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人工智能系统实战第三期/实战代码/深度学习项目实战/生成对抗网络/utils.py

+import torch
+import torch
+from torch import nn
+from torch.autograd import Variable
+
+import torchvision.transforms as tfs
+from torch.utils.data import DataLoader, sampler
+from torchvision.datasets import MNIST
+
+import numpy as np
+
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+
+plt.rcParams['figure.figsize'] = (10.0, 8.0) # 设置画图的尺寸
+plt.rcParams['image.interpolation'] = 'nearest'
+plt.rcParams['image.cmap'] = 'gray'
+
+def show_images(images): # 定义画图工具
+    images = np.reshape(images, [images.shape[0], -1])
+    sqrtn = int(np.ceil(np.sqrt(images.shape[0])))
+    sqrtimg = int(np.ceil(np.sqrt(images.shape[1])))
+
+    fig = plt.figure(figsize=(sqrtn, sqrtn))
+    gs = gridspec.GridSpec(sqrtn, sqrtn)
+    gs.update(wspace=0.05, hspace=0.05)
+
+    for i, img in enumerate(images):
+        ax = plt.subplot(gs[i])
+        plt.axis('off')
+        ax.set_xticklabels([])
+        ax.set_yticklabels([])
+        ax.set_aspect('equal')
+        plt.imshow(img.reshape([sqrtimg,sqrtimg]))
+    return
+
+def preprocess_img(x):
+    x = tfs.ToTensor()(x)
+    return (x - 0.5) / 0.5
+
+def deprocess_img(x):
+    return (x + 1.0) / 2.0
+
+
+class ChunkSampler(sampler.Sampler): # 定义一个取样的函数
+    """Samples elements sequentially from some offset.
+    Arguments:
+        num_samples: # of desired datapoints
+        start: offset where we should start selecting from
+    """
+    def __init__(self, num_samples, start=0):
+        self.num_samples = num_samples
+        self.start = start
+
+    def __iter__(self):
+        return iter(range(self.start, self.start + self.num_samples))
+
+    def __len__(self):
+        return self.num_samples
+
+NUM_TRAIN = 50000
+NUM_VAL = 5000
+
+NOISE_DIM = 96
+batch_size = 128
+
+train_set = MNIST('./mnist', train=True, download=True, transform=preprocess_img)
+
+train_data = DataLoader(train_set, batch_size=batch_size, sampler=ChunkSampler(NUM_TRAIN, 0))
+
+val_set = MNIST('./mnist', train=True, download=True, transform=preprocess_img)
+
+val_data = DataLoader(val_set, batch_size=batch_size, sampler=ChunkSampler(NUM_VAL, NUM_TRAIN))
+
+
+imgs = deprocess_img(train_data.__iter__().__next__()[0].view(batch_size, 784)).numpy().squeeze() # 可视化图片效果
+show_images(imgs)
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