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best_gen13.pt: PyTorch checkpoint (1083.0 MB)1import torch
2from aesop.models.policy import AesopPolicy
3
4# Load checkpoint
5checkpoint = torch.load("warm_up_best.pt", map_location="cpu")
6model = AesopPolicy()
7model.load_state_dict(checkpoint['model_state_dict'])1{
2 "stage": "warm_up",
3 "keys": [
4 "epoch",
5 "global_step",
6 "best_val_acc",
7 "model_state_dict",
8 "wandb_run_id",
9 "random_seed",
10 "random_state",
11 "numpy_random_state",
12 "torch_rng_state",
13 "global_moving_avg",
14 "global_moving_std",
15 "optimizer_0_state_dict",
16 "optimizer_1_state_dict",
17 "optimizer_2_state_dict",
18 "cuda_rng_state",
19 "scaler_state_dict",
20 "trainer_state_dict",
21 "metrics"
22 ],
23 "epoch": 3,
24 "best_val_acc": 0.27459016393442626,
25 "model_architecture": {
26 "is_valid": true,
27 "components": [
28 {
29 "name": "shared_mlp",
30 "found_keys": 3,
31 "total_expected": 3
32 },
33 {
34 "name": "tactic_heads",
35 "found_keys": 2,
36 "total_expected": 2
37 },
38 {
39 "name": "lemma_heads",
40 "found_keys": 2,
41 "total_expected": 2
42 },
43 {
44 "name": "config_heads",
45 "found_keys": 2,
46 "total_expected": 2
47 },
48 {
49 "name": "base_encoder",
50 "found_keys": 280,
51 "total_expected": "variable"
52 }
53 ],
54 "warnings": [],
55 "errors": []
56 },
57 "file_size_mb": 1083.0014581680298,
58 "files_description": "- `best_gen13.pt`: PyTorch checkpoint (1083.0 MB)"
59}