mirror of https://github.com/open-mmlab/mmpose
138 lines
4.7 KiB
Python
138 lines
4.7 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
|
|
from unittest import TestCase
|
|
|
|
import torch
|
|
from mmengine.utils.dl_utils.parrots_wrapper import _BatchNorm
|
|
|
|
from mmpose.models.backbones import VGG
|
|
|
|
|
|
class TestVGG(TestCase):
|
|
|
|
@staticmethod
|
|
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(self):
|
|
"""Test VGG backbone."""
|
|
with self.assertRaises(KeyError):
|
|
# VGG depth should be in [11, 13, 16, 19]
|
|
VGG(18)
|
|
|
|
with self.assertRaises(AssertionError):
|
|
# In VGG: 1 <= num_stages <= 5
|
|
VGG(11, num_stages=0)
|
|
|
|
with self.assertRaises(AssertionError):
|
|
# In VGG: 1 <= num_stages <= 5
|
|
VGG(11, num_stages=6)
|
|
|
|
with self.assertRaises(AssertionError):
|
|
# len(dilations) == num_stages
|
|
VGG(11, dilations=(1, 1), num_stages=3)
|
|
|
|
# Test VGG11 norm_eval=True
|
|
model = VGG(11, norm_eval=True)
|
|
model.init_weights()
|
|
model.train()
|
|
self.assertTrue(self.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)
|
|
self.assertEqual(len(feat), 5)
|
|
self.assertEqual(feat[0].shape, (1, 64, 112, 112))
|
|
self.assertEqual(feat[1].shape, (1, 128, 56, 56))
|
|
self.assertEqual(feat[2].shape, (1, 256, 28, 28))
|
|
self.assertEqual(feat[3].shape, (1, 512, 14, 14))
|
|
self.assertEqual(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)
|
|
self.assertEqual(len(feat), 6)
|
|
self.assertEqual(feat[0].shape, (1, 64, 112, 112))
|
|
self.assertEqual(feat[1].shape, (1, 128, 56, 56))
|
|
self.assertEqual(feat[2].shape, (1, 256, 28, 28))
|
|
self.assertEqual(feat[3].shape, (1, 512, 14, 14))
|
|
self.assertEqual(feat[4].shape, (1, 512, 7, 7))
|
|
self.assertEqual(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)
|
|
self.assertEqual(len(feat), 5)
|
|
self.assertEqual(feat[0].shape, (1, 64, 112, 112))
|
|
self.assertEqual(feat[1].shape, (1, 128, 56, 56))
|
|
self.assertEqual(feat[2].shape, (1, 256, 28, 28))
|
|
self.assertEqual(feat[3].shape, (1, 512, 14, 14))
|
|
self.assertEqual(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)
|
|
self.assertEqual(len(feat), 6)
|
|
self.assertEqual(feat[0].shape, (1, 64, 112, 112))
|
|
self.assertEqual(feat[1].shape, (1, 128, 56, 56))
|
|
self.assertEqual(feat[2].shape, (1, 256, 28, 28))
|
|
self.assertEqual(feat[3].shape, (1, 512, 14, 14))
|
|
self.assertEqual(feat[4].shape, (1, 512, 7, 7))
|
|
self.assertEqual(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)
|
|
self.assertEqual(len(feat), 3)
|
|
self.assertEqual(feat[0].shape, (1, 64, 112, 112))
|
|
self.assertEqual(feat[1].shape, (1, 128, 56, 56))
|
|
self.assertEqual(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)
|
|
self.assertEqual(len(feat), 1)
|
|
self.assertEqual(feat[-1].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)
|
|
self.assertEqual(len(feat), 1)
|
|
self.assertEqual(feat[-1].shape, (1, 10))
|