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remove np from beautiful_cifar (#10988)

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* remove np from beautiful_cifar

* remove np from cifar

* rename variable and rename tensor.arrange to just tensor.randperm

---------

Co-authored-by: chenyu <chenyu@fastmail.com>
This commit is contained in:
NoahKusaba
2025-08-29 19:34:16 -04:00
committed by GitHub
parent cf9d8c8142
commit 0838021753
1 changed files with 4 additions and 6 deletions
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10
examples/beautiful_cifar.py
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@@ -2,7 +2,6 @@ import time
start_tm = time.perf_counter()
import math
from typing import Tuple, cast
import numpy as np
from tinygrad import Tensor, nn, GlobalCounters, TinyJit, dtypes, Device
from tinygrad.helpers import partition, trange, getenv, Context
from extra.lr_scheduler import OneCycleLR
@@ -150,13 +149,12 @@ if __name__ == "__main__":
acc.append((out.argmax(-1) == Y).sum() / eval_batchsize)
return Tensor.stack(*loss).mean() / (batchsize*loss_batchsize_scaler), Tensor.stack(*acc).mean()
np.random.seed(1337)
Tensor.manual_seed(1337)
num_train_samples = X_train.shape[0]
for epoch in range(math.ceil(hyp['misc']['train_epochs'])):
# TODO: move to tinygrad
gst = time.perf_counter()
idxs = np.arange(X_train.shape[0])
np.random.shuffle(idxs)
tidxs = Tensor(idxs, dtype='int')[:num_steps_per_epoch*batchsize].reshape(num_steps_per_epoch, batchsize) # NOTE: long doesn't fold
tidxs = Tensor.randperm(num_train_samples, dtype='int')[:num_steps_per_epoch*batchsize].reshape(num_steps_per_epoch, batchsize)
train_loss:float = 0
for epoch_step in (t:=trange(num_steps_per_epoch)):
st = time.perf_counter()