2-1 transform1--数据增强

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--》可以直接用plt可视化出来 """ 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]) # 乘方差 + 均值(Normalize的反操作) img_ = img_.transpose(0, 2).transpose(0, 1) # C*H*W --> H*W*C img_ = np.array(img_) * 255 # 把数据0-1--》转换到0-255 # 把图像有ndarray形式转换为PIL形式（对三通道/单通道--》转换为彩色图或者灰度图） 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(196), # 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：---》【1】裁剪成5/10张图片都是tuple形式 transforms.FiveCrop(112), # TypeError: pic should be PIL Image or ndarray. Got <class 'tuple'> 直接操作不行 # 必须要进行转换--》PIL transforms.Lambda(lambda crops: torch.stack([(transforms.ToTensor()(crop)) for crop in crops])), # 函数输入：函数返回值 # 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 # 【1】对transforms进行逆操作---》使得我们可以观察到模型输入的数据是什么样子 # 因为数据进行transforms--》转换成了张量的形式，是一些浮点数据，没办法直接观察 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)