Add openpilot tests

tinygrad_repo/test/extra/test_utils.py

@@ -0,0 +1,106 @@
+#!/usr/bin/env python
+import io, unittest
+import os
+import tempfile
+from unittest.mock import patch, MagicMock
+
+import torch
+import numpy as np
+from tinygrad.helpers import CI
+from extra.utils import fetch, temp, download_file
+from tinygrad.nn.state import torch_load
+from PIL import Image
+
+@unittest.skipIf(CI, "no internet tests in CI")
+class TestFetch(unittest.TestCase):
+  def test_fetch_bad_http(self):
+    self.assertRaises(AssertionError, fetch, 'http://httpstat.us/500')
+    self.assertRaises(AssertionError, fetch, 'http://httpstat.us/404')
+    self.assertRaises(AssertionError, fetch, 'http://httpstat.us/400')
+
+  def test_fetch_small(self):
+    assert(len(fetch('https://google.com'))>0)
+
+  def test_fetch_img(self):
+    img = fetch("https://media.istockphoto.com/photos/hen-picture-id831791190")
+    pimg = Image.open(io.BytesIO(img))
+    assert pimg.size == (705, 1024)
+
+class TestFetchRelative(unittest.TestCase):
+  def setUp(self):
+    self.working_dir = os.getcwd()
+    self.tempdir = tempfile.TemporaryDirectory()
+    os.chdir(self.tempdir.name)
+    with open('test_file.txt', 'x') as f:
+      f.write("12345")
+
+  def tearDown(self):
+    os.chdir(self.working_dir)
+    self.tempdir.cleanup()
+
+  #test ./
+  def test_fetch_relative_dotslash(self):
+    self.assertEqual(b'12345', fetch("./test_file.txt"))
+
+  #test ../
+  def test_fetch_relative_dotdotslash(self):
+    os.mkdir('test_file_path')
+    os.chdir('test_file_path')
+    self.assertEqual(b'12345', fetch("../test_file.txt"))
+
+class TestDownloadFile(unittest.TestCase):
+  def setUp(self):
+    from pathlib import Path
+    self.test_file = Path(temp("test_download_file/test_file.txt"))
+
+  def tearDown(self):
+    os.remove(self.test_file)
+    os.removedirs(self.test_file.parent)
+
+  @patch('requests.get')
+  def test_download_file_with_mkdir(self, mock_requests):
+    mock_response = MagicMock()
+    mock_response.iter_content.return_value = [b'1234', b'5678']
+    mock_response.status_code = 200
+    mock_response.headers = {'content-length': '8'}
+    mock_requests.return_value = mock_response
+    self.assertFalse(self.test_file.parent.exists())
+    download_file("https://www.mock.com/fake.txt", self.test_file, skip_if_exists=False)
+    self.assertTrue(self.test_file.parent.exists())
+    self.assertTrue(self.test_file.is_file())
+    self.assertEqual('12345678', self.test_file.read_text())
+
+class TestUtils(unittest.TestCase):
+  def test_fake_torch_load_zipped(self): self._test_fake_torch_load_zipped()
+  def test_fake_torch_load_zipped_float16(self): self._test_fake_torch_load_zipped(isfloat16=True)
+  def _test_fake_torch_load_zipped(self, isfloat16=False):
+    class LayerWithOffset(torch.nn.Module):
+      def __init__(self):
+        super(LayerWithOffset, self).__init__()
+        d = torch.randn(16)
+        self.param1 = torch.nn.Parameter(
+          d.as_strided([2, 2], [1, 2], storage_offset=5)
+        )
+        self.param2 = torch.nn.Parameter(
+          d.as_strided([2, 2], [1, 2], storage_offset=4)
+        )
+
+    model = torch.nn.Sequential(
+      torch.nn.Linear(4, 8),
+      torch.nn.Linear(8, 3),
+      LayerWithOffset()
+    )
+    if isfloat16: model = model.half()
+
+    path = temp(f"test_load_{isfloat16}.pt")
+    torch.save(model.state_dict(), path)
+    model2 = torch_load(path)
+
+    for name, a in model.state_dict().items():
+      b = model2[name]
+      a, b = a.numpy(), b.numpy()
+      assert a.shape == b.shape
+      assert a.dtype == b.dtype
+      assert np.array_equal(a, b)
+if __name__ == '__main__':
+  unittest.main()