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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.
61 lines
2.1 KiB
Python
61 lines
2.1 KiB
Python
# beautiful mnist in the new "one-shot" style
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# one realize in the whole graph
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# depends on:
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# - "big graph" UOp scheduling
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# - symbolic removal
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from examples.beautiful_mnist import Model
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from tinygrad import Tensor, nn, getenv, GlobalCounters, Variable, Context
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from tinygrad.nn.datasets import mnist
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from tinygrad.helpers import trange
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# STEPS=70 python3 examples/stunning_mnist.py
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# NOTE: it's broken with STACK=1, why?
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if __name__ == "__main__":
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X_train, Y_train, X_test, Y_test = mnist()
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print("*** got data")
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model = Model()
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print("*** got model")
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opt = nn.optim.Adam(nn.state.get_parameters(model))
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print("*** got optimizer")
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samples = Tensor.randint(getenv("STEPS", 10), getenv("BS", 512), high=X_train.shape[0])
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X_samp, Y_samp = X_train[samples], Y_train[samples]
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print("*** got samples")
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with Context(TRAINING=1):
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"""
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i = UOp.range(samples.shape[0]) # TODO: fix range function on UOp
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losses = model(X_samp[i]).sparse_categorical_crossentropy(Y_samp[i]).backward().contract(i)
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opt.schedule_steps(i)
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"""
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# TODO: this shouldn't be a for loop. something like: (contract is still up in the air)
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vi = Variable('i', 0, samples.shape[0]-1)
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losses = []
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for i in range(samples.shape[0]):
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vib = vi.bind(i)
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opt.zero_grad()
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losses.append(model(X_samp[vib]).sparse_categorical_crossentropy(Y_samp[vib]).backward())
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opt.schedule_step()
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# TODO: this stack currently breaks the "generator" aspect of losses. it probably shouldn't
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if getenv("STACK", 0): losses = Tensor.stack(*losses)
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print("*** scheduled training")
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# evaluate the model
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test_acc = ((model(X_test).argmax(axis=1) == Y_test).mean()*100)
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print("*** scheduled eval")
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# NOTE: there's no kernels run in the scheduling phase
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assert GlobalCounters.kernel_count == 0, "kernels were run during scheduling!"
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# only actually do anything at the end
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if getenv("LOSS", 1):
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for i in (t:=trange(len(losses))):
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GlobalCounters.reset()
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t.set_description(f"loss: {losses[i].item():6.2f}")
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if getenv("TEST", 1):
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print(f"test_accuracy: {test_acc.item():5.2f}%")
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