53 lines
1.5 KiB
Python
53 lines
1.5 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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if 'IMAGE' not in os.environ:
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os.environ['IMAGE'] = '2'
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os.environ['GPU'] = '1'
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os.environ['OPT'] = '2'
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from tinygrad.tensor import Tensor
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from tinygrad.nn import Conv2d
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Tensor.no_grad = True
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class TestImage(unittest.TestCase):
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def test_create_image(self):
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t = Tensor.ones(128, 128, 1)
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t = t.reshape(128, 32, 4) + 3
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t.realize()
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np.testing.assert_array_equal(t.numpy(), np.ones((128,32,4))*4)
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def test_sum_image(self):
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t1 = Tensor.ones(16, 16, 1).reshape(16, 4, 4) + 3
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t1.realize()
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t1 = t1.sum()
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t1.realize()
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assert t1.numpy() == 16*4*4*4, f"got {t1.numpy()}"
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def test_add_image(self):
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t1 = Tensor.ones(16, 16, 1).reshape(16, 4, 4) + 3
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t2 = Tensor.ones(16, 16, 1).reshape(16, 4, 4) + 4
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t1.realize()
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t2.realize()
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t3 = t1 + t2
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t3.realize()
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np.testing.assert_array_equal(t3.numpy(), np.ones((16,4,4))*9)
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def test_padded_conv(self):
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bs, in_chans, out_chans = 1,12,32
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tiny_conv = Conv2d(in_chans, out_chans, 3, bias=None, padding=1)
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tiny_dat = Tensor.ones(bs, 12, 64, 128)
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tiny_conv(tiny_dat).realize()
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def test_op_conv(self):
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bs, in_chans, out_chans = 1,12,32
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tiny_conv = Conv2d(in_chans, out_chans, 3, bias=None, padding=1)
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tiny_dconv = Conv2d(out_chans, out_chans, 1, bias=None, padding=0)
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tiny_dat = Tensor.ones(bs, 12, 64, 128)
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p2 = tiny_conv(tiny_dat).relu()
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p2 = tiny_dconv(p2)
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p2.realize()
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if __name__ == '__main__':
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unittest.main()
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