(data_dir, valid_ratio):
    labels = d2l.read_csv_labels(os.path.join(data_dir,'labels.csv'))
    d2l.reorg_train_valid(data_dir,labels,valid_ratio)
    d2l.reorg_test(data_dir)
    
batch_size = 32 if demo else 128
valid_ratio = 0.1
reorg_dog_data(data_dir, valid_ratio)

# 图像增广
transform_train = torchvision.transforms.Compose([
    torchvision.transforms.RandomResizedCrop(224,scale=(0.08,1.0),ratio=(3.0/4.0, 4.0/3.0)),
    torchvision.transforms.RandomHorizontalFlip(),
    torchvision.transforms.ColorJitter(brightness=0.4,contrast=0.4,saturation=0.4),
    torchvision.transforms.ToTensor(),
    torchvision.transforms.Normalize([0.485,0.456,0.406],
                                    [0.229,0.224,0.225])])

transform_test = torchvision.transforms.Compose([
    torchvision.transforms.Resize(256),
    torchvision.transforms.CenterCrop(224),
    torchvision.transforms.ToTensor(),
    torchvision.transforms.Normalize([0.485,0.456,0.406],
                                     [0.229,0.224,0.225])])

train_ds, train_valid_ds = [
    torchvision.datasets.ImageFolder(
        os.path.join(data_dir,'train_valid_test',folder),
        transform=transform_train) for folder in ['train','train_valid']]       

valid_ds, test_ds = [
    torchvision.datasets.ImageFolder(
    os.path.join(data_dir,'train_valid_test',folder),
    transform=transform_test) for folder in ['valid','test']]

train_iter, train_valid_iter = [
    torch.utils.data.DataLoader(dataset,batch_size,shuffle=True,drop_last=True) for dataset in (train_ds, train_valid_ds)]

valid_iter = torch.utils.data.DataLoader(valid_ds,batch_size,shuffle=False,drop_last=True)
test_iter = torch.utils.data.DataLoader(test_ds,batch_size,shuffle=False,drop_last=False)        

# 微调预训练模型
# 除了最后一层外，前面的层固定住参数不变
def get_net(device):
    finetune_net = nn.Sequential()
    finetune_net.features = torchvision.models.resnet34(pretrained=True)
    print("finetune_net：", finetune_net)
    finetune_net.output_new = nn.Sequential(nn.Linear(1000,256),nn.ReLU(),nn.Linear(256,120)) #  在原始网络后又加了一层 
    print("finetune_net：", finetune_net)
    finetune_net = finetune_net.to(devices[0])
    for param in finetune_net.features.parameters(): # 遍历features的所有参数
        param.requires_grad = False  
    return finetune_net # 返回整个网络，这个网络中原始层的参数固定住了，保持不变

# 计算损失
loss = nn.CrossEntropyLoss(reduction='none')

def evaluate_loss(data_iter, net, devices):
    l_sum, n = 0.0, 0
    for features, labels in data_iter:
        features, labels = features.to(devices[0]), labels.to(devices[0])
        outputs = net(features)
        l = loss(outputs, labels)
        l_sum += l.sum()
        n += labels.numel()
    return l_sum / n

# 训练函数
def train(net, train_iter, valid_iter, num_epochs, lr, wd, devices, lr_period,lr_decay):
    net = nn.DataParallel(net,device_ids=devices).to(devices[0])
    trainer = torch.optim.SGD(
        (param for param in net.parameters() if param.requires_grad), 
        lr = lr, momentum = 0.9, weight_decay=wd)
    scheduler = torch.optim.lr_scheduler.StepLR(trainer, lr_period, lr_decay)
    num_batches, timer = len(train_iter), d2l.Timer()
    legend = ['train loss']
    if valid_iter is not None:
        legend.append('valid loss')
    animator = d2l.Animator(xlabel='epoch',xlim=[1,num_epochs],legend=legend)
    
    for epoch in range(num_epochs):
        metric = d2l.Accumulator(2)
        for i, (features, labels) in enumerate(train_iter):
            timer.start()
            features, labels = features.to(devices[0]), labels.to(devices[0])
            trainer.zero_grad()
            output = net(features)
            l = loss(output, labels).sum()
            l.backward()
            trainer.step()
            metric.add(l,labels.shape[0])
            timer.stop()
            if (i+1) % (num_batches // 5) == 0 or i == num_batches -1:
                animator.add(epoch + (i+1) / num_batches,
                             (metric[0] / metric[1], None))
        measures = f'train loss {metric[0] / metric[1]:.3f}'
        if valid_iter is not None:
            valid_loss = evaluate_loss(valid_iter, net, devices)
            animator.add(epoch + 1, (None, valid_loss.detach()))
        scheduler.step()
    if valid_iter is not None:
        measures += f', valid loss {valid_loss:.3f}'
    print(measures + f'\n{metric[1] * num_epochs / timer.sum():.1f}'
         f' examples/sec on {str(devices)}')