Tensorflow implementation of the FastDeconv2D and ChannelDeconv layers from the paper Network Deconvolution by Ye et al. Code ported from the repository - https://github.com/yechengxi/deconvolution/.
Tensorflow implementation also support mixed precision training, allowing larger training sizes with no reduction in accuracy (found in tf_deconv_mixed_prec.py).
Simply download the tf_deconv.py script and import ChannelDeconv2D and FastDeconv2D layers. Mixed precision support can be found in equivalent classes inside tf_deconv_mixed_prec.py.
A baseline model has been provided in models/vgg.py to try out the architecture. FastDeconv2D can replace most Conv2D layer operations.
It is crucial to initialize your models properly to obtain correct performance.
-
All
FastDeconv2Dkernels are initialized by default usinghe_uniform, and their bias byBiasHeUniform. -
Final
Denselayerkernel_initializershould behe_uniformandbias_initializershould beBiasHeUniform.
import tensorflow as tf
from tf_deconv import FastDeconv2D, ChannelDeconv2D, BiasHeUniform
kernel_size = 3
cfg = {
'VGG11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
'VGG13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
'VGG16': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],
'VGG19': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],
}
class VGG(tf.keras.Model):
def __init__(self, vgg_name, num_classes=10):
super(VGG, self).__init__()
assert vgg_name in cfg.keys(), "Choose VGG model from {}".format(cfg.keys())
self.features = self._make_layers(cfg[vgg_name])
self.channel_deconv = ChannelDeconv2D(block=512)
self.classifier = tf.keras.layers.Dense(num_classes, activation='softmax',
kernel_initializer='he_uniform',
bias_initializer=BiasHeUniform(),
)
def call(self, x, training=None, mask=None):
out = self.features(x, training=training)
out = self.channel_deconv(out, training=training)
out = self.classifier(out)
return out
def _make_layers(self, cfg):
layers = []
in_channels = 3
for x in cfg:
if x == 'M':
layers.append(tf.keras.layers.MaxPool2D())
else:
if in_channels == 3:
deconv = FastDeconv2D(in_channels, x, kernel_size=(kernel_size, kernel_size), padding='same',
freeze=True, n_iter=15, block=64, activation='relu')
else:
deconv = FastDeconv2D(in_channels, x, kernel_size=(kernel_size, kernel_size), padding='same',
block=64, activation='relu')
layers.append(deconv)
in_channels = x
layers.append(tf.keras.layers.GlobalAveragePooling2D())
return tf.keras.Sequential(layers)- Tensorflow 2.1+
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