小土堆pytorch学习笔记002
目录
1、TensorBoard的使用
（1）显示坐标：
（2）显示图片：
2、Transform的使用
3、常见的Transforms
（1）#ToTensor()
（2）# Normalize()
（3）#Resize()
（4）#Compose()
4、总结：
1、TensorBoard的使用
（1）显示坐标：
from torch.utils.tensorboard import SummaryWriter
import numpy as np
from PIL import Image
writer = SummaryWriter("logs") # 写入的位置 log_dir = 'logs
# writer.add_image()
"""
y = x
"""
for i in range(100):
writer.add_scalar("y=x", i, i)
writer.close()
运行：
浏览器运行结果：
# 运行
>tensorboard --logdir=logs #会打开http://localhost:6006/
>tensorboard --logdir=logs --port=6007 （#增加端口设置）（2）显示图片：
from torch.utils.tensorboard import SummaryWriter
import numpy as np
from PIL import Image
writer = SummaryWriter("logs") # 写入的位置 log_dir = 'logs
"""
参数：
def add_image(
self, tag, img_tensor, global_step=None, walltime=None, dataformats="CHW"
):
其中：
img_tensor (torch.Tensor, numpy.ndarray, or string/blobname): Image data
"""
image_path = "./hymenoptera_data/train/ants/0013035.jpg"
img_PIL = Image.open(image_path)
img_array = np.array(img_PIL)
print(type(img_array))
print(img_array.shape) # (512, 768, 3) H高度，W宽度，C通道数
writer.add_image("test", img_array, 5, dataformats="HWC") # 从PIL到numpy，需要在add_image()中指定shape中每一个数字/维表示的含义。
"""
y = x
参数：
def add_scalar(
self,
tag, # 标签
scalar_value, # y轴
global_step=None, # x轴
walltime=None,
new_style=False,
double_precision=False,
):
"""
for i in range(100):
writer.add_scalar("y=2x", 3*i, i)
writer.close()
结果展示：
2、Transform的使用
（1）transforms该如何使用（python）
from PIL import Image
from torchvision import transforms
# python的用法 ==>tensor
"""
通过 transforms.ToTensors去解决两个问题：
1、transforms该如何使用（python）
2、为什么我们需要Tensor数据类型
"""
img_path = "./dataset/Snipaste_01.jpg"
img = Image.open(img_path)
print(img)
# 1、transforms该如何使用（python）
tensor_trans = transforms.ToTensor
tensor_img = tensor_trans().__call__(img) # 将img转换成 tensor_img的图片
print(tensor_img)
运行结果：
（2）为什么我们需要Tensor数据类型 （在之后的神经网络会用到，前向传播、反向传播等）
from PIL import Image
from torch.utils.tensorboard import SummaryWriter
from torchvision import transforms
# python的用法 ==>tensor
"""
通过 transforms.ToTensors去解决两个问题：
1、transforms该如何使用（python）
2、为什么我们需要Tensor数据类型
"""
img_path = "./dataset/Snipaste_01.jpg"
img = Image.open(img_path)
print(img)
# 1、transforms该如何使用（python）
tensor_trans = transforms.ToTensor
tensor_img = tensor_trans().__call__(img) # 将img转换成 tensor_img的图片
print(tensor_img)
writer = SummaryWriter("logs_")
writer.add_image("Tensor_img", tensor_img)
writer.close()
运行结果截图：
3、常见的Transforms
（1）#ToTensor()
（2）# Normalize()
from PIL import Image
from torch.utils.tensorboard import SummaryWriter
from torchvision import transforms
img_path = "./dataset/Snipaste_01.jpg"
img = Image.open(img_path)
print(img)
writer = SummaryWriter("logs__")
# ToTensor()
trans_totensor = transforms.ToTensor()
img_tensor = trans_totensor(img)
writer.add_image("ToTensor", img_tensor)
# Normalize
"""
output[channel] = (input[channel] - mean[channel]) / std[channel]
"""
print(img_tensor[0][0][0])
trans_norm = transforms.Normalize([6,3,5],[3,2,1])
img_norm = trans_norm(img_tensor)
print(img_norm[0][0][0])
writer.add_image("Normalize", img_norm)
writer.close()
Noumalize() 和ToTensor()运行结果：
（3）#Resize()
（4）#Compose()
from PIL import Image
from torch.utils.tensorboard import SummaryWriter
from torchvision import transforms
img_path = "./dataset/Snipaste_01.jpg"
img = Image.open(img_path)
print(img)
writer = SummaryWriter("logs__")
# ToTensor()
trans_totensor = transforms.ToTensor()
img_tensor = trans_totensor(img)
writer.add_image("ToTensor", img_tensor)
# Normalize
"""
output[channel] = (input[channel] - mean[channel]) / std[channel]
"""
print(img_tensor[0][0][0])
trans_norm = transforms.Normalize([6,3,5],[3,2,1])
img_norm = trans_norm(img_tensor)
print(img_norm[0][0][0])
writer.add_image("Normalize", img_norm)
# Resize()
print(img.size)
trans_resize = transforms.Resize((512, 512))
# img PIL -> resize ->img_resize PIL
img_resize = trans_resize(img)
print(img_resize)
# img_resize PIL -> totensor -> img_resize tensor
img_resize = trans_totensor(img_resize)
writer.add_image("Resize", img_resize)
#Compose --resize -2
trans_resize_2 = transforms.Resize(512)
trans_compose = transforms.Compose([trans_resize_2, trans_totensor])
img_resize_2 = trans_compose(img)
writer.add_image("Resize_compose", img_resize_2, 1)
writer.close()
4、总结：
①关注输入和输出类型。
②多看官方文档。
③关注方法需要哪些参数
④查看数据类型 type( xxx)