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https://github.com/tinygrad/tinygrad.git
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57d3aa76a5* matrix strategy * push env to GITHUB_ENV * use printf instead of echo * use temp helper function for cross os paths * use path join * switched to using temp helper function * skip test on windows due to memory limit * small fix * removed semi * touchups * clean up * seperate tests * test changes to test_utils on windows * small refactor * more cleanups * undo helpers change * only skip if in CI and WINDOWS
58 lines
No EOL
1.9 KiB
Python
58 lines
No EOL
1.9 KiB
Python
#!/usr/bin/env python
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import io, unittest
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import torch
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import numpy as np
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from tinygrad.helpers import getenv
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from extra.utils import fetch, temp
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from tinygrad.state import torch_load
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from PIL import Image
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@unittest.skipIf(getenv("CI", "") != "", "no internet tests in CI")
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class TestFetch(unittest.TestCase):
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def test_fetch_bad_http(self):
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self.assertRaises(AssertionError, fetch, 'http://httpstat.us/500')
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self.assertRaises(AssertionError, fetch, 'http://httpstat.us/404')
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self.assertRaises(AssertionError, fetch, 'http://httpstat.us/400')
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def test_fetch_small(self):
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assert(len(fetch('https://google.com'))>0)
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def test_fetch_img(self):
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img = fetch("https://media.istockphoto.com/photos/hen-picture-id831791190")
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pimg = Image.open(io.BytesIO(img))
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assert pimg.size == (705, 1024)
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class TestUtils(unittest.TestCase):
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def test_fake_torch_load_zipped(self): self._test_fake_torch_load_zipped()
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def test_fake_torch_load_zipped_float16(self): self._test_fake_torch_load_zipped(isfloat16=True)
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def _test_fake_torch_load_zipped(self, isfloat16=False):
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class LayerWithOffset(torch.nn.Module):
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def __init__(self):
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super(LayerWithOffset, self).__init__()
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d = torch.randn(16)
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self.param1 = torch.nn.Parameter(
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d.as_strided([2, 2], [1, 2], storage_offset=5)
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)
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self.param2 = torch.nn.Parameter(
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d.as_strided([2, 2], [1, 2], storage_offset=4)
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)
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model = torch.nn.Sequential(
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torch.nn.Linear(4, 8),
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torch.nn.Linear(8, 3),
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LayerWithOffset()
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)
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if isfloat16: model = model.half()
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path = temp(f"test_load_{isfloat16}.pt")
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torch.save(model.state_dict(), path)
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model2 = torch_load(path)
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for name, a in model.state_dict().items():
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b = model2[name]
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a, b = a.numpy(), b.numpy()
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assert a.shape == b.shape
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assert a.dtype == b.dtype
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assert np.array_equal(a, b)
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if __name__ == '__main__':
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unittest.main() |