efee1712aa
* fix.. speed_limit error... * draw tpms settings. * fix.. traffic light stopping only.. * fix.. waze cam * fix.. waze... * add setting (Enable comma connect ) * auto detect LFA2 * fix.. cruisespeed1 * vff2 driving model. * fix.. * agnos 12.3 * fix.. * ff * ff * test * ff * fix.. drawTurnInfo.. * Update drive_helpers.py * fix.. support eng voice eng sounds fix settings... english fix.. mph.. fix.. roadlimit speed bug.. * new vff model.. 250608 * fix soundd.. * fix safe exit speed.. * fix.. sounds. * fix.. radar timeStep.. * KerryGold model * Update drive_helpers.py * fix.. model. * fix.. * fix.. * Revert "fix.." This reverts commit b09ec459afb855c533d47fd7e8a1a6b1a09466e7. * Revert "fix.." This reverts commit 290bec6b83a4554ca232d531a911edccf94a2156. * fix esim * add more acc table. 10kph * kg update.. * fix cruisebutton mode3 * test atc..cond. * fix.. canfd * fix.. angle control limit
67 lines
2.2 KiB
Python
67 lines
2.2 KiB
Python
import numpy as np
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import math
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import random
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np.set_printoptions(suppress=True)
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from copy import deepcopy
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from tinygrad.helpers import getenv, colored
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from tinygrad.tensor import Tensor
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from tinygrad.nn.state import get_parameters, get_state_dict, safe_save, safe_load, load_state_dict
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from tinygrad.engine.search import bufs_from_lin, actions, get_kernel_actions
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from tinygrad.codegen.heuristic import hand_coded_optimizations
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from extra.optimization.helpers import load_worlds, ast_str_to_lin, lin_to_feats, time_linearizer
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from extra.optimization.extract_policynet import PolicyNet
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from extra.optimization.pretrain_valuenet import ValueNet
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VALUE = getenv("VALUE")
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if __name__ == "__main__":
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if VALUE:
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net = ValueNet()
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load_state_dict(net, safe_load("/tmp/valuenet.safetensors"))
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else:
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net = PolicyNet()
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load_state_dict(net, safe_load("/tmp/policynet.safetensors"))
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ast_strs = load_worlds()
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# real randomness
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random.seed()
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random.shuffle(ast_strs)
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wins = 0
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for ep_num,ast_str in enumerate(ast_strs):
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print("\nEPISODE", ep_num, f"win {wins*100/max(1,ep_num):.2f}%")
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lin = ast_str_to_lin(ast_str)
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rawbufs = bufs_from_lin(lin)
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linhc = deepcopy(lin)
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linhc.applied_opts(hand_coded_optimizations(linhc))
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tmhc = time_linearizer(linhc, rawbufs)
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print(f"{tmhc*1e6:10.2f} HC ", linhc.colored_shape())
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pred_time = float('nan')
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tm = float('inf')
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while 1:
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if VALUE:
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acts,feats = [], []
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for k,v in get_kernel_actions(lin).items():
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acts.append(k)
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feats.append(lin_to_feats(v))
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preds = net(Tensor(feats))
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pred_time = math.exp(preds.numpy().min())
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act = acts[preds.numpy().argmin()]
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else:
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probs = net(Tensor([lin_to_feats(lin)]))
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dist = probs.exp().numpy()
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act = dist.argmax()
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if act == 0: break
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try:
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lin.apply_opt(actions[act-1])
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except Exception:
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print("FAILED")
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break
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tm = time_linearizer(lin, rawbufs)
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print(f"{tm*1e6:10.2f} {pred_time*1e6:10.2f}", lin.colored_shape())
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print(f"{colored('BEAT', 'green') if tm < tmhc else colored('lost', 'red')} hand coded {tmhc/tm:5.2f}x")
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wins += int(tm < tmhc) |