在 PyTorch 中移动 MNIST

请我喝杯咖啡☕

*我的帖子解释了移动 mnist。

movingmnist() 可以使用 moving mnist 数据集，如下所示：

*备忘录：

第一个参数是 root（必需类型：str 或 pathlib.path）。 *绝对或相对路径都是可能的。第二个参数是 split(optional-default:none-type:str): *备注：没有，可以设置“train”或“test”。如果为 none，则返回每个视频的所有 20 帧（图像），忽略 split_ratio。第三个参数是 split_ratio(optional-default:10-type:int): *备注：如果 split 为“train”，则返回 data[:, :split_ratio]。如果 split 为“test”，则返回 data[:, split_ratio:]。如果 split 为 none，则忽略它。忽略 split_ratio。第四个参数是transform(optional-default:none-type:callable)。第五个参数是 download(optional-default:false-type:bool):*备注：如果为 true，则数据集将从互联网下载到 root。如果为 true 并且数据集已下载，则将其提取。如果为 true 并且数据集已下载，则不会发生任何事情。如果数据集已经下载，则应该为 false，因为它速度更快。您可以从此处手动下载并提取数据集，例如数据/移动mnist/。

from torchvision.datasets import movingmnistall_data = movingmnist(    root="data")all_data = movingmnist(    root="data",    split=none,    split_ratio=10,    download=false,    transform=none)train_data = movingmnist(    root="data",    split="train")test_data = movingmnist(    root="data",    split="test")len(all_data), len(train_data), len(test_data)# (10000, 10000, 10000)len(all_data[0]), len(train_data[0]), len(test_data[0])# (20, 10, 10)all_data# dataset movingmnist#     number of datapoints: 10000#     root location: dataall_data.root# 'data'print(all_data.split)# noneall_data.split_ratio# 10all_data.download# print(all_data.transform)# nonefrom torchvision.datasets import movingmnistimport matplotlib.pyplot as pltplt.figure(figsize=(10, 3))plt.subplot(1, 3, 1)plt.title("all_data")plt.imshow(all_data[0].squeeze()[0])plt.subplot(1, 3, 2)plt.title("train_data")plt.imshow(train_data[0].squeeze()[0])plt.subplot(1, 3, 3)plt.title("test_data")plt.imshow(test_data[0].squeeze()[0])plt.show()

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from torchvision.datasets import movingmnistall_data = movingmnist(    root="data",    split=none)train_data = movingmnist(    root="data",    split="train")test_data = movingmnist(    root="data",    split="test")def show_images(data, main_title=none):    plt.figure(figsize=(10, 8))    plt.suptitle(t=main_title, y=1.0, fontsize=14)    for i, image in enumerate(data, start=1):        plt.subplot(4, 5, i)        plt.tight_layout(pad=1.0)        plt.title(i)        plt.imshow(image)    plt.show()show_images(data=all_data[0].squeeze(), main_title="all_data")show_images(data=train_data[0].squeeze(), main_title="train_data")show_images(data=test_data[0].squeeze(), main_title="test_data")

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from torchvision.datasets import movingmnistall_data = movingmnist(    root="data",    split=none)train_data = movingmnist(    root="data",    split="train")test_data = movingmnist(    root="data",    split="test")import matplotlib.pyplot as pltdef show_images(data, main_title=none):    plt.figure(figsize=(10, 8))    plt.suptitle(t=main_title, y=1.0, fontsize=14)    col = 5    for i, image in enumerate(data, start=1):        plt.subplot(4, 5, i)        plt.tight_layout(pad=1.0)        plt.title(i)        plt.imshow(image.squeeze()[0])        if i == col:            break    plt.show()show_images(data=all_data, main_title="all_data")show_images(data=train_data, main_title="train_data")show_images(data=test_data, main_title="test_data")

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from torchvision.datasets import movingmnistimport matplotlib.animation as animationall_data = movingmnist(    root="data")import matplotlib.pyplot as pltfrom ipython.display import htmlfigure, axis = plt.subplots()# ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ `artistanimation()` ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓images = []for image in all_data[0].squeeze():    images.append([axis.imshow(image)])ani = animation.artistanimation(fig=figure, artists=images,                                interval=100)# ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ `artistanimation()` ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑# ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ `funcanimation()` ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓# def animate(i):#     axis.imshow(all_data[0].squeeze()[i])## ani = animation.funcanimation(fig=figure, func=animate,#                               frames=20, interval=100)# ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ `funcanimation()` ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑# ani.save('result.gif') # save the animation as a `.gif` fileplt.ioff() # hide a useless image# ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ show animation ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓html(ani.to_jshtml()) # animation operator# html(ani.to_html5_video()) # animation video# ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ show animation ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑# ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ show animation ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓# plt.rcparams["animation.html"] = "jshtml" # animation operator# plt.rcparams["animation.html"] = "html5" # animation video# ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ show animation ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑

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from torchvision.datasets import MovingMNISTfrom ipywidgets import interact, IntSliderall_data = MovingMNIST(    root="data")import matplotlib.pyplot as pltfrom IPython.display import HTMLdef func(i):    plt.imshow(all_data[0].squeeze()[i])interact(func, i=(0, 19, 1))# interact(func, i=IntSlider(min=0, max=19, step=1, value=0))# ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ Set the start value ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑plt.show()

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