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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.
58 lines
2.2 KiB
Python
58 lines
2.2 KiB
Python
import numpy as np
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from tinygrad.tensor import Tensor
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from tinygrad.helpers import trange, Context
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from tinygrad.engine.jit import TinyJit
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def train(model, X_train, Y_train, optim, steps, BS=128, lossfn=lambda out,y: out.sparse_categorical_crossentropy(y),
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transform=lambda x: x, target_transform=lambda x: x, noloss=False, allow_jit=True):
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def train_step(x, y):
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# network
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out = model.forward(x) if hasattr(model, 'forward') else model(x)
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loss = lossfn(out, y)
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optim.zero_grad()
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loss.backward()
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if noloss: del loss
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optim.step()
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if noloss: return (None, None)
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cat = out.argmax(axis=-1)
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accuracy = (cat == y).mean()
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return loss.realize(), accuracy.realize()
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if allow_jit: train_step = TinyJit(train_step)
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with Context(TRAINING=1):
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losses, accuracies = [], []
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for i in (t := trange(steps, disable=None)):
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samp = np.random.randint(0, X_train.shape[0], size=(BS))
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x = Tensor(transform(X_train[samp]))
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y = Tensor(target_transform(Y_train[samp]))
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loss, accuracy = train_step(x, y)
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# printing
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if not noloss:
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loss, accuracy = loss.numpy(), accuracy.numpy()
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losses.append(loss)
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accuracies.append(accuracy)
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t.set_description("loss %.2f accuracy %.2f" % (loss, accuracy))
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return [losses, accuracies]
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def evaluate(model, X_test, Y_test, num_classes=None, BS=128, return_predict=False, transform=lambda x: x,
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target_transform=lambda y: y):
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@Context(TRAINING=0)
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def numpy_eval(Y_test, num_classes):
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Y_test_preds_out = np.zeros(list(Y_test.shape)+[num_classes])
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for i in trange((len(Y_test)-1)//BS+1, disable=None):
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x = Tensor(transform(X_test[i*BS:(i+1)*BS]))
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out = model.forward(x) if hasattr(model, 'forward') else model(x)
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Y_test_preds_out[i*BS:(i+1)*BS] = out.numpy()
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Y_test_preds = np.argmax(Y_test_preds_out, axis=-1)
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Y_test = target_transform(Y_test)
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return (Y_test == Y_test_preds).mean(), Y_test_preds
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if num_classes is None: num_classes = Y_test.max().astype(int)+1
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acc, Y_test_pred = numpy_eval(Y_test, num_classes)
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print("test set accuracy is %f" % acc)
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return (acc, Y_test_pred) if return_predict else acc
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