mirror of
https://github.com/firestar5683/StarPilot.git
synced 2026-11-08 13:33:42 +08:00
firestar5683
22a8575199
Keep official openpilot ancestry and dependency licenses. Store resources directly in Git, remove LFS setup requirements, and leave the downloadable big model outside the source tree.
49 lines
2.0 KiB
Python
49 lines
2.0 KiB
Python
# model based off https://medium.com/data-science/going-beyond-99-mnist-handwritten-digits-recognition-cfff96337392
|
|
from typing import Callable
|
|
from tinygrad import Tensor, TinyJit, nn, GlobalCounters, function, Context
|
|
from tinygrad.helpers import getenv, colored, trange
|
|
from tinygrad.nn.datasets import mnist
|
|
|
|
class Model:
|
|
def __init__(self):
|
|
self.layers: list[Callable[[Tensor], Tensor]] = [
|
|
nn.Conv2d(1, 32, 5), Tensor.relu,
|
|
nn.Conv2d(32, 32, 5), Tensor.relu,
|
|
nn.BatchNorm(32), Tensor.max_pool2d,
|
|
nn.Conv2d(32, 64, 3), Tensor.relu,
|
|
nn.Conv2d(64, 64, 3), Tensor.relu,
|
|
nn.BatchNorm(64), Tensor.max_pool2d,
|
|
lambda x: x.flatten(1), nn.Linear(576, 10)]
|
|
|
|
@function
|
|
def __call__(self, x:Tensor) -> Tensor: return x.sequential(self.layers)
|
|
|
|
@TinyJit
|
|
@Context(TRAINING=1)
|
|
def train_step(self, X_train:Tensor, Y_train:Tensor) -> Tensor:
|
|
opt.zero_grad()
|
|
samples = Tensor.randint(getenv("BS", 512), high=X_train.shape[0])
|
|
loss = self(X_train[samples]).sparse_categorical_crossentropy(Y_train[samples]).backward()
|
|
return loss.realize(*opt.schedule_step())
|
|
|
|
@TinyJit
|
|
def get_test_acc(self, X_test:Tensor, Y_test:Tensor) -> Tensor: return (self(X_test).argmax(axis=1) == Y_test).mean()*100
|
|
|
|
if __name__ == "__main__":
|
|
X_train, Y_train, X_test, Y_test = mnist(fashion=getenv("FASHION"))
|
|
|
|
model = Model()
|
|
opt = (nn.optim.Muon if getenv("MUON") else nn.optim.SGD if getenv("SGD") else nn.optim.Adam)(nn.state.get_parameters(model))
|
|
|
|
test_acc = float('nan')
|
|
for i in (t:=trange(getenv("STEPS", 70))):
|
|
GlobalCounters.reset() # NOTE: this makes it nice for DEBUG=2 timing
|
|
loss = model.train_step(X_train, Y_train)
|
|
if i%10 == 9: test_acc = model.get_test_acc(X_test, Y_test).item()
|
|
t.set_description(f"loss: {loss.item():6.2f} test_accuracy: {test_acc:5.2f}%")
|
|
|
|
# verify eval acc
|
|
if target := getenv("TARGET_EVAL_ACC_PCT", 0.0):
|
|
if test_acc >= target and test_acc != 100.0: print(colored(f"{test_acc=} >= {target}", "green"))
|
|
else: raise ValueError(colored(f"{test_acc=} < {target}", "red"))
|