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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.
57 lines
2.3 KiB
Python
57 lines
2.3 KiB
Python
# model based off https://towardsdatascience.com/going-beyond-99-mnist-handwritten-digits-recognition-cfff96337392
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from typing import List, Callable
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from tinygrad import Tensor, TinyJit, nn, GlobalCounters, Device, Context
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from tinygrad.helpers import getenv, colored, trange
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from tinygrad.nn.datasets import mnist
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GPUS = tuple(f'{Device.DEFAULT}:{i}' for i in range(getenv("GPUS", 2)))
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class Model:
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def __init__(self):
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self.layers: List[Callable[[Tensor], Tensor]] = [
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nn.Conv2d(1, 32, 5), Tensor.relu,
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nn.Conv2d(32, 32, 5), Tensor.relu,
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nn.BatchNorm2d(32), Tensor.max_pool2d,
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nn.Conv2d(32, 64, 3), Tensor.relu,
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nn.Conv2d(64, 64, 3), Tensor.relu,
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nn.BatchNorm2d(64), Tensor.max_pool2d,
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lambda x: x.flatten(1), nn.Linear(576, 10)]
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def __call__(self, x:Tensor) -> Tensor: return x.sequential(self.layers)
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if __name__ == "__main__":
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X_train, Y_train, X_test, Y_test = mnist()
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# we shard the test data on axis 0
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X_test.shard_(GPUS, axis=0)
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Y_test.shard_(GPUS, axis=0)
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model = Model()
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for k, x in nn.state.get_state_dict(model).items(): x.to_(GPUS) # we put a copy of the model on every GPU
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opt = nn.optim.Adam(nn.state.get_parameters(model))
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@TinyJit
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def train_step() -> Tensor:
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with Context(TRAINING=1):
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opt.zero_grad()
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samples = Tensor.randint(getenv("BS", 512), high=X_train.shape[0])
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Xt, Yt = X_train[samples].shard_(GPUS, axis=0), Y_train[samples].shard_(GPUS, axis=0) # we shard the data on axis 0
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# TODO: this "gather" of samples is very slow. will be under 5s when this is fixed
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loss = model(Xt).sparse_categorical_crossentropy(Yt).backward()
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opt.step()
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return loss
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@TinyJit
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def get_test_acc() -> Tensor: return (model(X_test).argmax(axis=1) == Y_test).mean()*100
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test_acc = float('nan')
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for i in (t:=trange(getenv("STEPS", 70))):
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GlobalCounters.reset() # NOTE: this makes it nice for DEBUG=2 timing
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loss = train_step()
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if i%10 == 9: test_acc = get_test_acc().item()
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t.set_description(f"loss: {loss.item():6.2f} test_accuracy: {test_acc:5.2f}%")
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# verify eval acc
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if target := getenv("TARGET_EVAL_ACC_PCT", 0.0):
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if test_acc >= target: print(colored(f"{test_acc=} >= {target}", "green"))
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else: raise ValueError(colored(f"{test_acc=} < {target}", "red"))
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