Upload ai/utils/debug_agent.py with huggingface_hub

ai/utils/debug_agent.py

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+import os
+import sys
+
+import numpy as np
+import torch
+
+# Add project root to path
+sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+
+from ai.models.training_config import POLICY_SIZE
+from ai.training.train import AlphaNet
+
+
+def debug_model(model_path, data_path):
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    checkpoint = torch.load(model_path, map_location=device)
+
+    if isinstance(checkpoint, dict) and "model_state" in checkpoint:
+        state_dict = checkpoint["model_state"]
+    else:
+        state_dict = checkpoint
+
+    model = AlphaNet(policy_size=POLICY_SIZE).to(device)
+    model.load_state_dict(state_dict)
+    model.eval()
+
+    print(f"Loading data from {data_path}...")
+    data = np.load(data_path)
+    states = data["states"][:5]
+    true_policies = data["policies"][:5]
+
+    for i in range(len(states)):
+        state = torch.FloatTensor(states[i]).unsqueeze(0).to(device)
+        with torch.no_grad():
+            p_logits, v = model(state)
+            p_probs = torch.softmax(p_logits, dim=1)
+
+        print(f"\nSample {i}:")
+        print(f"Value prediction: {v.item():.4f}")
+
+        # Check Top-5 predicted actions
+        top_probs, top_actions = torch.topk(p_probs, 5)
+        print("Top 5 Predictions:")
+        for j in range(5):
+            print(f"  Action {top_actions[0][j].item()}: {top_probs[0][j].item():.1%}")
+
+        # Check ground truth Top-1
+        gt_action = np.argmax(true_policies[i])
+        gt_prob = true_policies[i][gt_action]
+        print(f"Ground Truth Action {gt_action} with weight {gt_prob:.1%}")
+
+
+if __name__ == "__main__":
+    debug_model("ai/models/alphanet_best.pt", "ai/data/data_batch_0.npz")