mirror of https://github.com/open-mmlab/mmpose
67 lines
2.4 KiB
Python
67 lines
2.4 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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from unittest import TestCase
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import torch
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from mmpose.models.backbones import ResNeXt
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from mmpose.models.backbones.resnext import Bottleneck as BottleneckX
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class TestResnext(TestCase):
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def test_bottleneck(self):
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with self.assertRaises(AssertionError):
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# Style must be in ['pytorch', 'caffe']
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BottleneckX(
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64, 64, groups=32, width_per_group=4, style='tensorflow')
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# Test ResNeXt Bottleneck structure
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block = BottleneckX(
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64, 256, groups=32, width_per_group=4, stride=2, style='pytorch')
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self.assertEqual(block.conv2.stride, (2, 2))
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self.assertEqual(block.conv2.groups, 32)
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self.assertEqual(block.conv2.out_channels, 128)
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# Test ResNeXt Bottleneck forward
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block = BottleneckX(
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64, 64, base_channels=16, groups=32, width_per_group=4)
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x = torch.randn(1, 64, 56, 56)
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x_out = block(x)
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self.assertEqual(x_out.shape, torch.Size([1, 64, 56, 56]))
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def test_resnext(self):
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with self.assertRaises(KeyError):
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# ResNeXt depth should be in [50, 101, 152]
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ResNeXt(depth=18)
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# Test ResNeXt with group 32, width_per_group 4
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model = ResNeXt(
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depth=50, groups=32, width_per_group=4, out_indices=(0, 1, 2, 3))
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for m in model.modules():
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if isinstance(m, BottleneckX):
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self.assertEqual(m.conv2.groups, 32)
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model.init_weights()
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model.train()
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imgs = torch.randn(1, 3, 224, 224)
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feat = model(imgs)
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self.assertEqual(len(feat), 4)
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self.assertEqual(feat[0].shape, torch.Size([1, 256, 56, 56]))
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self.assertEqual(feat[1].shape, torch.Size([1, 512, 28, 28]))
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self.assertEqual(feat[2].shape, torch.Size([1, 1024, 14, 14]))
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self.assertEqual(feat[3].shape, torch.Size([1, 2048, 7, 7]))
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# Test ResNeXt with layers 3 out forward
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model = ResNeXt(
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depth=50, groups=32, width_per_group=4, out_indices=(3, ))
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for m in model.modules():
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if isinstance(m, BottleneckX):
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self.assertEqual(m.conv2.groups, 32)
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model.init_weights()
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model.train()
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imgs = torch.randn(1, 3, 224, 224)
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feat = model(imgs)
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self.assertEqual(len(feat), 1)
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self.assertEqual(feat[-1].shape, torch.Size([1, 2048, 7, 7]))
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