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# Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmpose.models.backbones import RegNet
regnet_test_data = [
('regnetx_400mf',
dict(w0=24, wa=24.48, wm=2.54, group_w=16, depth=22,
bot_mul=1.0), [32, 64, 160, 384]),
('regnetx_800mf',
dict(w0=56, wa=35.73, wm=2.28, group_w=16, depth=16,
bot_mul=1.0), [64, 128, 288, 672]),
('regnetx_1.6gf',
dict(w0=80, wa=34.01, wm=2.25, group_w=24, depth=18,
bot_mul=1.0), [72, 168, 408, 912]),
('regnetx_3.2gf',
dict(w0=88, wa=26.31, wm=2.25, group_w=48, depth=25,
bot_mul=1.0), [96, 192, 432, 1008]),
('regnetx_4.0gf',
dict(w0=96, wa=38.65, wm=2.43, group_w=40, depth=23,
bot_mul=1.0), [80, 240, 560, 1360]),
('regnetx_6.4gf',
dict(w0=184, wa=60.83, wm=2.07, group_w=56, depth=17,
bot_mul=1.0), [168, 392, 784, 1624]),
('regnetx_8.0gf',
dict(w0=80, wa=49.56, wm=2.88, group_w=120, depth=23,
bot_mul=1.0), [80, 240, 720, 1920]),
('regnetx_12gf',
dict(w0=168, wa=73.36, wm=2.37, group_w=112, depth=19,
bot_mul=1.0), [224, 448, 896, 2240]),
]
@pytest.mark.parametrize('arch_name,arch,out_channels', regnet_test_data)
def test_regnet_backbone(arch_name, arch, out_channels):
with pytest.raises(AssertionError):
# ResNeXt depth should be in [50, 101, 152]
RegNet(arch_name + '233')
# output the last feature map
model = RegNet(arch_name)
model.init_weights()
model.train()
imgs = torch.randn(1, 3, 224, 224)
feat = model(imgs)
assert isinstance(feat, torch.Tensor)
assert feat.shape == (1, out_channels[-1], 7, 7)
# output feature map of all stages
model = RegNet(arch_name, out_indices=(0, 1, 2, 3))
model.init_weights()
model.train()
imgs = torch.randn(1, 3, 224, 224)
feat = model(imgs)
assert len(feat) == 4
assert feat[0].shape == (1, out_channels[0], 56, 56)
assert feat[1].shape == (1, out_channels[1], 28, 28)
assert feat[2].shape == (1, out_channels[2], 14, 14)
assert feat[3].shape == (1, out_channels[3], 7, 7)
@pytest.mark.parametrize('arch_name,arch,out_channels', regnet_test_data)
def test_custom_arch(arch_name, arch, out_channels):
# output the last feature map
model = RegNet(arch)
model.init_weights()
imgs = torch.randn(1, 3, 224, 224)
feat = model(imgs)
assert isinstance(feat, torch.Tensor)
assert feat.shape == (1, out_channels[-1], 7, 7)
# output feature map of all stages
model = RegNet(arch, out_indices=(0, 1, 2, 3))
model.init_weights()
imgs = torch.randn(1, 3, 224, 224)
feat = model(imgs)
assert len(feat) == 4
assert feat[0].shape == (1, out_channels[0], 56, 56)
assert feat[1].shape == (1, out_channels[1], 28, 28)
assert feat[2].shape == (1, out_channels[2], 14, 14)
assert feat[3].shape == (1, out_channels[3], 7, 7)
def test_exception():
# arch must be a str or dict
with pytest.raises(TypeError):
_ = RegNet(50)
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