Sun Dec 3 15:23:00 CST 2023 inscode

.inscode

 run = "pip install -r requirements.txt;python main.py"
+language = "python"
 
 [packager]
 AUTO_PIP = true
@@ -9,4 +10,7 @@ PATH = "${VIRTUAL_ENV}/bin:${PATH}"
 PYTHONPATH = "$PYTHONHOME/lib/python3.10:${VIRTUAL_ENV}/lib/python3.10/site-packages"
 REPLIT_POETRY_PYPI_REPOSITORY = "http://mirrors.csdn.net.cn/repository/csdn-pypi-mirrors/simple"
 MPLBACKEND = "TkAgg"
-POETRY_CACHE_DIR = "/root/${PROJECT_DIR}/.cache/pypoetry"
\ No newline at end of file
+POETRY_CACHE_DIR = "/root/${PROJECT_DIR}/.cache/pypoetry"
+
+[debugger]
+program = "main.py"

main.py

@@ -21,11 +21,8 @@ class MyDataset(Dataset):
     def __getitem__(self, idx):
         tokens = self.data[idx].split()
         tokens = [self.tokenizer.get(token, self.tokenizer['<UNK>']) for token in tokens]
-        if len(tokens) < self.max_len:
-            tokens += [self.tokenizer['<PAD>']] * (self.max_len - len(tokens))
-        else:
-            tokens = tokens[:self.max_len]
-        return torch.tensor(tokens)
+        tokens += [self.tokenizer['<PAD>']] * (self.max_len - len(tokens))
+        return torch.tensor(tokens[:self.max_len])
     
 dataset = MyDataset(data, tokenizer, max_len)
 dataloader = DataLoader(dataset, batch_size=2, shuffle=True)
@@ -62,7 +59,7 @@ for epoch in range(10):
 # 测试模型
 test_data = ['hello', 'goodbye', 'pytorch']
 test_tokens = [[tokenizer.get(token, tokenizer['<UNK>']) for token in data.split()] for data in test_data]
-test_tokens = [torch.tensor(tokens) for tokens in test_tokens]
+test_tokens = [torch.tensor(tokens[:max_len]) for tokens in test_tokens]
 test_output = model(torch.stack(test_tokens))
 test_pred = torch.argmax(test_output, dim=-1)
 for i, data in enumerate(test_data):
@@ -71,7 +68,7 @@ for i, data in enumerate(test_data):
 # 应用模型
 input_data = 'hello'
 input_tokens = [tokenizer.get(token, tokenizer['<UNK>']) for token in input_data.split()]
-input_tokens = torch.tensor(input_tokens).unsqueeze(0)
+input_tokens = torch.tensor(input_tokens[:max_len]).unsqueeze(0)
 output = model(input_tokens)
 pred = torch.argmax(output, dim=-1)
-print(' '.join([k for k, v in tokenizer.items() if v == pred.item()]))
\ No newline at end of file
+print(' '.join([k for k, v in tokenizer.items() if v == pred.item()]))