P10.Transforms的使用（二）
10.1為什么需要Tensor數據類型
1.通過transforms.ToTensor去看兩個問題：
①transforms的使用（pytorch)；②為什么需要ToTensor數據類型。
2.為什么需要ToTensor數據類型？
因為他包裝了我們反向神經網絡所需要的理論基礎的參數
點擊查看代碼
from PIL import Image
from torch.utils.tensorboard import SummaryWriter
from torchvision import transforms
# python的語法--tensor的數據類型
# 通過transforms.ToTensor去看兩個問題
# 1.transforms的使用（pytorch)
# 2.為什么需要ToTensor數據類型
img_path = "Data_antbee/hymenoptera_data/train/ants_img/0013035.jpg"
img = Image.open(img_path)
print(img)
# 1.transforms的使用（pytorch)
#img_tensor = transforms.ToTensor(img)錯誤，他是類不是函數，應該先創建實例再調用
tensor_trans = transforms.ToTensor() #創建實例
#ToTensor是類，相當于一個工具的模板（即工廠的模具），而tensor_trans相當于一個具體的工具
img_tensor = tensor_trans(img)
#調用實例（這一步相當于使用工具）
輸出結果如下：
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D:\anaconda3\envs\pytorch\python.exe D:/DeepLearning/Learn_torch/P9_Transform.py
<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=768x512 at 0x2A2DB8B74C0>
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3.ToTensor類
10.2Transforms使用
1.PIL轉換成tensor格式
之前是將PIL格式的照片轉換為numpy.array格式，這次是PIL轉換成tensor格式
2.通過add_image()將tensor格式的照片在tensorboard上打開
代碼如下：
點擊查看代碼
from PIL import Image
from torch.utils.tensorboard import SummaryWriter
from torchvision import transforms
img_path = "Data_antbee/hymenoptera_data/train/ants_img/0013035.jpg"
img = Image.open(img_path)
print(img)
tensor_trans = transforms.ToTensor()
img_tensor = tensor_trans(img)
writer = SummaryWriter("log")
writer.add_image("img_tensor",img_tensor)
print(img_tensor)
writer.close()
輸出結果如下：
點擊查看代碼
D:\anaconda3\envs\pytorch\python.exe D:/DeepLearning/Learn_torch/P9_Transform.py
<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=768x512 at 0x1A291B2A3A0>
tensor([[[0.3137, 0.3137, 0.3137, ..., 0.3176, 0.3098, 0.2980],
[0.3176, 0.3176, 0.3176, ..., 0.3176, 0.3098, 0.2980],
[0.3216, 0.3216, 0.3216, ..., 0.3137, 0.3098, 0.3020],
...,
[0.3412, 0.3412, 0.3373, ..., 0.1725, 0.3725, 0.3529],
[0.3412, 0.3412, 0.3373, ..., 0.3294, 0.3529, 0.3294],
[0.3412, 0.3412, 0.3373, ..., 0.3098, 0.3059, 0.3294]],
[[0.5922, 0.5922, 0.5922, ..., 0.5961, 0.5882, 0.5765],
[0.5961, 0.5961, 0.5961, ..., 0.5961, 0.5882, 0.5765],
[0.6000, 0.6000, 0.6000, ..., 0.5922, 0.5882, 0.5804],
...,
[0.6275, 0.6275, 0.6235, ..., 0.3608, 0.6196, 0.6157],
[0.6275, 0.6275, 0.6235, ..., 0.5765, 0.6275, 0.5961],
[0.6275, 0.6275, 0.6235, ..., 0.6275, 0.6235, 0.6314]],
[[0.9137, 0.9137, 0.9137, ..., 0.9176, 0.9098, 0.8980],
[0.9176, 0.9176, 0.9176, ..., 0.9176, 0.9098, 0.8980],
[0.9216, 0.9216, 0.9216, ..., 0.9137, 0.9098, 0.9020],
...,
[0.9294, 0.9294, 0.9255, ..., 0.5529, 0.9216, 0.8941],
[0.9294, 0.9294, 0.9255, ..., 0.8863, 1.0000, 0.9137],
[0.9294, 0.9294, 0.9255, ..., 0.9490, 0.9804, 0.9137]]])
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