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
89 lines
2.6 KiB
Python
89 lines
2.6 KiB
Python
from tinygrad.codegen.kernel import Kernel
|
|
from tqdm import tqdm, trange
|
|
import math
|
|
import random
|
|
from tinygrad.tensor import Tensor
|
|
from tinygrad.nn import Linear
|
|
from tinygrad.nn.optim import Adam
|
|
from tinygrad.nn.state import get_parameters, get_state_dict, safe_save, safe_load, load_state_dict
|
|
|
|
# stuff needed to unpack a kernel
|
|
from tinygrad.uop.ops import LazyOp, TernaryOps, BinaryOps, UnaryOps, ReduceOps, BufferOps, MemBuffer, ConstBuffer
|
|
from tinygrad.dtype import dtypes
|
|
from tinygrad.shape.shapetracker import ShapeTracker
|
|
from tinygrad.shape.view import View
|
|
from tinygrad.uop.ops import Variable
|
|
inf, nan = float('inf'), float('nan')
|
|
from tinygrad.codegen.kernel import Opt, OptOps
|
|
|
|
from extra.optimization.helpers import lin_to_feats, MAX_DIMS
|
|
|
|
# NOTE: this is not real value of the state, it's just a prediction of the runtime
|
|
INNER = 512
|
|
class ValueNet:
|
|
def __init__(self, feats=240, out=1):
|
|
self.l1 = Linear(feats,INNER)
|
|
self.l2 = Linear(INNER,INNER)
|
|
self.l3 = Linear(INNER,INNER)
|
|
self.l4 = Linear(INNER,out)
|
|
def __call__(self, x):
|
|
x = self.l1(x).relu()
|
|
x = self.l2(x).relu()
|
|
x = self.l3(x).relu().dropout(0.8)
|
|
return self.l4(x)
|
|
|
|
if __name__ == "__main__":
|
|
net = ValueNet()
|
|
optim = Adam(get_parameters(net))
|
|
|
|
TEST_SIZE = 256
|
|
|
|
dset = open("/tmp/logtm").read().strip().split("\n")
|
|
random.seed(1337)
|
|
random.shuffle(dset)
|
|
|
|
X,Y = [], []
|
|
for i,x in enumerate(tqdm(dset)):
|
|
ast, opts, tms = eval(x)
|
|
lin = Kernel(ast)
|
|
for o in opts: lin.apply_opt(o)
|
|
if lin.shape_len >= MAX_DIMS: continue
|
|
if min(tms) == float('inf'): continue
|
|
X.append(lin_to_feats(lin))
|
|
Y.append([math.log(min(tms))])
|
|
print(f"got {len(X)} samples")
|
|
|
|
X_test,Y_test = Tensor(X[-TEST_SIZE:]), Tensor(Y[-TEST_SIZE:])
|
|
X,Y = X[:-TEST_SIZE], Y[:-TEST_SIZE]
|
|
|
|
def get_minibatch(X,Y,bs):
|
|
xs, ys = [], []
|
|
for _ in range(bs):
|
|
sel = random.randint(0, len(X)-1)
|
|
xs.append(X[sel])
|
|
ys.append(Y[sel])
|
|
return Tensor(xs), Tensor(ys)
|
|
|
|
Tensor.no_grad, Tensor.training = False, True
|
|
losses = []
|
|
test_losses = []
|
|
test_loss = float('inf')
|
|
for i in (t:=trange(2000)):
|
|
x,y = get_minibatch(X,Y,bs=256)
|
|
out = net(x)
|
|
loss = (out-y).square().mean()
|
|
optim.zero_grad()
|
|
loss.backward()
|
|
optim.step()
|
|
t.set_description(f"loss {loss.numpy():7.2f}, test loss {test_loss:7.2f}")
|
|
losses.append(loss.numpy().item())
|
|
test_losses.append(test_loss)
|
|
if i % 10: test_loss = (net(X_test)-Y_test).square().mean().numpy().item()
|
|
|
|
safe_save(get_state_dict(net), "/tmp/valuenet.safetensors")
|
|
|
|
import matplotlib.pyplot as plt
|
|
plt.plot(losses[200:])
|
|
plt.plot(test_losses[200:])
|
|
plt.show()
|