Create setup_project.py

setup_project.py

@@ -0,0 +1,142 @@
+import os
+import json
+import torch
+import random
+
+# Define the directory structure
+project_root = 'project_root'
+model_dir = os.path.join(project_root, 'model')
+tokenizer_dir = os.path.join(model_dir, 'tokenizer')
+scripts_dir = os.path.join(project_root, 'scripts')
+
+# Create directories
+os.makedirs(tokenizer_dir, exist_ok=True)
+os.makedirs(scripts_dir, exist_ok=True)
+
+# Step 2: Create config.json
+config = {
+    "model_type": "my_model_type",
+    "input_size": 100,
+    "hidden_size": 64,
+    "output_size": 10,
+    "num_layers": 1,
+    "dropout": 0.2
+}
+
+with open(os.path.join(model_dir, 'config.json'), 'w') as f:
+    json.dump(config, f)
+
+# Step 3: Create a sample pytorch_model.bin
+class SampleModel(torch.nn.Module):
+    def __init__(self):
+        super(SampleModel, self).__init__()
+        self.linear = torch.nn.Linear(100, 10)
+
+    def forward(self, x):
+        return self.linear(x)
+
+# Initialize and save the model weights
+model = SampleModel()
+torch.save(model.state_dict(), os.path.join(model_dir, 'pytorch_model.bin'))
+
+# Step 4: Create vocab.txt for tokenizer
+vocab = ['hello', 'world', 'my', 'model', 'tokenization', 'is', 'important']
+vocab_file_path = os.path.join(tokenizer_dir, 'vocab.txt')
+with open(vocab_file_path, 'w') as f:
+    for token in vocab:
+        f.write(f"{token}\n")
+
+# Step 5: Create tokenizer.json
+tokenizer_config = {
+    "vocab_size": len(vocab),
+    "do_lower_case": True,
+    "tokenizer_type": "MyTokenizer"
+}
+with open(os.path.join(tokenizer_dir, 'tokenizer.json'), 'w') as f:
+    json.dump(tokenizer_config, f)
+
+# Step 6: Create train.py
+train_script = """import torch
+import torch.nn as nn
+import torch.optim as optim
+
+class SampleModel(nn.Module):
+    def __init__(self):
+        super(SampleModel, self).__init__()
+        self.linear = nn.Linear(100, 10)
+
+    def forward(self, x):
+        return self.linear(x)
+
+def train():
+    model = SampleModel()
+    criterion = nn.CrossEntropyLoss()
+    optimizer = optim.Adam(model.parameters(), lr=0.001)
+    
+    # Sample data
+    inputs = torch.randn(100, 100)  # 100 samples
+    targets = torch.randint(0, 10, (100,))  # 100 random labels
+    
+    # Training loop (simplified)
+    for epoch in range(5):  # 5 epochs
+        optimizer.zero_grad()
+        outputs = model(inputs)
+        loss = criterion(outputs, targets)
+        loss.backward()
+        optimizer.step()
+        print(f"Epoch {epoch+1}, Loss: {loss.item():.4f}")
+
+if __name__ == "__main__":
+    train()
+"""
+
+with open(os.path.join(scripts_dir, 'train.py'), 'w') as f:
+    f.write(train_script)
+
+# Step 7: Create inference.py
+inference_script = """import torch
+import torch.nn as nn
+
+class SampleModel(nn.Module):
+    def __init__(self):
+        super(SampleModel, self).__init__()
+        self.linear = nn.Linear(100, 10)
+
+    def forward(self, x):
+        return self.linear(x)
+
+def inference(input_data):
+    model = SampleModel()
+    model.load_state_dict(torch.load('model/pytorch_model.bin'))
+    model.eval()
+    with torch.no_grad():
+        output = model(input_data)
+    return output
+
+if __name__ == "__main__":
+    # Sample inference
+    input_data = torch.randn(1, 100)  # Single sample
+    output = inference(input_data)
+    print(output)
+"""
+
+with open(os.path.join(scripts_dir, 'inference.py'), 'w') as f:
+    f.write(inference_script)
+
+# Step 8: Create utils.py
+utils_script = """def load_model(model_path):
+    import torch
+    model = SampleModel()
+    model.load_state_dict(torch.load(model_path))
+    model.eval()
+    return model
+
+def preprocess_input(input_data):
+    # Add input preprocessing logic here
+    return input_data
+"""
+
+with open(os.path.join(scripts_dir, 'utils.py'), 'w') as f:
+    f.write(utils_script)
+
+print("Project structure created successfully!")