1.數據處理代碼utils.py:
1）
# coding:utf-8 import os import torch.nn as nn import numpy as np import scipy.misc import imageio import matplotlib.pyplot as plt import torch def tensor2im(input_image, imtype=np.uint8): """"將tensor的數據類型轉成numpy類型，並反歸一化. Parameters: input_image (tensor) -- 輸入的圖像tensor數組 imtype (type) -- 轉換后的numpy的數據類型 """ mean = [0.485,0.456,0.406] #dataLoader中設置的mean參數 std = [0.229,0.224,0.225] #dataLoader中設置的std參數 if not isinstance(input_image, np.ndarray): if isinstance(input_image, torch.Tensor): #如果傳入的圖片類型為torch.Tensor，則讀取其數據進行下面的處理 image_tensor = input_image.data else: return input_image image_numpy = image_tensor.cpu().float().numpy() # convert it into a numpy array if image_numpy.shape[0] == 1: # grayscale to RGB image_numpy = np.tile(image_numpy, (3, 1, 1)) for i in range(len(mean)): #反標准化 image_numpy[i] = image_numpy[i] * std[i] + mean[i] image_numpy = image_numpy * 255 #反ToTensor(),從[0,1]轉為[0,255] image_numpy = np.transpose(image_numpy, (1, 2, 0)) # 從(channels, height, width)變為(height, width, channels) else: # 如果傳入的是numpy數組,則不做處理 image_numpy = input_image return image_numpy.astype(imtype) def save_img(im, path, size): """im可是沒經過任何處理的tensor類型的數據,將數據存儲到path中 Parameters: im (tensor) -- 輸入的圖像tensor數組 path (str) -- 圖像尋出的路徑 size (list/tuple) -- 圖像合並的高寬(heigth, width) """ scipy.misc.imsave(path, merge(im, size)) #將合並后的圖保存到相應path中 def merge(images, size): """ 將batch size張圖像合成一張大圖，一行有size張圖 :param images: 輸入的圖像tensor數組,shape = (batch_size, channels, height, width) :param size: 合並的高寬(heigth, width) :return: 合並后的圖 """ h, w = images[0].shape[1], images[0].shape[1] if (images[0].shape[0] in (3,4)): # 彩色圖像 c = images[0].shape[0] img = np.zeros((h * size[0], w * size[1], c)) for idx, image in enumerate(images): i = idx % size[1] j = idx // size[1] image = tensor2im(image) img[j * h:j * h + h, i * w:i * w + w, :] = image return img elif images.shape[3]==1: # 灰度圖像 img = np.zeros((h * size[0], w * size[1])) for idx, image in enumerate(images): i = idx % size[1] j = idx // size[1] image = tensor2im(image) img[j * h:j * h + h, i * w:i * w + w] = image[:,:,0] return img else: raise ValueError('in merge(images,size) images parameter ''must have dimensions: HxW or HxWx3 or HxWx4')
2）
后面發現torchvision.utils有一個make_grid()函數能夠直接實現將（batchsize，channels，height，width）格式的tensor圖像數據合並成一張圖。
同時其也有一個save_img(tensor, file_path)的方法，如果你的歸一化的均值和方差都設置為0.5，那么你可以很簡單地使用這個方法保存圖片
但是因為我這里的均值和方差是自定義的，所以要自己寫一個。所以上面的代碼的merge()函數就可以不用了，可以簡化為：
# coding:utf-8 import os, torchvision import torch.nn as nn import numpy as np import imageio import matplotlib.pyplot as plt from PIL import Image import torch def tensor2im(input_image, imtype=np.uint8): """"將tensor的數據類型轉成numpy類型，並反歸一化. Parameters: input_image (tensor) -- 輸入的圖像tensor數組 imtype (type) -- 轉換后的numpy的數據類型 """ mean = [0.485,0.456,0.406] #自己設置的 std = [0.229,0.224,0.225] #自己設置的 if not isinstance(input_image, np.ndarray): if isinstance(input_image, torch.Tensor): # get the data from a variable image_tensor = input_image.data else: return input_image image_numpy = image_tensor.cpu().float().numpy() # convert it into a numpy array if image_numpy.shape[0] == 1: # grayscale to RGB image_numpy = np.tile(image_numpy, (3, 1, 1)) for i in range(len(mean)): image_numpy[i] = image_numpy[i] * std[i] + mean[i] image_numpy = image_numpy * 255 image_numpy = np.transpose(image_numpy, (1, 2, 0)) # post-processing: tranpose and scaling else: # if it is a numpy array, do nothing image_numpy = input_image return image_numpy.astype(imtype) def save_img(im, path, size): """im可是沒經過任何處理的tensor類型的數據,將數據存儲到path中 Parameters: im (tensor) -- 輸入的圖像tensor數組 path (str) -- 圖像保存的路徑 size (int) -- 一行有size張圖,最好是2的倍數 """ im_grid = torchvision.utils.make_grid(im, size) #將batchsize的圖合成一張圖 im_numpy = tensor2im(im_grid) #轉成numpy類型並反歸一化 im_array = Image.fromarray(im_numpy) im_array.save(path)
2.數據讀取代碼dataLoader.py為：
# coding:utf-8 from torch.utils.data import DataLoader import utils import torch.utils.data as data from PIL import Image import os import torchvision.transforms as transforms import torch class ListDataset(data.Dataset): """處理數據，返回圖片數據和數據類型""" def __init__(self, root, transform, type): self.type_list = [] self.imgsList = [] self.transform = transform self.imgs = os.listdir(root) for img in self.imgs: #得到所有數據的路徑 self.imgsList.append(os.path.join(root, img)) self.type_list.append(int(type)) def __getitem__(self, idx): img_path = self.imgsList[idx] img = Image.open(img_path) img = self.transform(img) type_pred = self.type_list[idx] return img, type_pred def __len__(self): return len(self.imgs) def getTransform(input_size): transform = transforms.Compose([ transforms.Resize((input_size, input_size)),#重置大小 transforms.ToTensor(), #轉為[0,1]值 transforms.Normalize((0.485,0.456,0.406), (0.229,0.224,0.225)) #標准化處理（mean, std） ]) return transform def dataloader0(input_size, batch_size, type): transform = getTransform(input_size) dataset = ListDataset(root='./GAN/data/0', transform=transform, type=type) loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True, num_workers=8) return loader if __name__ == '__main__': batch_size = 4 dataloader0 = dataloader0(input_size=224, batch_size=batch_size, type=1) fix_images, _ = next(iter(dataloader0)) utils.save_img(fix_images, './real.png', (1, batch_size))
運行該代碼，保存圖像為：
使用簡化后的utils.py代碼,dataloader.py也要相應更改為：
if __name__ == '__main__': batch_size = 4 dataloader0 = dataloader0(input_size=256, batch_size=batch_size, type=1) fix_images, _ = next(iter(dataloader0)) utils.save_img(fix_images, './real.png', batch_size)
保存的圖片為，效果相同：
