def test_fc_densenet(self):
densenet = FCDenseNet(in_channels=self.rgb_channels,
out_channels=self.num_classes,
initial_num_features=24,
dropout=0.2,
down_dense_growth_rates=8,
down_dense_bottleneck_ratios=None,
down_dense_num_layers=(4, 5, 7),
down_transition_compression_factors=1.0,
middle_dense_growth_rate=8,
middle_dense_bottleneck=None,
middle_dense_num_layers=10,
up_dense_growth_rates=8,
up_dense_bottleneck_ratios=None,
up_dense_num_layers=(7, 5, 4))
print(densenet)
print('Layers:', count_conv2d(densenet))
print('Parameters:', count_parameters(densenet))
logits = densenet(self.images)
print('Logits:', logits.shape)
self.assertEqual(
logits.shape,
Size((self.batch_size, self.num_classes, self.H, self.W)))
def test_fc_densenet_103(self):
densenet = FCDenseNet103()
layers = count_conv2d(densenet)
print('Layers:', layers)
print('Parameters:', count_parameters(densenet))
self.assertEqual(103, layers)
def test_densenet(self):
densenets = [
DenseNet(self.rgb_channels, self.imagenet_classes),
DenseNet121(),
DenseNet161(),
DenseNet169(),
DenseNet201(),
]
for densenet in densenets:
with self.subTest(klass=type(densenet).__name__):
print(densenet)
layers = count_conv2d(densenet)
print('Layers:', layers)
print('Parameters:', count_parameters(densenet))
logits = densenet(self.images)
print('Logits:', logits.shape)
self.assertEqual(logits.shape, Size((self.batch_size, self.imagenet_classes)))