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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.
44 lines
2.3 KiB
Python
44 lines
2.3 KiB
Python
import os, gzip, tarfile, pickle
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import numpy as np
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from tinygrad import Tensor, dtypes
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from tinygrad.helpers import fetch
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def fetch_mnist(tensors=False):
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parse = lambda file: np.frombuffer(gzip.open(file).read(), dtype=np.uint8).copy()
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BASE_URL = "https://storage.googleapis.com/cvdf-datasets/mnist/" # http://yann.lecun.com/exdb/mnist/ lacks https
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X_train = parse(fetch(f"{BASE_URL}train-images-idx3-ubyte.gz"))[0x10:].reshape((-1, 28*28)).astype(np.float32)
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Y_train = parse(fetch(f"{BASE_URL}train-labels-idx1-ubyte.gz"))[8:].astype(np.int8)
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X_test = parse(fetch(f"{BASE_URL}t10k-images-idx3-ubyte.gz"))[0x10:].reshape((-1, 28*28)).astype(np.float32)
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Y_test = parse(fetch(f"{BASE_URL}t10k-labels-idx1-ubyte.gz"))[8:].astype(np.int8)
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if tensors: return Tensor(X_train).reshape(-1, 1, 28, 28), Tensor(Y_train), Tensor(X_test).reshape(-1, 1, 28, 28), Tensor(Y_test)
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else: 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():
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X_train = Tensor.empty(50000, 3*32*32, device=f'disk:/tmp/cifar_train_x', dtype=dtypes.uint8)
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Y_train = Tensor.empty(50000, device=f'disk:/tmp/cifar_train_y', dtype=dtypes.int64)
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X_test = Tensor.empty(10000, 3*32*32, device=f'disk:/tmp/cifar_test_x', dtype=dtypes.uint8)
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Y_test = Tensor.empty(10000, device=f'disk:/tmp/cifar_test_y', dtype=dtypes.int64)
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if not os.path.isfile("/tmp/cifar_extracted"):
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def _load_disk_tensor(X, Y, db_list):
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idx = 0
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for db in db_list:
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x, y = db[b'data'], np.array(db[b'labels'])
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assert x.shape[0] == y.shape[0]
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X[idx:idx+x.shape[0]].assign(x)
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Y[idx:idx+x.shape[0]].assign(y)
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idx += x.shape[0]
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assert idx == X.shape[0] and X.shape[0] == Y.shape[0]
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print("downloading and extracting CIFAR...")
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fn = fetch('https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz')
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tt = tarfile.open(fn, mode='r:gz')
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_load_disk_tensor(X_train, Y_train, [pickle.load(tt.extractfile(f'cifar-10-batches-py/data_batch_{i}'), encoding="bytes") for i in range(1,6)])
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_load_disk_tensor(X_test, Y_test, [pickle.load(tt.extractfile('cifar-10-batches-py/test_batch'), encoding="bytes")])
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open("/tmp/cifar_extracted", "wb").close()
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return X_train, Y_train, X_test, Y_test
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