def network(self, image, batch_size, update_collection):
h0 = lrelu(
conv2d(image,
self.dim,
name=self.prefix + 'h0_conv',
update_collection=update_collection,
with_sn=self.with_sn,
scale=self.scale,
with_learnable_sn_scale=self.with_learnable_sn_scale,
data_format=self.format,
with_singular_values=True))
h1 = lrelu(
self.d_bn1(
conv2d(h0,
self.dim * 2,
name=self.prefix + 'h1_conv',
update_collection=update_collection,
with_sn=self.with_sn,
scale=self.scale,
with_learnable_sn_scale=self.with_learnable_sn_scale,
data_format=self.format,
with_singular_values=True)))
h2 = lrelu(
self.d_bn2(
conv2d(h1,
self.dim * 4,
name=self.prefix + 'h2_conv',
update_collection=update_collection,
with_sn=self.with_sn,
scale=self.scale,
with_learnable_sn_scale=self.with_learnable_sn_scale,
data_format=self.format,
with_singular_values=True)))
h3 = lrelu(
self.d_bn3(
conv2d(h2,
self.dim * 8,
name=self.prefix + 'h3_conv',
update_collection=update_collection,
with_sn=self.with_sn,
scale=self.scale,
with_learnable_sn_scale=self.with_learnable_sn_scale,
data_format=self.format,
with_singular_values=True)))
hF = lrelu(
self.d_bn4(
conv2d(h3,
self.o_dim,
name=self.prefix + 'hF_conv',
update_collection=update_collection,
with_sn=self.with_sn,
scale=self.scale,
with_learnable_sn_scale=self.with_learnable_sn_scale,
with_singular_values=True)))
hF = tf.reshape(hF, [batch_size, -1])
return {'h0': h0, 'h1': h1, 'h2': h2, 'h3': h3, 'hF': hF}
def network(self, image, batch_size, update_collection):
from core.resnet import block, ops
if self.format == 'NHWC':
image = tf.transpose(image, [0, 3, 1, 2]) # NHWC to NCHW
h0 = lrelu(
ops.conv2d.Conv2D(self.prefix + 'h0_conv', 3, self.dim, 3, image))
h1 = block.ResidualBlock(self.prefix + 'res1',
self.dim,
2 * self.dim,
3,
h0,
resample='down')
h2 = block.ResidualBlock(self.prefix + 'res2',
2 * self.dim,
4 * self.dim,
3,
h1,
resample='down')
h3 = block.ResidualBlock(self.prefix + 'res3',
4 * self.dim,
8 * self.dim,
3,
h2,
resample='down')
h4 = block.ResidualBlock(self.prefix + 'res4',
8 * self.dim,
8 * self.dim,
3,
h3,
resample='down')
h4 = tf.reshape(h4, [-1, 4 * 4 * 8 * self.dim])
hF = linear(h4, self.o_dim, self.prefix + 'h5_lin')
return {'h0': h0, 'h1': h1, 'h2': h2, 'h3': h3, 'h4': h4, 'hF': hF}
def network(self, image, batch_size, update_collection):
c0_0 = lrelu(
conv2d(image,
64,
3,
3,
1,
1,
with_sn=self.with_sn,
with_learnable_sn_scale=self.with_learnable_sn_scale,
update_collection=update_collection,
stddev=0.02,
name=self.prefix + 'c0_0',
data_format=self.format,
with_singular_values=True))
c0_1 = lrelu(
conv2d(c0_0,
128,
4,
4,
2,
2,
with_sn=self.with_sn,
with_learnable_sn_scale=self.with_learnable_sn_scale,
update_collection=update_collection,
stddev=0.02,
name=self.prefix + 'c0_1',
data_format=self.format,
with_singular_values=True))
c1_0 = lrelu(
conv2d(c0_1,
128,
3,
3,
1,
1,
with_sn=self.with_sn,
with_learnable_sn_scale=self.with_learnable_sn_scale,
update_collection=update_collection,
stddev=0.02,
name=self.prefix + 'c1_0',
data_format=self.format,
with_singular_values=True))
c1_1 = lrelu(
conv2d(c1_0,
256,
4,
4,
2,
2,
with_sn=self.with_sn,
with_learnable_sn_scale=self.with_learnable_sn_scale,
update_collection=update_collection,
stddev=0.02,
name=self.prefix + 'c1_1',
data_format=self.format,
with_singular_values=True))
c2_0 = lrelu(
conv2d(c1_1,
256,
3,
3,
1,
1,
with_sn=self.with_sn,
with_learnable_sn_scale=self.with_learnable_sn_scale,
update_collection=update_collection,
stddev=0.02,
name=self.prefix + 'c2_0',
data_format=self.format,
with_singular_values=True))
c2_1 = lrelu(
conv2d(c2_0,
512,
4,
4,
2,
2,
with_sn=self.with_sn,
with_learnable_sn_scale=self.with_learnable_sn_scale,
update_collection=update_collection,
stddev=0.02,
name=self.prefix + 'c2_1',
data_format=self.format,
with_singular_values=True))
c3_0 = lrelu(
conv2d(c2_1,
512,
3,
3,
1,
1,
with_sn=self.with_sn,
with_learnable_sn_scale=self.with_learnable_sn_scale,
update_collection=update_collection,
stddev=0.02,
name=self.prefix + 'c3_0',
data_format=self.format,
with_singular_values=True))
c3_0 = tf.reshape(c3_0, [batch_size, -1])
l4 = linear(c3_0,
self.o_dim,
with_sn=self.with_sn,
with_learnable_sn_scale=self.with_learnable_sn_scale,
update_collection=update_collection,
stddev=0.02,
name=self.prefix + 'l4')
return {
'h0': c0_0,
'h1': c0_1,
'h2': c1_0,
'h3': c1_1,
'h4': c2_0,
'h5': c2_1,
'h6': c3_0,
'hF': l4
}
def network(self, image, batch_size, update_collection, y):
from core.resnet import block, ops
if self.format == 'NHWC':
image = tf.transpose(image, [0, 3, 1, 2]) # NHWC to NCHW
h0 = lrelu(
ops.conv2d.Conv2D(
self.prefix + 'h0_conv',
3,
self.dim,
3,
image,
update_collection=update_collection,
with_sn=self.with_sn,
with_learnable_sn_scale=self.with_learnable_sn_scale))
h1 = block.ResidualBlock(
self.prefix + 'res1',
self.dim,
2 * self.dim,
3,
h0,
resample='down',
update_collection=update_collection,
with_sn=self.with_sn,
with_learnable_sn_scale=self.with_learnable_sn_scale)
h2 = block.ResidualBlock(
self.prefix + 'res2',
2 * self.dim,
4 * self.dim,
3,
h1,
resample='down',
update_collection=update_collection,
with_sn=self.with_sn,
with_learnable_sn_scale=self.with_learnable_sn_scale)
h3 = block.ResidualBlock(
self.prefix + 'res3',
4 * self.dim,
8 * self.dim,
3,
h2,
resample='down',
update_collection=update_collection,
with_sn=self.with_sn,
with_learnable_sn_scale=self.with_learnable_sn_scale)
h4 = block.ResidualBlock(
self.prefix + 'res4',
8 * self.dim,
16 * self.dim,
3,
h3,
resample='down',
update_collection=update_collection,
with_sn=self.with_sn,
with_learnable_sn_scale=self.with_learnable_sn_scale)
if image.get_shape().as_list()[2] == 64:
h4_bis = h4
else:
h4_bis = block.ResidualBlock(
self.prefix + 'res4_bis',
16 * self.dim,
16 * self.dim,
3,
h4,
resample=None,
update_collection=update_collection,
with_sn=self.with_sn,
with_learnable_sn_scale=self.with_learnable_sn_scale)
h4_bis = lrelu(h4_bis)
h4_bis = tf.reduce_sum(h4_bis, axis=[2, 3])
hF = linear(h4_bis,
self.o_dim,
self.prefix + 'h5_lin',
update_collection=update_collection,
with_sn=self.with_sn,
with_learnable_sn_scale=self.with_learnable_sn_scale)
if not y is None:
w_y = linear_one_hot(
y,
self.o_dim,
self.num_classes,
name=self.prefix + "Linear_one_hot",
update_collection=update_collection,
with_sn=self.with_sn,
with_learnable_sn_scale=self.with_learnable_sn_scale)
hF += tf.reduce_sum(w_y * hF, axis=1, keepdims=True)
return {'h0': h0, 'h1': h1, 'h2': h2, 'h3': h3, 'h4': h4, 'hF': hF}
