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ebd72ff437* Split tests Split tests into "Test CPU" and "Test GPU". Add test flag "TEST_DEVICES" which is a comma separated list of devices: CPU,GPU,ANE * Run tests based on provided TEST_DEVICES flag By default will run all "CPU,GPU,ANE" * fix bad quote * Revert changes and use GPU=1 This is done through setting the default Tensor Device to Device.CPU of GPU=1 is set. Run GPU tests: GPU=1 pytest -s -v
81 lines
2.6 KiB
Python
81 lines
2.6 KiB
Python
#!/usr/bin/env python
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import os
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import unittest
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import numpy as np
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from tinygrad.tensor import Tensor
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import tinygrad.optim as optim
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from extra.training import train, evaluate
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from extra.utils import fetch, get_parameters
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# mnist loader
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def fetch_mnist():
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import gzip
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parse = lambda dat: np.frombuffer(gzip.decompress(dat), dtype=np.uint8).copy()
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X_train = parse(fetch("http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz"))[0x10:].reshape((-1, 28*28)).astype(np.float32)
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Y_train = parse(fetch("http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz"))[8:]
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X_test = parse(fetch("http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz"))[0x10:].reshape((-1, 28*28)).astype(np.float32)
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Y_test = parse(fetch("http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz"))[8:]
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return X_train, Y_train, X_test, Y_test
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# load the mnist dataset
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X_train, Y_train, X_test, Y_test = fetch_mnist()
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# create a model
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class TinyBobNet:
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def __init__(self):
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self.l1 = Tensor.uniform(784, 128)
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self.l2 = Tensor.uniform(128, 10)
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def parameters(self):
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return get_parameters(self)
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def forward(self, x):
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return x.dot(self.l1).relu().dot(self.l2).logsoftmax()
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# create a model with a conv layer
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class TinyConvNet:
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def __init__(self):
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# https://keras.io/examples/vision/mnist_convnet/
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conv = 3
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#inter_chan, out_chan = 32, 64
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inter_chan, out_chan = 8, 16 # for speed
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self.c1 = Tensor.uniform(inter_chan,1,conv,conv)
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self.c2 = Tensor.uniform(out_chan,inter_chan,conv,conv)
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self.l1 = Tensor.uniform(out_chan*5*5, 10)
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def parameters(self):
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return get_parameters(self)
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def forward(self, x):
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x = x.reshape(shape=(-1, 1, 28, 28)) # hacks
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x = x.conv2d(self.c1).relu().max_pool2d()
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x = x.conv2d(self.c2).relu().max_pool2d()
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x = x.reshape(shape=[x.shape[0], -1])
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return x.dot(self.l1).logsoftmax()
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class TestMNIST(unittest.TestCase):
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def test_conv(self):
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np.random.seed(1337)
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model = TinyConvNet()
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optimizer = optim.Adam(model.parameters(), lr=0.001)
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train(model, X_train, Y_train, optimizer, steps=200)
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assert evaluate(model, X_test, Y_test) > 0.95
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def test_sgd(self):
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np.random.seed(1337)
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model = TinyBobNet()
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optimizer = optim.SGD(model.parameters(), lr=0.001)
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train(model, X_train, Y_train, optimizer, steps=1000)
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assert evaluate(model, X_test, Y_test) > 0.95
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def test_rmsprop(self):
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np.random.seed(1337)
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model = TinyBobNet()
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optimizer = optim.RMSprop(model.parameters(), lr=0.0002)
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train(model, X_train, Y_train, optimizer, steps=1000)
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assert evaluate(model, X_test, Y_test) > 0.95
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if __name__ == '__main__':
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unittest.main()
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