Transform：数据的预处理/数据增强

Transform：数据的预处理/数据增强

补充：数据增强（对数据集进行变换，丰富数据集，从而使模型更具泛化能力）

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数据增强一

1.裁剪（Crop）
2.翻转、旋转（Flip\Rotation）

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数据增强二

1.图像变换
2.transform方法操作
3.自定义的transform方法

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torchvision.transforms :常用的图像预处理方法
torchvision.datasets :常用数据集的dataset实现，MNIST, CIFAR-10, ImageNet等
torchvision.model :常用的模型预训练，AlexNet, VGG，ResNet, GoogLeNet等

上面的三个数据包为pytorch的视觉工具包中的主要工具包，还是比较重要的

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torchvision.transforms :常用的图像预处理方法

• 数据中心化
• 数据标准化
• 缩放
• 裁剪
• 旋转
• 翻转
• 填充
• 噪声添加
• 灰度变换
• 线性变换
• 仿射变换
• 亮度、饱和度及对比度变换

---

import torch
import torch.nn as nn
import matplotlib.pyplot as plt
import numpy as np
torch.manual_seed(10)

lr = 0.01  # 学习率

# 生成虚拟数据
sample_nums = 100
mean_value = 1.7
bias = 5         # 5
n_data = torch.ones(sample_nums, 2)
x0 = torch.normal(mean_value * n_data, 1) + bias      # 类别0 数据 shape=(100, 2)
y0 = torch.zeros(sample_nums)                         # 类别0 标签 shape=(100, 1)
x1 = torch.normal(-mean_value * n_data, 1) + bias     # 类别1 数据 shape=(100, 2)
y1 = torch.ones(sample_nums)                          # 类别1 标签 shape=(100, 1)
train_x = torch.cat((x0, x1), 0)
train_y = torch.cat((y0, y1), 0)


# 定义模型
class LR(nn.Module):
    def __init__(self):
        super(LR, self).__init__()
        self.features = nn.Linear(2, 1)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        x = self.features(x)
        x = self.sigmoid(x)
        return x


lr_net = LR()

# 定义损失函数与优化器
loss_fn = nn.BCELoss()
optimizer = torch.optim.SGD(lr_net.parameters(), lr=0.01, momentum=0.9)

for iteration in range(1000):

    # 前向传播
    y_pred = lr_net(train_x)

    # 计算 MSE loss
    loss = loss_fn(y_pred, train_y)

    # 反向传播
    loss.backward()

    # 更新参数
    optimizer.step()

    # 清空梯度
    optimizer.zero_grad()

    # 绘图
    if iteration % 40 == 0:

        mask = y_pred.ge(0.5).float().squeeze()  # 以0.5为阈值进行分类
        correct = (mask == train_y).sum()  # 计算正确预测的样本个数
        acc = correct.item() / train_y.size(0)  # 计算精度

        plt.scatter(x0.data.numpy()[:, 0], x0.data.numpy()[:, 1], c='r', label='class 0')
        plt.scatter(x1.data.numpy()[:, 0], x1.data.numpy()[:, 1], c='b', label='class 1')

        w0, w1 = lr_net.features.weight[0]
        w0, w1 = float(w0.item()), float(w1.item())
        plot_b = float(lr_net.features.bias[0].item())
        plot_x = np.arange(-6, 6, 0.1)
        plot_y = (-w0 * plot_x - plot_b) / w1

        plt.xlim(-5, 10)
        plt.ylim(-7, 10)
        plt.plot(plot_x, plot_y)

        plt.text(-5, 5, 'Loss=%.4f' % loss.data.numpy(), fontdict={'size': 20, 'color': 'red'})
        plt.title("Iteration: {}\nw0:{:.2f} w1:{:.2f} b: {:.2f} accuracy:{:.2%}".format(iteration, w0, w1, plot_b, acc))
        plt.legend()

        plt.show()
        plt.pause(0.5)

        if acc > 0.99:
            break

将数据的均值变为0 ，标准差变为1

为什么进行标准化，为了加快模型的收敛（改逻辑回归的bias可知）
（可以数学证明吗？？？）
为什么会慢就是模型的初始化是零均值的，如果不标准化，模型找最优分界平面就很慢。

Transform图像增强

import os
import numpy as np
import torch
import random
from torch.utils.data import DataLoader
import torchvision.transforms as transforms
from tools.my_dataset import RMBDataset
from PIL import Image
from matplotlib import pyplot as plt


def set_seed(seed=1):
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)


set_seed(1)  # 设置随机种子

# 参数设置
MAX_EPOCH = 10
BATCH_SIZE = 1
LR = 0.01
log_interval = 10
val_interval = 1
rmb_label = {"1": 0, "100": 1}


def transform_invert(img_, transform_train):
    """
    将data 进行反transfrom操作
    :param img_: tensor
    :param transform_train: torchvision.transforms
    :return: PIL image
    """
    if 'Normalize' in str(transform_train):
        norm_transform = list(filter(lambda x: isinstance(x, transforms.Normalize), transform_train.transforms))
        mean = torch.tensor(norm_transform[0].mean, dtype=img_.dtype, device=img_.device)
        std = torch.tensor(norm_transform[0].std, dtype=img_.dtype, device=img_.device)
        img_.mul_(std[:, None, None]).add_(mean[:, None, None])

    img_ = img_.transpose(0, 2).transpose(0, 1)  # C*H*W --> H*W*C
    img_ = np.array(img_) * 255

    if img_.shape[2] == 3:
        img_ = Image.fromarray(img_.astype('uint8')).convert('RGB')
    elif img_.shape[2] == 1:
        img_ = Image.fromarray(img_.astype('uint8').squeeze())
    else:
        raise Exception("Invalid img shape, expected 1 or 3 in axis 2, but got {}!".format(img_.shape[2]) )

    return img_


# ============================ step 1/5 数据 ============================
split_dir = os.path.join("..", "..", "data", "rmb_split")
train_dir = os.path.join(split_dir, "train")
valid_dir = os.path.join(split_dir, "valid")

norm_mean = [0.485, 0.456, 0.406]
norm_std = [0.229, 0.224, 0.225]


train_transform = transforms.Compose([
    transforms.Resize((224, 224)),

    # 1 CenterCrop
    # transforms.CenterCrop(512),     # 512

    # 2 RandomCrop
    # transforms.RandomCrop(224, padding=16),
    # transforms.RandomCrop(224, padding=(16, 64)),
    # transforms.RandomCrop(224, padding=16, fill=(255, 0, 0)),
    # transforms.RandomCrop(512, pad_if_needed=True),   # pad_if_needed=True
    # transforms.RandomCrop(224, padding=64, padding_mode='edge'),
    # transforms.RandomCrop(224, padding=64, padding_mode='reflect'),
    # transforms.RandomCrop(1024, padding=1024, padding_mode='symmetric'),

    # 3 RandomResizedCrop
    # transforms.RandomResizedCrop(size=224, scale=(0.5, 0.5)),

    # 4 FiveCrop
    # transforms.FiveCrop(112),
    # transforms.Lambda(lambda crops: torch.stack([(transforms.ToTensor()(crop)) for crop in crops])),

    # 5 TenCrop
    # transforms.TenCrop(112, vertical_flip=False),
    # transforms.Lambda(lambda crops: torch.stack([(transforms.ToTensor()(crop)) for crop in crops])),

    # 1 Horizontal Flip
    # transforms.RandomHorizontalFlip(p=1),

    # 2 Vertical Flip
    # transforms.RandomVerticalFlip(p=0.5),

    # 3 RandomRotation
    # transforms.RandomRotation(90),
    # transforms.RandomRotation((90), expand=True),
    # transforms.RandomRotation(30, center=(0, 0)),
    # transforms.RandomRotation(30, center=(0, 0), expand=True),   # expand only for center rotation

    transforms.ToTensor(),
    transforms.Normalize(norm_mean, norm_std),
])

valid_transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(norm_mean, norm_std)
])

# 构建MyDataset实例
train_data = RMBDataset(data_dir=train_dir, transform=train_transform)
valid_data = RMBDataset(data_dir=valid_dir, transform=valid_transform)

# 构建DataLoder
train_loader = DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)
valid_loader = DataLoader(dataset=valid_data, batch_size=BATCH_SIZE)


# ============================ step 5/5 训练 ============================
for epoch in range(MAX_EPOCH):
    for i, data in enumerate(train_loader):

        inputs, labels = data   # B C H W

        img_tensor = inputs[0, ...]     # C H W
        img = transform_invert(img_tensor, train_transform)
        plt.imshow(img)
        plt.show()
        plt.pause(0.5)
        plt.close()

        # bs, ncrops, c, h, w = inputs.shape
        # for n in range(ncrops):
        #     img_tensor = inputs[0, n, ...]  # C H W
        #     img = transform_invert(img_tensor, train_transform)
        #     plt.imshow(img)
        #     plt.show()
        #     plt.pause(1)

import os
import numpy as np
import torch
import random
from matplotlib import pyplot as plt
from torch.utils.data import DataLoader
import torchvision.transforms as transforms
from tools.my_dataset import RMBDataset
from tools.common_tools import transform_invert



def set_seed(seed=1):
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)


set_seed(1)  # 设置随机种子

# 参数设置
MAX_EPOCH = 10
BATCH_SIZE = 1
LR = 0.01
log_interval = 10
val_interval = 1
rmb_label = {"1": 0, "100": 1}


# ============================ step 1/5 数据 ============================
split_dir = os.path.join("..", "..", "data", "rmb_split")
train_dir = os.path.join(split_dir, "train")
valid_dir = os.path.join(split_dir, "valid")

norm_mean = [0.485, 0.456, 0.406]
norm_std = [0.229, 0.224, 0.225]


train_transform = transforms.Compose([
    transforms.Resize((224, 224)),

    # 1 Pad
    # transforms.Pad(padding=32, fill=(255, 0, 0), padding_mode='constant'),
    # transforms.Pad(padding=(8, 64), fill=(255, 0, 0), padding_mode='constant'),
    # transforms.Pad(padding=(8, 16, 32, 64), fill=(255, 0, 0), padding_mode='constant'),
    # transforms.Pad(padding=(8, 16, 32, 64), fill=(255, 0, 0), padding_mode='symmetric'),

    # 2 ColorJitter
    # transforms.ColorJitter(brightness=0.5),
    # transforms.ColorJitter(contrast=0.5),
    # transforms.ColorJitter(saturation=0.5),
    # transforms.ColorJitter(hue=0.3),

    # 3 Grayscale
    # transforms.Grayscale(num_output_channels=3),

    # 4 Affine
    # transforms.RandomAffine(degrees=30),
    # transforms.RandomAffine(degrees=0, translate=(0.2, 0.2), fillcolor=(255, 0, 0)),
    # transforms.RandomAffine(degrees=0, scale=(0.7, 0.7)),
    # transforms.RandomAffine(degrees=0, shear=(0, 0, 0, 45)),
    # transforms.RandomAffine(degrees=0, shear=90, fillcolor=(255, 0, 0)),

    # 5 Erasing
    # transforms.ToTensor(),
    # transforms.RandomErasing(p=1, scale=(0.02, 0.33), ratio=(0.3, 3.3), value=(254/255, 0, 0)),
    # transforms.RandomErasing(p=1, scale=(0.02, 0.33), ratio=(0.3, 3.3), value='1234'),

    # 1 RandomChoice
    # transforms.RandomChoice([transforms.RandomVerticalFlip(p=1), transforms.RandomHorizontalFlip(p=1)]),

    # 2 RandomApply
    # transforms.RandomApply([transforms.RandomAffine(degrees=0, shear=45, fillcolor=(255, 0, 0)),
    #                         transforms.Grayscale(num_output_channels=3)], p=0.5),
    # 3 RandomOrder
    # transforms.RandomOrder([transforms.RandomRotation(15),
    #                         transforms.Pad(padding=32),
    #                         transforms.RandomAffine(degrees=0, translate=(0.01, 0.1), scale=(0.9, 1.1))]),

    transforms.ToTensor(),
    transforms.Normalize(norm_mean, norm_std),
])

valid_transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(norm_mean, norm_std)
])

# 构建MyDataset实例
train_data = RMBDataset(data_dir=train_dir, transform=train_transform)
valid_data = RMBDataset(data_dir=valid_dir, transform=valid_transform)

# 构建DataLoder
train_loader = DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)
valid_loader = DataLoader(dataset=valid_data, batch_size=BATCH_SIZE)


# ============================ step 5/5 训练 ============================
for epoch in range(MAX_EPOCH):
    for i, data in enumerate(train_loader):

        inputs, labels = data   # B C H W

        img_tensor = inputs[0, ...]     # C H W
        img = transform_invert(img_tensor, train_transform)
        plt.imshow(img)
        plt.show()
        plt.pause(0.5)
        plt.close()