def discriminator_model(pair, activation="relu"):
with nn.model_arg_scope(activation=activation):
outpair = list()
for z in pair:
nc = dsize
h = nn.nin(z, nc)
for _ in range(4):
h = nn.residual_block(h, conv=nn.nin)
h = nn.activate(h)
h = nn.nin(h, nc)
outpair.append(h)
h = outpair[0] * outpair[1]
h = tf.reduce_sum(h, [1, 2, 3])
h = tf.expand_dims(h, -1)
return h
def encoder_model(x, out_size, config, extra_resnets, activation="relu"):
with nn.model_arg_scope(activation=activation):
h = nn.conv2d(x, config[0])
h = nn.residual_block(h)
for nf in config[1:]:
h = nn.downsample(h, nf)
h = nn.residual_block(h)
for _ in range(extra_resnets):
h = nn.residual_block(h)
h = nn.activate(h)
h = tf.reduce_mean(h, [1, 2], keepdims=True)
h = nn.nin(h, out_size)
return h
def image_discriminator_model(x, config=None, activation="relu", coords=False):
"""
returns props, logits
"""
with nn.model_arg_scope(activation=activation, coords=coords):
hs = list()
h = nn.conv2d(x, config[0])
hs.append(h)
for nf in config[1:]:
h = nn.downsample(h, nf)
h = nn.residual_block(h)
hs.append(h)
h = nn.activate(h)
h = nn.conv2d(h, config[-1])
h = tf.reduce_mean(h, [1, 2], keepdims=True)
h = nn.nin(h, 1)
h = tf.reduce_mean(h, [1, 2, 3])
h = tf.expand_dims(h, -1)
return tf.nn.sigmoid(h), h
def pretty_discriminator_model(x, c):
with nn.model_arg_scope(activation="relu"):
hs = list()
h = nn.conv2d(x, convconf[0])
hs.append(h)
for nf in convconf[1:]:
h = nn.downsample(h, nf)
h = nn.residual_block(h)
hs.append(h)
h = nn.activate(h)
h = nn.conv2d(h, dsize)
h = tf.reduce_mean(h, [1, 2], keepdims=True)
hc = nn.nin(c, dsize)
hc = nn.residual_block(hc, conv=nn.nin)
hc = nn.residual_block(hc, conv=nn.nin)
h = h * hc
h = tf.reduce_mean(h, [1, 2, 3])
h = tf.expand_dims(h, -1)
return h, hs
