【colab pytorch】数据预处理
时间:2022-07-23
本文章向大家介绍【colab pytorch】数据预处理,主要内容包括其使用实例、应用技巧、基本知识点总结和需要注意事项,具有一定的参考价值,需要的朋友可以参考一下。
1、计算数据集的均值和方差
import os
import cv2
import numpy as np
from torch.utils.data import Dataset
from PIL import Image
def compute_mean_and_std(dataset):
# 输入PyTorch的dataset,输出均值和标准差
mean_r = 0
mean_g = 0
mean_b = 0
for img, _ in dataset:
img = np.asarray(img) # change PIL Image to numpy array
mean_b += np.mean(img[:, :, 0])
mean_g += np.mean(img[:, :, 1])
mean_r += np.mean(img[:, :, 2])
mean_b /= len(dataset)
mean_g /= len(dataset)
mean_r /= len(dataset)
diff_r = 0
diff_g = 0
diff_b = 0
N = 0
for img, _ in dataset:
img = np.asarray(img)
diff_b += np.sum(np.power(img[:, :, 0] - mean_b, 2))
diff_g += np.sum(np.power(img[:, :, 1] - mean_g, 2))
diff_r += np.sum(np.power(img[:, :, 2] - mean_r, 2))
N += np.prod(img[:, :, 0].shape)
std_b = np.sqrt(diff_b / N)
std_g = np.sqrt(diff_g / N)
std_r = np.sqrt(diff_r / N)
mean = (mean_b.item() / 255.0, mean_g.item() / 255.0, mean_r.item() / 255.0)
std = (std_b.item() / 255.0, std_g.item() / 255.0, std_r.item() / 255.0)
return mean, std
2、得到视频数据的基本信息
import cv2
video = cv2.VideoCapture(mp4_path)
height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))
width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))
num_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
fps = int(video.get(cv2.CAP_PROP_FPS))
video.release()
3、每段采样一帧视频
K = self._num_segments
if is_train:
if num_frames > K:
# Random index for each segment.
frame_indices = torch.randint(
high=num_frames // K, size=(K,), dtype=torch.long)
frame_indices += num_frames // K * torch.arange(K)
else:
frame_indices = torch.randint(
high=num_frames, size=(K - num_frames,), dtype=torch.long)
frame_indices = torch.sort(torch.cat((
torch.arange(num_frames), frame_indices)))[0]
else:
if num_frames > K:
# Middle index for each segment.
frame_indices = num_frames / K // 2
frame_indices += num_frames // K * torch.arange(K)
else:
frame_indices = torch.sort(torch.cat((
torch.arange(num_frames), torch.arange(K - num_frames))))[0]
assert frame_indices.size() == (K,)
return [frame_indices[i] for i in range(K)]
4、常用训练和验证数据预处理
train_transform = torchvision.transforms.Compose([
torchvision.transforms.RandomResizedCrop(size=224,
scale=(0.08, 1.0)),
torchvision.transforms.RandomHorizontalFlip(),
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize(mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225)),
])
val_transform = torchvision.transforms.Compose([
torchvision.transforms.Resize(256),
torchvision.transforms.CenterCrop(224),
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize(mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225)),
])
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