這篇文章主要介紹了關於pytorch visdom CNN處理自建圖片資料集的方法，有著一定的參考價值，現在分享給大家，有需要的朋友可以參考一下
環境
系統：win10
cpu：i7-6700HQ
gpu：gtx965m
python : 3.6
pytorch ：0.3
資料下載
#來源自Sasank Chilamkurthy 的教學；資料：下載連結。
下載後解壓縮放到專案根目錄：
資料集為用來分類 螞蟻和蜜蜂。有大約120個訓練圖像，每個類別有75個驗證圖像。
資料導入
可以使用 torchvision.datasets.ImageFolder(root,transforms) 模組 可以將 圖片轉換為 tensor。
先定義transform：
ata_transforms = {
'train': transforms.Compose([
# 随机切成224x224 大小图片 统一图片格式
transforms.RandomResizedCrop(224),
# 图像翻转
transforms.RandomHorizontalFlip(),
# totensor 归一化(0,255) >> (0,1) normalize channel=（channel-mean）/std
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
]),
"val" : transforms.Compose([
# 图片大小缩放 统一图片格式
transforms.Resize(256),
# 以中心裁剪
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
}#匯入，載入資料：
##
data_dir = './hymenoptera_data'
# trans data
image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x]) for x in ['train', 'val']}
# load data
data_loaders = {x: DataLoader(image_datasets[x], batch_size=BATCH_SIZE, shuffle=True) for x in ['train', 'val']}
data_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
class_names = image_datasets['train'].classes
print(data_sizes, class_names){'train': 244, 'val': 153} ['ants', 'bees']inputs, classes = next(iter(data_loaders['val'])) out = torchvision.utils.make_grid(inputs) inp = torch.transpose(out, 0, 2) mean = torch.FloatTensor([0.485, 0.456, 0.406]) std = torch.FloatTensor([0.229, 0.224, 0.225]) inp = std * inp + mean inp = torch.transpose(inp, 0, 2) viz.images(inp)
建立CNN
net 根據上一篇的處理cifar10的改了一下規格：class CNN(nn.Module):
def __init__(self, in_dim, n_class):
super(CNN, self).__init__()
self.cnn = nn.Sequential(
nn.BatchNorm2d(in_dim),
nn.ReLU(True),
nn.Conv2d(in_dim, 16, 7), # 224 >> 218
nn.BatchNorm2d(16),
nn.ReLU(inplace=True),
nn.MaxPool2d(2, 2), # 218 >> 109
nn.ReLU(True),
nn.Conv2d(16, 32, 5), # 105
nn.BatchNorm2d(32),
nn.ReLU(True),
nn.Conv2d(32, 64, 5), # 101
nn.BatchNorm2d(64),
nn.ReLU(True),
nn.Conv2d(64, 64, 3, 1, 1),
nn.BatchNorm2d(64),
nn.ReLU(True),
nn.MaxPool2d(2, 2), # 101 >> 50
nn.Conv2d(64, 128, 3, 1, 1), #
nn.BatchNorm2d(128),
nn.ReLU(True),
nn.MaxPool2d(3), # 50 >> 16
)
self.fc = nn.Sequential(
nn.Linear(128*16*16, 120),
nn.BatchNorm1d(120),
nn.ReLU(True),
nn.Linear(120, n_class))
def forward(self, x):
out = self.cnn(x)
out = self.fc(out.view(-1, 128*16*16))
return out
# 输入3层rgb ，输出 分类 2
model = CNN(3, 2)line = viz.line(Y=np.arange(10)) loss_f = nn.CrossEntropyLoss() optimizer = optim.SGD(model.parameters(), lr=LR, momentum=0.9) scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=7, gamma=0.1)
BATCH_SIZE = 4 LR = 0.001 EPOCHS = 10
##運行10個epoch 看看：
[9/10] train_loss:0.650|train_acc:0.639|test_loss:0.621|test_acc0.706 [10/10] train_loss:0.645|train_acc:0.627|test_loss:0.654|test_acc0.686 Training complete in 1m 16s Best val Acc: 0.712418
運行20個看看：
[19/20] train_loss:0.592|train_acc:0.701|test_loss:0.563|test_acc0.712 [20/20] train_loss:0.564|train_acc:0.721|test_loss:0.571|test_acc0.706 Training complete in 2m 30s Best val Acc: 0.745098
##準確率比較低：只有74.5%
我們使用models 裡的resnet18 運行10個epoch：
model = torchvision.models.resnet18(True) num_ftrs = model.fc.in_features model.fc = nn.Linear(num_ftrs, 2)
[9/10] train_loss:0.621|train_acc:0.652|test_loss:0.588|test_acc0.667 [10/10] train_loss:0.610|train_acc:0.680|test_loss:0.561|test_acc0.667 Training complete in 1m 24s Best val Acc: 0.686275
效果也很一般，想要短時間內就訓練出效果很好的models，我們可以下載訓練好的state，在此基礎上訓練：
model = torchvision.models.resnet18(pretrained=True) num_ftrs = model.fc.in_features model.fc = nn.Linear(num_ftrs, 2)
[9/10] train_loss:0.308|train_acc:0.877|test_loss:0.160|test_acc0.941 [10/10] train_loss:0.267|train_acc:0.885|test_loss:0.148|test_acc0.954 Training complete in 1m 25s Best val Acc: 0.954248
pytorch visdom 處理簡單分類問題
###################以上是pytorch + visdom CNN處理自建圖片資料集的方法的詳細內容。更多資訊請關注PHP中文網其他相關文章！
