from turtle import forward
import torch.nn as nn
import torch
class DoubleConv(nn.Module):
    def __init__(self, in_ch, out_ch):
        super(DoubleConv, self).__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(in_ch, out_ch, 3, padding=1),
            nn.BatchNorm2d(out_ch),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_ch, out_ch, 3, padding=1),
            nn.BatchNorm2d(out_ch),
            nn.ReLU(inplace=True)
        )
    def forward(self, x):
        return self.conv(x)
class Unet(nn.Module):
    def __init__(self, in_ch, out_ch):
        super(Unet, self).__init__()
        # Encoder
        self.conv1 = DoubleConv(in_ch, 64)
        self.pool1 = nn.MaxPool2d(2)
        self.conv2 = DoubleConv(64, 128)
        self.pool2 = nn.MaxPool2d(2)
        self.conv3 = DoubleConv(128, 256)
        self.pool3 = nn.MaxPool2d(2)
        self.conv4 = DoubleConv(256, 512)
        self.pool4 = nn.MaxPool2d(2)
        self.conv5 = DoubleConv(512, 1024)
        # Decoder
        self.up6 = nn.ConvTranspose2d(1024, 512, 2, stride=2)
        self.conv6 = DoubleConv(1024, 512)
        self.up7 = nn.ConvTranspose2d(512, 256, 2, stride=2)
        self.conv7 = DoubleConv(512, 256)
        self.up8 = nn.ConvTranspose2d(256, 128, 2, stride=2)
        self.conv8 = DoubleConv(256, 128)
        self.up9 = nn.ConvTranspose2d(128, 64, 2, stride=2)
        self.conv9 = DoubleConv(128, 64)
        self.output = nn.Conv2d(64, out_ch, 1)
    def forward(self, x):
        conv1 = self.conv1(x)
        pool1 = self.pool1(conv1)
        conv2 = self.conv2(pool1)
        pool2 = self.pool2(conv2)
        conv3 = self.conv3(pool2)
        pool3 = self.pool3(conv3)
        conv4 = self.conv4(pool3)
        pool4 = self.pool4(conv4)
        conv5 = self.conv5(pool4)
        up6 = self.up6(conv5)
        meger6 = torch.cat([up6, conv4], dim=1)
        conv6 = self.conv6(meger6)
        up7 = self.up7(conv6)
        meger7 = torch.cat([up7, conv3], dim=1)
        conv7 = self.conv7(meger7)
        up8 = self.up8(conv7)
        meger8 = torch.cat([up8, conv2], dim=1)
        conv8 = self.conv8(meger8)
        up9 = self.up9(conv8)
        meger9 = torch.cat([up9, conv1], dim=1)
        conv9 = self.conv9(meger9)
        out = self.output(conv9)
        return out
if __name__=="__main__":
    model = Unet(3, 21)
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model = model.to(device)
    print(model)

from PIL import Image
from requests import check_compatibility
import torch
import torch.nn as nn
from torch.utils.data import DataLoader, Dataset
from torchvision import transforms as T
import numpy as np
import os
import matplotlib.pyplot as plt
class Car(Dataset):
    def __init__(self, root, train=True):
        self.root = root
        self.crop_size = (256, 256)
        self.img_path = os.path.join(root, "train_hq")
        self.label_path = os.path.join(root, "train_masks")
        img_path_list = [os.path.join(self.img_path, im) for im in os.listdir(self.img_path)]
        train_path_list, val_path_list = self._split_data_set(img_path_list)
        if train:
            self.imgs_list = train_path_list
        else:
            self.imgs_list = val_path_list
        normalize = T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
        self.transforms = T.Compose([
                T.Resize(256),
                T.CenterCrop(256),
                T.ToTensor(),
                normalize
            ])
        self.transforms_val = T.Compose([
            T.Resize(256),
            T.CenterCrop(256)
        ])
        self.color_map = [[0, 0, 0], [255, 255, 255]]
    def __getitem__(self, index: int):
        im_path = self.imgs_list[index]
        image = Image.open(im_path).convert("RGB")
        data = self.transforms(image)
        (filepath, filename) = os.path.split(im_path)
        filename = filename.split('.')[0]
        label = Image.open(self.label_path +"/"+filename+"_mask.gif").convert("RGB")
        label = self.transforms_val(label)
        cm2lb=np.zeros(256**3)
        for i,cm in enumerate(self.color_map):
            cm2lb[(cm[0]*256+cm[1])*256+cm[2]]=i
        image=np.array(label,dtype=np.int64)
        idx=(image[:,:,0]*256+image[:,:,1])*256+image[:,:,2]
        label=np.array(cm2lb[idx],dtype=np.int64)
        label=torch.from_numpy(label).long()
        return data, label
    def label2img(self, label):
        cmap = self.color_map
        cmap = np.array(cmap).astype(np.uint8)
        pred = cmap[label]
        return pred
    def __len__(self):
        return len(self.imgs_list)
    def _split_data_set(self, img_path_list):
        val_path_list = img_path_list[::8]
        train_path_list = []
        for item in img_path_list:
            if item not in val_path_list:
                train_path_list.append(item)
        return train_path_list, val_path_list
if __name__=="__main__":
    root = "../dataset/carvana"
    car_train = Car(root,train=True)
    train_dataloader = DataLoader(car_train, batch_size=8, shuffle=True)
    print(len(car_train))
    print(len(train_dataloader))
    # for data, label in car_train:
    #     print(data.shape)
    #     print(label.shape)
    #     break
    (data, label) = car_train[190]
    label_np = label.data.numpy()
    label_im = car_train.label2img(label_np)
    plt.figure()
    plt.imshow(label_im)
    plt.show()

import torch
import torch.nn as nn
from torch.utils.data import DataLoader,Dataset
from voc import VOC
from carnava import Car
from unet import Unet
import os
import numpy as np
from torch import optim
import torch.nn as nn
import util
# 計(jì)算混淆矩陣
def _fast_hist(label_true, label_pred, n_class):
    mask = (label_true >= 0) & (label_true < n_class)
    hist = np.bincount(
        n_class * label_true[mask].astype(int) +
        label_pred[mask], minlength=n_class ** 2).reshape(n_class, n_class)
    return hist
def label_accuracy_score(label_trues, label_preds, n_class):
    """Returns accuracy score evaluation result.
      - overall accuracy
      - mean accuracy
      - mean IU
    """
    hist = np.zeros((n_class, n_class))
    for lt, lp in zip(label_trues, label_preds):
        hist += _fast_hist(lt.flatten(), lp.flatten(), n_class)
    acc = np.diag(hist).sum() / hist.sum()
    with np.errstate(divide='ignore', invalid='ignore'):
        acc_cls = np.diag(hist) / hist.sum(axis=1)
    acc_cls = np.nanmean(acc_cls)
    with np.errstate(divide='ignore', invalid='ignore'):
        iu = np.diag(hist) / (
            hist.sum(axis=1) + hist.sum(axis=0) - np.diag(hist)
        )
    mean_iu = np.nanmean(iu)
    freq = hist.sum(axis=1) / hist.sum()
    return acc, acc_cls, mean_iu
out_path = "./out"
if not os.path.exists(out_path):
    os.makedirs(out_path)
log_path = os.path.join(out_path, "result.txt")
if os.path.exists(log_path):
    os.remove(log_path)
model_path = os.path.join(out_path, "best_model.pth")
root = "../dataset/carvana"
epochs = 5
numclasses = 2
train_data = Car(root, train=True)
train_dataloader = DataLoader(train_data, batch_size=16, shuffle=True)
val_data = Car(root, train=False)
val_dataloader = DataLoader(val_data, batch_size=16, shuffle=True)
net = Unet(3, numclasses)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
net = net.to(device)
optimizer = optim.SGD(net.parameters(), lr=0.01, weight_decay=1e-4)
criterion = nn.CrossEntropyLoss()
def train_model():
    best_score = 0.0
    for e in range(epochs):
        net.train()
        train_loss = 0.0
        label_true = torch.LongTensor()
        label_pred = torch.LongTensor()
        for batch_id, (data, label) in enumerate(train_dataloader):
            data, label = data.to(device), label.to(device)
            output = net(data)
            loss = criterion(output, label)
            pred = output.argmax(dim=1).squeeze().data.cpu()
            real = label.data.cpu()
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
            train_loss+=loss.cpu().item()
            label_true = torch.cat((label_true,real),dim=0)
            label_pred = torch.cat((label_pred,pred),dim=0)
        train_loss /= len(train_dataloader)
        acc, acc_cls, mean_iu = label_accuracy_score(label_true.numpy(),label_pred.numpy(),numclasses)
        print("\n epoch:{}, train_loss:{:.4f}, acc:{:.4f}, acc_cls:{:.4f}, mean_iu:{:.4f}".format(
            e+1, train_loss, acc, acc_cls, mean_iu))
        with open(log_path, 'a') as f:
            f.write('\n epoch:{}, train_loss:{:.4f}, acc:{:.4f}, acc_cls:{:.4f}, mean_iu:{:.4f}'.format(
                e+1,train_loss,acc, acc_cls, mean_iu))
        net.eval()
        val_loss = 0.0
        val_label_true = torch.LongTensor()
        val_label_pred = torch.LongTensor()
        with torch.no_grad():
            for batch_id, (data, label) in enumerate(val_dataloader):
                data, label = data.to(device), label.to(device)
                output = net(data)
                loss = criterion(output, label)
                pred = output.argmax(dim=1).squeeze().data.cpu()
                real = label.data.cpu()
                val_loss += loss.cpu().item()
                val_label_true = torch.cat((val_label_true, real), dim=0)
                val_label_pred = torch.cat((val_label_pred, pred), dim=0)
            val_loss/=len(val_dataloader)
            val_acc, val_acc_cls, val_mean_iu = label_accuracy_score(val_label_true.numpy(),
                                                                    val_label_pred.numpy(),numclasses)
        print('\n epoch:{}, val_loss:{:.4f}, acc:{:.4f}, acc_cls:{:.4f}, mean_iu:{:.4f}'.format(e+1, val_loss, val_acc, val_acc_cls, val_mean_iu))
        with open(log_path, 'a') as f:
            f.write('\n epoch:{}, val_loss:{:.4f}, acc:{:.4f}, acc_cls:{:.4f}, mean_iu:{:.4f}'.format(
            e+1,val_loss,val_acc, val_acc_cls, val_mean_iu))
        score = (val_acc_cls+val_mean_iu)/2
        if score > best_score:
            best_score = score
            torch.save(net.state_dict(), model_path)
def evaluate():
    import util
    import random
    import matplotlib.pyplot as plt
    net.load_state_dict(torch.load(model_path))
    index = random.randint(0, len(val_data)-1)
    val_image, val_label = val_data[index]
    out = net(val_image.unsqueeze(0).to(device))
    pred = out.argmax(dim=1).squeeze().data.cpu().numpy()
    label = val_label.data.numpy()
    img_pred = val_data.label2img(pred)
    img_label = val_data.label2img(label)
    temp = val_image.numpy()
    temp = (temp-np.min(temp)) / (np.max(temp)-np.min(temp))*255
    fig, ax = plt.subplots(1,3)
    ax[0].imshow(temp.transpose(1,2,0).astype("uint8"))
    ax[1].imshow(img_label)
    ax[2].imshow(img_pred)
    plt.show()
if __name__=="__main__":
    # train_model()
    evaluate()