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# Copyright (c) OpenMMLab. All rights reserved. | |
import pytest | |
import torch | |
from mmpose.models import RSN | |
def test_rsn_backbone(): | |
with pytest.raises(AssertionError): | |
# RSN's num_stages should larger than 0 | |
RSN(num_stages=0) | |
with pytest.raises(AssertionError): | |
# RSN's num_steps should larger than 1 | |
RSN(num_steps=1) | |
with pytest.raises(AssertionError): | |
# RSN's num_units should larger than 1 | |
RSN(num_units=1) | |
with pytest.raises(AssertionError): | |
# len(num_blocks) should equal num_units | |
RSN(num_units=2, num_blocks=[2, 2, 2]) | |
# Test RSN's outputs | |
model = RSN(num_stages=2, num_units=2, num_blocks=[2, 2]) | |
model.init_weights() | |
model.train() | |
imgs = torch.randn(1, 3, 511, 511) | |
feat = model(imgs) | |
assert len(feat) == 2 | |
assert len(feat[0]) == 2 | |
assert len(feat[1]) == 2 | |
assert feat[0][0].shape == torch.Size([1, 256, 64, 64]) | |
assert feat[0][1].shape == torch.Size([1, 256, 128, 128]) | |
assert feat[1][0].shape == torch.Size([1, 256, 64, 64]) | |
assert feat[1][1].shape == torch.Size([1, 256, 128, 128]) | |