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18 | 18 |
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19 | 19 | from monai.networks import eval_mode |
20 | 20 | from monai.networks.blocks.segresnet_block import ResBlock |
21 | | - |
22 | | -TEST_CASE_RESBLOCK = [] |
23 | | -for spatial_dims in range(2, 4): |
24 | | - for in_channels in range(1, 4): |
25 | | - for kernel_size in [1, 3]: |
26 | | - for norm in [("group", {"num_groups": 1}), "batch", "instance"]: |
27 | | - test_case = [ |
28 | | - { |
29 | | - "spatial_dims": spatial_dims, |
30 | | - "in_channels": in_channels, |
31 | | - "kernel_size": kernel_size, |
32 | | - "norm": norm, |
33 | | - }, |
34 | | - (2, in_channels, *([16] * spatial_dims)), |
35 | | - (2, in_channels, *([16] * spatial_dims)), |
36 | | - ] |
37 | | - TEST_CASE_RESBLOCK.append(test_case) |
| 21 | +from tests.test_utils import dict_product |
| 22 | + |
| 23 | +TEST_CASE_RESBLOCK = [ |
| 24 | + [ |
| 25 | + { |
| 26 | + "spatial_dims": params["spatial_dims"], |
| 27 | + "in_channels": params["in_channels"], |
| 28 | + "kernel_size": params["kernel_size"], |
| 29 | + "norm": params["norm"], |
| 30 | + }, |
| 31 | + (2, params["in_channels"], *([16] * params["spatial_dims"])), |
| 32 | + (2, params["in_channels"], *([16] * params["spatial_dims"])), |
| 33 | + ] |
| 34 | + for params in dict_product( |
| 35 | + spatial_dims=range(2, 4), |
| 36 | + in_channels=range(1, 4), |
| 37 | + kernel_size=[1, 3], |
| 38 | + norm=[("group", {"num_groups": 1}), "batch", "instance"], |
| 39 | + ) |
| 40 | +] |
38 | 41 |
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39 | 42 |
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40 | 43 | class TestResBlock(unittest.TestCase): |
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