mmpose-0.29.0/tests/test_backbones/test_resnext.py

# Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch

from mmpose.models.backbones import ResNeXt
from mmpose.models.backbones.resnext import Bottleneck as BottleneckX


def test_bottleneck():
 with pytest.raises(AssertionError):
 # Style must be in ['pytorch', 'caffe']
 BottleneckX(64, 64, groups=32, width_per_group=4, style='tensorflow')

 # Test ResNeXt Bottleneck structure
 block = BottleneckX(
 64, 256, groups=32, width_per_group=4, stride=2, style='pytorch')
 assert block.conv2.stride == (2, 2)
 assert block.conv2.groups == 32
 assert block.conv2.out_channels == 128

 # Test ResNeXt Bottleneck forward
 block = BottleneckX(64, 64, base_channels=16, groups=32, width_per_group=4)
 x = torch.randn(1, 64, 56, 56)
 x_out = block(x)
 assert x_out.shape == torch.Size([1, 64, 56, 56])


def test_resnext():
 with pytest.raises(KeyError):
 # ResNeXt depth should be in [50, 101, 152]
 ResNeXt(depth=18)

 # Test ResNeXt with group 32, width_per_group 4
 model = ResNeXt(
 depth=50, groups=32, width_per_group=4, out_indices=(0, 1, 2, 3))
 for m in model.modules():
 if isinstance(m, BottleneckX):
 assert m.conv2.groups == 32
 model.init_weights()
 model.train()

 imgs = torch.randn(1, 3, 224, 224)
 feat = model(imgs)
 assert len(feat) == 4
 assert feat[0].shape == torch.Size([1, 256, 56, 56])
 assert feat[1].shape == torch.Size([1, 512, 28, 28])
 assert feat[2].shape == torch.Size([1, 1024, 14, 14])
 assert feat[3].shape == torch.Size([1, 2048, 7, 7])

 # Test ResNeXt with group 32, width_per_group 4 and layers 3 out forward
 model = ResNeXt(depth=50, groups=32, width_per_group=4, out_indices=(3, ))
 for m in model.modules():
 if isinstance(m, BottleneckX):
 assert m.conv2.groups == 32
 model.init_weights()
 model.train()

 imgs = torch.randn(1, 3, 224, 224)
 feat = model(imgs)
 assert feat.shape == torch.Size([1, 2048, 7, 7])