File size: 6,092 Bytes
3bbb319 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 |
# Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from torch.nn.modules import GroupNorm
from torch.nn.modules.batchnorm import _BatchNorm
from mmpose.models.backbones import MobileNetV3
from mmpose.models.backbones.utils import InvertedResidual
def is_norm(modules):
"""Check if is one of the norms."""
if isinstance(modules, (GroupNorm, _BatchNorm)):
return True
return False
def check_norm_state(modules, train_state):
"""Check if norm layer is in correct train state."""
for mod in modules:
if isinstance(mod, _BatchNorm):
if mod.training != train_state:
return False
return True
def test_mobilenetv3_backbone():
with pytest.raises(TypeError):
# pretrained must be a string path
model = MobileNetV3()
model.init_weights(pretrained=0)
with pytest.raises(AssertionError):
# arch must in [small, big]
MobileNetV3(arch='others')
with pytest.raises(ValueError):
# frozen_stages must less than 12 when arch is small
MobileNetV3(arch='small', frozen_stages=12)
with pytest.raises(ValueError):
# frozen_stages must less than 16 when arch is big
MobileNetV3(arch='big', frozen_stages=16)
with pytest.raises(ValueError):
# max out_indices must less than 11 when arch is small
MobileNetV3(arch='small', out_indices=(11, ))
with pytest.raises(ValueError):
# max out_indices must less than 15 when arch is big
MobileNetV3(arch='big', out_indices=(15, ))
# Test MobileNetv3
model = MobileNetV3()
model.init_weights()
model.train()
# Test MobileNetv3 with first stage frozen
frozen_stages = 1
model = MobileNetV3(frozen_stages=frozen_stages)
model.init_weights()
model.train()
for param in model.conv1.parameters():
assert param.requires_grad is False
for i in range(1, frozen_stages + 1):
layer = getattr(model, f'layer{i}')
for mod in layer.modules():
if isinstance(mod, _BatchNorm):
assert mod.training is False
for param in layer.parameters():
assert param.requires_grad is False
# Test MobileNetv3 with norm eval
model = MobileNetV3(norm_eval=True, out_indices=range(0, 11))
model.init_weights()
model.train()
assert check_norm_state(model.modules(), False)
# Test MobileNetv3 forward with small arch
model = MobileNetV3(out_indices=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10))
model.init_weights()
model.train()
imgs = torch.randn(1, 3, 224, 224)
feat = model(imgs)
assert len(feat) == 11
assert feat[0].shape == torch.Size([1, 16, 56, 56])
assert feat[1].shape == torch.Size([1, 24, 28, 28])
assert feat[2].shape == torch.Size([1, 24, 28, 28])
assert feat[3].shape == torch.Size([1, 40, 14, 14])
assert feat[4].shape == torch.Size([1, 40, 14, 14])
assert feat[5].shape == torch.Size([1, 40, 14, 14])
assert feat[6].shape == torch.Size([1, 48, 14, 14])
assert feat[7].shape == torch.Size([1, 48, 14, 14])
assert feat[8].shape == torch.Size([1, 96, 7, 7])
assert feat[9].shape == torch.Size([1, 96, 7, 7])
assert feat[10].shape == torch.Size([1, 96, 7, 7])
# Test MobileNetv3 forward with small arch and GroupNorm
model = MobileNetV3(
out_indices=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10),
norm_cfg=dict(type='GN', num_groups=2, requires_grad=True))
for m in model.modules():
if is_norm(m):
assert isinstance(m, GroupNorm)
model.init_weights()
model.train()
imgs = torch.randn(1, 3, 224, 224)
feat = model(imgs)
assert len(feat) == 11
assert feat[0].shape == torch.Size([1, 16, 56, 56])
assert feat[1].shape == torch.Size([1, 24, 28, 28])
assert feat[2].shape == torch.Size([1, 24, 28, 28])
assert feat[3].shape == torch.Size([1, 40, 14, 14])
assert feat[4].shape == torch.Size([1, 40, 14, 14])
assert feat[5].shape == torch.Size([1, 40, 14, 14])
assert feat[6].shape == torch.Size([1, 48, 14, 14])
assert feat[7].shape == torch.Size([1, 48, 14, 14])
assert feat[8].shape == torch.Size([1, 96, 7, 7])
assert feat[9].shape == torch.Size([1, 96, 7, 7])
assert feat[10].shape == torch.Size([1, 96, 7, 7])
# Test MobileNetv3 forward with big arch
model = MobileNetV3(
arch='big',
out_indices=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14))
model.init_weights()
model.train()
imgs = torch.randn(1, 3, 224, 224)
feat = model(imgs)
assert len(feat) == 15
assert feat[0].shape == torch.Size([1, 16, 112, 112])
assert feat[1].shape == torch.Size([1, 24, 56, 56])
assert feat[2].shape == torch.Size([1, 24, 56, 56])
assert feat[3].shape == torch.Size([1, 40, 28, 28])
assert feat[4].shape == torch.Size([1, 40, 28, 28])
assert feat[5].shape == torch.Size([1, 40, 28, 28])
assert feat[6].shape == torch.Size([1, 80, 14, 14])
assert feat[7].shape == torch.Size([1, 80, 14, 14])
assert feat[8].shape == torch.Size([1, 80, 14, 14])
assert feat[9].shape == torch.Size([1, 80, 14, 14])
assert feat[10].shape == torch.Size([1, 112, 14, 14])
assert feat[11].shape == torch.Size([1, 112, 14, 14])
assert feat[12].shape == torch.Size([1, 160, 14, 14])
assert feat[13].shape == torch.Size([1, 160, 7, 7])
assert feat[14].shape == torch.Size([1, 160, 7, 7])
# Test MobileNetv3 forward with big arch
model = MobileNetV3(arch='big', out_indices=(0, ))
model.init_weights()
model.train()
imgs = torch.randn(1, 3, 224, 224)
feat = model(imgs)
assert feat.shape == torch.Size([1, 16, 112, 112])
# Test MobileNetv3 with checkpoint forward
model = MobileNetV3(with_cp=True)
for m in model.modules():
if isinstance(m, InvertedResidual):
assert m.with_cp
model.init_weights()
model.train()
imgs = torch.randn(1, 3, 224, 224)
feat = model(imgs)
assert feat.shape == torch.Size([1, 96, 7, 7])
|