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myenv/bin/python

@@ -0,0 +1 @@
+python3

myenv/bin/python3

@@ -0,0 +1 @@
+/usr/bin/python3

myenv/bin/python3.10

@@ -0,0 +1 @@
+python3

myenv/lib64

@@ -0,0 +1 @@
+lib

myenv/pyvenv.cfg

@@ -0,0 +1,3 @@
+home = /usr/bin
+include-system-site-packages = false
+version = 3.10.12

read_data.py

@@ -0,0 +1,70 @@
+from torch.utils.data import Dataset, DataLoader
+import numpy as np
+from PIL import Image
+import os
+from torchvision import transforms
+from torch.utils.tensorboard import SummaryWriter
+from torchvision.utils import make_grid
+
+writer = SummaryWriter("logs")
+
+class MyData(Dataset):
+
+    def __init__(self, root_dir, image_dir, label_dir, transform):
+        self.root_dir = root_dir
+        self.image_dir = image_dir
+        self.label_dir = label_dir
+        self.label_path = os.path.join(self.root_dir, self.label_dir)
+        self.image_path = os.path.join(self.root_dir, self.image_dir)
+        self.image_list = os.listdir(self.image_path)
+        self.label_list = os.listdir(self.label_path)
+        self.transform = transform
+        # 因为label 和 Image文件名相同，进行一样的排序，可以保证取出的数据和label是一一对应的
+        self.image_list.sort()
+        self.label_list.sort()
+
+    def __getitem__(self, idx):
+        img_name = self.image_list[idx]
+        label_name = self.label_list[idx]
+        img_item_path = os.path.join(self.root_dir, self.image_dir, img_name)
+        label_item_path = os.path.join(self.root_dir, self.label_dir, label_name)
+        img = Image.open(img_item_path)
+
+        with open(label_item_path, 'r') as f:
+            label = f.readline()
+
+        # img = np.array(img)
+        img = self.transform(img)
+        sample = {'img': img, 'label': label}
+        return sample
+
+    def __len__(self):
+        assert len(self.image_list) == len(self.label_list)
+        return len(self.image_list)
+
+if __name__ == '__main__':
+    transform = transforms.Compose([transforms.Resize((256, 256)), transforms.ToTensor()])
+    root_dir = "dataset/train"
+    image_ants = "ants_image"
+    label_ants = "ants_label"
+    ants_dataset = MyData(root_dir, image_ants, label_ants, transform)
+    image_bees = "bees_image"
+    label_bees = "bees_label"
+    bees_dataset = MyData(root_dir, image_bees, label_bees, transform)
+    train_dataset = ants_dataset + bees_dataset
+
+    # transforms = transforms.Compose([transforms.Resize(256, 256)])
+    dataloader = DataLoader(train_dataset, batch_size=1, num_workers=2)
+
+    writer.add_image('error', train_dataset[119]['img'])
+    writer.close()
+    for i, j in enumerate(dataloader):
+         # imgs, labels = j
+         print(type(j))
+         print(i, j['img'].shape)
+         # writer.add_image("train_data_b2", make_grid(j['img']), i)
+    
+    writer.close()
+
+
+

venv/人工智能/作业/03.py

@@ -1,8 +1,9 @@
+#这些注释都是原来的错误代码。最后是找了AI 解决的（🤓）
 import torch
 from torchvision import datasets, transforms
 from torch.utils.data import DataLoader
 from torchvision.datasets import MNIST
-import matplotlib.pyplot as plt
+#import matplotlib.pyplot as plt #可有可无
 
 device = 'cuda:0' 
 class Net(torch.nn.Module):
@@ -31,7 +32,7 @@ def evaluate(test_data, net):
     n_total = 0
     with torch.no_grad():
         for (x, y) in test_data:
-            # 将数据发送到GPU
+            # 将数据发送到GPU(这个02是没有的)
             x = x.view(-1, 28 * 28).to(device)
             y = y.to(device)
             output = net(x)
@@ -57,15 +58,15 @@ def main():
     print('initial accuracy', evaluate(test_data, net))
     optimizer = torch.optim.Adam(net.parameters())
     
-    for epoch in range(3):
+    for epoch in range(100):
         for (x, y) in train_data:
-            # 将数据发送到GPU
+            # 将数据发送到GPU(这个02是没有的)
             x = x.view(-1, 28 * 28).to(device)
             y = y.to(device)
             net.zero_grad()
             output = net(x)
             loss = torch.nn.functional.nll_loss(output, y)
-            loss.backward()
+            loss.backward()#02没有这个，导致正确率也没有提高
             optimizer.step()
         print('epoch', epoch, 'accuracy', evaluate(test_data, net))
     torch.save(net,'./model.pth')

venv_2/bin/python

@@ -0,0 +1 @@
+python3

venv_2/bin/python3

@@ -0,0 +1 @@
+/home/mtftau-5/workplace/.venv/bin/python3

venv_2/bin/python3.10

@@ -0,0 +1 @@
+python3

venv_2/lib64

@@ -0,0 +1 @@
+lib

venv_2/pyvenv.cfg

@@ -0,0 +1,3 @@
+home = /home/mtftau-5/workplace/.venv/bin
+include-system-site-packages = false
+version = 3.10.12