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# Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmcv.utils.parrots_wrapper import _BatchNorm
from mmpose.models.backbones import VGG
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_vgg():
"""Test VGG backbone."""
with pytest.raises(KeyError):
# VGG depth should be in [11, 13, 16, 19]
VGG(18)
with pytest.raises(AssertionError):
# In VGG: 1 <= num_stages <= 5
VGG(11, num_stages=0)
with pytest.raises(AssertionError):
# In VGG: 1 <= num_stages <= 5
VGG(11, num_stages=6)
with pytest.raises(AssertionError):
# len(dilations) == num_stages
VGG(11, dilations=(1, 1), num_stages=3)
with pytest.raises(TypeError):
# pretrained must be a string path
model = VGG(11)
model.init_weights(pretrained=0)
# Test VGG11 norm_eval=True
model = VGG(11, norm_eval=True)
model.init_weights()
model.train()
assert check_norm_state(model.modules(), False)
# Test VGG11 forward without classifiers
model = VGG(11, out_indices=(0, 1, 2, 3, 4))
model.init_weights()
model.train()
imgs = torch.randn(1, 3, 224, 224)
feat = model(imgs)
assert len(feat) == 5
assert feat[0].shape == (1, 64, 112, 112)
assert feat[1].shape == (1, 128, 56, 56)
assert feat[2].shape == (1, 256, 28, 28)
assert feat[3].shape == (1, 512, 14, 14)
assert feat[4].shape == (1, 512, 7, 7)
# Test VGG11 forward with classifiers
model = VGG(11, num_classes=10, out_indices=(0, 1, 2, 3, 4, 5))
model.init_weights()
model.train()
imgs = torch.randn(1, 3, 224, 224)
feat = model(imgs)
assert len(feat) == 6
assert feat[0].shape == (1, 64, 112, 112)
assert feat[1].shape == (1, 128, 56, 56)
assert feat[2].shape == (1, 256, 28, 28)
assert feat[3].shape == (1, 512, 14, 14)
assert feat[4].shape == (1, 512, 7, 7)
assert feat[5].shape == (1, 10)
# Test VGG11BN forward
model = VGG(11, norm_cfg=dict(type='BN'), out_indices=(0, 1, 2, 3, 4))
model.init_weights()
model.train()
imgs = torch.randn(1, 3, 224, 224)
feat = model(imgs)
assert len(feat) == 5
assert feat[0].shape == (1, 64, 112, 112)
assert feat[1].shape == (1, 128, 56, 56)
assert feat[2].shape == (1, 256, 28, 28)
assert feat[3].shape == (1, 512, 14, 14)
assert feat[4].shape == (1, 512, 7, 7)
# Test VGG11BN forward with classifiers
model = VGG(
11,
num_classes=10,
norm_cfg=dict(type='BN'),
out_indices=(0, 1, 2, 3, 4, 5))
model.init_weights()
model.train()
imgs = torch.randn(1, 3, 224, 224)
feat = model(imgs)
assert len(feat) == 6
assert feat[0].shape == (1, 64, 112, 112)
assert feat[1].shape == (1, 128, 56, 56)
assert feat[2].shape == (1, 256, 28, 28)
assert feat[3].shape == (1, 512, 14, 14)
assert feat[4].shape == (1, 512, 7, 7)
assert feat[5].shape == (1, 10)
# Test VGG13 with layers 1, 2, 3 out forward
model = VGG(13, out_indices=(0, 1, 2))
model.init_weights()
model.train()
imgs = torch.randn(1, 3, 224, 224)
feat = model(imgs)
assert len(feat) == 3
assert feat[0].shape == (1, 64, 112, 112)
assert feat[1].shape == (1, 128, 56, 56)
assert feat[2].shape == (1, 256, 28, 28)
# Test VGG16 with top feature maps out forward
model = VGG(16)
model.init_weights()
model.train()
imgs = torch.randn(1, 3, 224, 224)
feat = model(imgs)
assert feat.shape == (1, 512, 7, 7)
# Test VGG19 with classification score out forward
model = VGG(19, num_classes=10)
model.init_weights()
model.train()
imgs = torch.randn(1, 3, 224, 224)
feat = model(imgs)
assert feat.shape == (1, 10)
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