code stringlengths 3 6.57k |
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test_random_crop_and_resize_02() |
test_random_crop_and_resize_03() |
test_random_crop_and_resize_04_c() |
test_random_crop_and_resize_04_py() |
test_random_crop_and_resize_05_c() |
test_random_crop_and_resize_05_py() |
test_random_crop_and_resize_06() |
test_random_crop_and_resize_comp(True) |
RandomGeneratorBaseTests() |
test_valid_param_combinations(self, device, dtype) |
test_invalid_param_combinations(self, device, dtype) |
test_random_gen(self, device, dtype) |
test_same_on_batch(self, device, dtype) |
TestRandomProbGen(RandomGeneratorBaseTests) |
pytest.mark.parametrize('p', [0., 0.5, 1.]) |
pytest.mark.parametrize('batch_size', [0, 1, 8]) |
pytest.mark.parametrize('same_on_batch', [True, False]) |
test_valid_param_combinations(self, p, batch_size, same_on_batch, device, dtype) |
random_prob_generator(batch_size=batch_size, p=p, same_on_batch=same_on_batch) |
test_invalid_param_combinations(self, p, device, dtype) |
pytest.raises(Exception) |
random_prob_generator(batch_size=8, p=p) |
test_random_gen(self, p, expected, device, dtype) |
torch.manual_seed(42) |
random_prob_generator(batch_size=batch_size, p=p) |
assert (res == torch.tensor(expected) |
long() |
sum() |
test_same_on_batch(self, seed, expected, device, dtype) |
torch.manual_seed(seed) |
random_prob_generator(batch_size=batch_size, p=.5, same_on_batch=True) |
assert (res == torch.tensor(expected) |
long() |
sum() |
TestColorJitterGen(RandomGeneratorBaseTests) |
pytest.mark.parametrize('brightness', [None, torch.tensor([0.8, 1.2]) |
pytest.mark.parametrize('contrast', [None, torch.tensor([0.8, 1.2]) |
pytest.mark.parametrize('saturation', [None, torch.tensor([0.8, 1.2]) |
pytest.mark.parametrize('hue', [None, torch.tensor([-0.1, 0.1]) |
pytest.mark.parametrize('batch_size', [0, 1, 8]) |
pytest.mark.parametrize('same_on_batch', [True, False]) |
brightness.to(device=device, dtype=dtype) |
contrast.to(device=device, dtype=dtype) |
saturation.to(device=device, dtype=dtype) |
hue.to(device=device, dtype=dtype) |
torch.tensor([-1., 2.]) |
torch.tensor([0., 3.]) |
torch.tensor(0.) |
torch.tensor([0.]) |
torch.tensor([0., 1., 2.]) |
torch.tensor([-1., 2.]) |
torch.tensor(0.) |
torch.tensor([0.]) |
torch.tensor([0., 1., 2.]) |
torch.tensor([-1., 2.]) |
torch.tensor(0.) |
torch.tensor([0.]) |
torch.tensor([0., 1., 2.]) |
torch.tensor([-1., 0.]) |
torch.tensor([0, 1.]) |
torch.tensor(0.) |
torch.tensor([0.]) |
torch.tensor([0., 1., 2.]) |
test_invalid_param_combinations(self, brightness, contrast, saturation, hue, device, dtype) |
pytest.raises(Exception) |
brightness.to(device=device, dtype=dtype) |
contrast.to(device=device, dtype=dtype) |
saturation.to(device=device, dtype=dtype) |
hue.to(device=device, dtype=dtype) |
test_random_gen(self, device, dtype) |
torch.manual_seed(42) |
torch.tensor([0.8, 1.2], device=device, dtype=dtype) |
torch.tensor([0.7, 1.3], device=device, dtype=dtype) |
torch.tensor([0.6, 1.4], device=device, dtype=dtype) |
torch.tensor([-0.1, 0.1], device=device, dtype=dtype) |
torch.tensor([3, 2, 0, 1], device=device, dtype=dtype) |
set(list(jitter_params.keys() |
assert_allclose(jitter_params['hue_factor'], expected_jitter_params['hue_factor'], rtol=1e-4, atol=1e-4) |
assert_allclose(jitter_params['order'].to(dtype) |
test_same_on_batch(self, device, dtype) |
torch.manual_seed(42) |
torch.tensor([0.8, 1.2], device=device, dtype=dtype) |
torch.tensor([0.7, 1.3], device=device, dtype=dtype) |
torch.tensor([0.6, 1.4], device=device, dtype=dtype) |
torch.tensor([-0.1, 0.1], device=device, dtype=dtype) |
torch.tensor([1.1529] * batch_size, device=device, dtype=dtype) |
torch.tensor([1.2490] * batch_size, device=device, dtype=dtype) |
torch.tensor([-0.0234] * batch_size, device=device, dtype=dtype) |
torch.tensor([1.3674] * batch_size, device=device, dtype=dtype) |
torch.tensor([2, 3, 0, 1], device=device, dtype=dtype) |
assert_allclose(jitter_params['brightness_factor'], expected_res['brightness_factor'], rtol=1e-4, atol=1e-4) |
assert_allclose(jitter_params['contrast_factor'], expected_res['contrast_factor'], rtol=1e-4, atol=1e-4) |
assert_allclose(jitter_params['hue_factor'], expected_res['hue_factor'], rtol=1e-4, atol=1e-4) |
assert_allclose(jitter_params['saturation_factor'], expected_res['saturation_factor'], rtol=1e-4, atol=1e-4) |
assert_allclose(jitter_params['order'].to(dtype) |
TestRandomPerspectiveGen(RandomGeneratorBaseTests) |
pytest.mark.parametrize('height,width', [(200, 200) |
pytest.mark.parametrize('distortion_scale', [torch.tensor(0.) |
torch.tensor(0.5) |
torch.tensor(1.) |
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