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import tensorflow as tf
import hyperchamber as hc
import inspect
import os
from .base_discriminator import BaseDiscriminator
class PyramidDiscriminator(BaseDiscriminator):
def required(self):
return "activation layers block depth_increase initial_depth".split()
def build(self, net):
config = self.config
gan = self.gan
ops = self.ops
layers = config.layers
depth_increase = config.depth_increase
activation = ops.lookup(config.activation)
final_activation = ops.lookup(config.final_activation)
x, g = self.split_batch(net)
net = self.add_noise(net)
if layers > 0:
initial_depth = max(ops.shape(net)[3], config.initial_depth or 64)
if config.skip_layer_filters and 0 in config.skip_layer_filters:
pass
else:
net = self.layer_filter(net)
net = config.block(self, net, initial_depth, filter=config.initial_filter or 3)
for i in range(layers):
xg = None
is_last_layer = (i == layers-1)
filters = ops.shape(net)[3]
net = activation(net)
net = self.layer_regularizer(net)
if config.skip_layer_filters and (i+1) in config.skip_layer_filters:
pass
else:
net = self.layer_filter(net)
print("[hypergan] adding layer filter", net)
net = self.progressive_enhancement(config, net, xg)
depth = filters + depth_increase
net = config.block(self, net, depth)
print('[discriminator] layer', net)
for i in range(config.extra_layers or 0):
output_features = int(int(net.get_shape()[3]))
net = activation(net)
net = self.layer_regularizer(net)
net = ops.conv2d(net, 3, 3, 1, 1, output_features//(config.extra_layers_reduction or 1))
print('[discriminator] extra layer', net)
k=-1
if config.relation_layer:
net = activation(net)
net = self.layer_regularizer(net)
net = self.relation_layer(net)
#net = tf.reshape(net, [ops.shape(net)[0], -1])
if final_activation or (config.fc_layers or 0) > 0:
net = self.layer_regularizer(net)
for i in range(config.fc_layers or 0):
net = self.layer_regularizer(net)
net = activation(net)
net = ops.reshape(net, [ops.shape(net)[0], -1])
net = ops.linear(net, config.fc_layer_size or 300)
if final_activation:
net = final_activation(net)
print("[discriminator] output", net)
return net
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