mirror of https://github.com/commaai/tinygrad.git
44 lines
2.0 KiB
Python
44 lines
2.0 KiB
Python
import random, gzip, tarfile, pickle
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import numpy as np
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from pathlib import Path
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from tinygrad.tensor import Tensor
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from tinygrad.helpers import dtypes
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from extra.utils import download_file
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def fetch_mnist():
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parse = lambda file: np.frombuffer(gzip.open(file).read(), dtype=np.uint8).copy()
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dirname = Path(__file__).parent.resolve()
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X_train = parse(dirname / "mnist/train-images-idx3-ubyte.gz")[0x10:].reshape((-1, 28*28)).astype(np.float32)
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Y_train = parse(dirname / "mnist/train-labels-idx1-ubyte.gz")[8:]
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X_test = parse(dirname / "mnist/t10k-images-idx3-ubyte.gz")[0x10:].reshape((-1, 28*28)).astype(np.float32)
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Y_test = parse(dirname / "mnist/t10k-labels-idx1-ubyte.gz")[8:]
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return X_train, Y_train, X_test, Y_test
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cifar_mean = [0.4913997551666284, 0.48215855929893703, 0.4465309133731618]
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cifar_std = [0.24703225141799082, 0.24348516474564, 0.26158783926049628]
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def fetch_cifar(shuffle=False):
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def _load_disk_tensor(sz, bs, db_list, path, shuffle=False):
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idx=0
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X, Y = None, None
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for db in db_list:
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x = db[b'data']
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y = np.array(db[b'labels'])
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order = list(range(0, len(y)))
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if shuffle: random.shuffle(order)
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if X is None:
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X = Tensor.empty(sz, *x.shape[1:], device=f'disk:/tmp/{path}'+'_x', dtype=dtypes.uint8)
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Y = Tensor.empty(sz, *y.shape[1:], device=f'disk:/tmp/{path}'+'_y', dtype=dtypes.int64)
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X[idx:idx+bs].assign(x[order,:])
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Y[idx:idx+bs].assign(y[order])
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idx += bs
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return X, Y
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fn = Path(__file__).parent.resolve() / "cifar-10-python.tar.gz"
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download_file('https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz', fn)
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tt = tarfile.open(fn, mode='r:gz')
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db = [pickle.load(tt.extractfile(f'cifar-10-batches-py/data_batch_{i}'), encoding="bytes") for i in range(1,6)]
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X_train, Y_train = _load_disk_tensor(50000, 10000, db, "cifar_train", shuffle=shuffle)
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db = [pickle.load(tt.extractfile('cifar-10-batches-py/test_batch'), encoding="bytes")]
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X_test, Y_test = _load_disk_tensor(10000, 10000, db, "cifar_test", shuffle=shuffle)
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return X_train, Y_train, X_test, Y_test
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