def _block_up(self,
down_inputs,
up_inputs,
filters,
padding,
scope,
upsampling=True):
with tf.variable_scope(scope):
# upsample first
if upsampling:
up_inputs = layers.upsampling_2d(inputs=up_inputs,
factors=[2, 2])
out = layers.crop_to_fit(down_inputs=down_inputs,
up_inputs=up_inputs)
out = layers.conv2d(inputs=out,
filters=filters,
kernel_size=3,
stride_size=1,
padding=padding,
normalization='instance_normalization',
activation=tf.nn.relu,
mode=self.mode,
name='conv1')
out = layers.conv2d(inputs=out,
filters=filters,
kernel_size=3,
stride_size=1,
padding=padding,
normalization='instance_normalization',
activation=tf.nn.relu,
mode=self.mode,
name='conv2')
return out
def _block_down(self, inputs, filters, padding, scope):
with tf.variable_scope(scope):
# downsampling path
out = layers.conv2d(inputs=inputs,
filters=filters,
kernel_size=3,
stride_size=1,
padding=padding,
normalization='instance_normalization',
activation=tf.nn.relu,
mode=self.mode,
name='conv1')
out = layers.conv2d(inputs=out,
filters=filters,
kernel_size=3,
stride_size=1,
padding=padding,
normalization='instance_normalization',
activation=tf.nn.relu,
mode=self.mode,
name='conv2')
maxp = layers.max_pooling2d(inputs=out,
pool_size=2,
strides=2,
padding='VALID')
return out, maxp
def encoder(x, latent_dim, name='encoder'):
with tf.variable_scope(name):
out = layers.conv2d(input_=x,
filters=32,
kernel_size=3,
stride_size=2,
padding='SAME',
activation=tf.nn.relu,
kernel_initializer=initializer,
name='conv2d_1')
out = layers.conv2d(input_=out,
filters=64,
kernel_size=3,
stride_size=2,
padding='SAME',
activation=tf.nn.relu,
kernel_initializer=initializer,
name='conv2d_2')
out = layers.flatten(out)
out = layers.fc(input_=out,
units=16,
activation=tf.nn.relu,
kernel_initializer=initializer,
name='fc_1')
mu = layers.fc(input_=out,
units=latent_dim,
activation=None,
kernel_initializer=initializer,
name='mu')
logvar = layers.fc(input_=out,
units=latent_dim,
activation=None,
kernel_initializer=initializer,
name='logvar')
return mu, logvar
def _build_graph(self):
# downsampling path
init_filter = 16
self.next_images = self.datapipe.next_images
down_out1, maxp1 = self._block_down(inputs=self.next_images,
filters=init_filter,
padding='SAME',
name='down_block1')
down_out2, maxp2 = self._block_down(inputs=maxp1,
filters=init_filter * 2,
padding='SAME',
name='down_block2')
down_out3, maxp3 = self._block_down(inputs=maxp2,
filters=init_filter * 4,
padding='SAME',
name='down_block3')
down_out4, maxp4 = self._block_down(inputs=maxp3,
filters=init_filter * 8,
padding='SAME',
name='down_block4')
down_out5, maxp5 = self._block_down(inputs=maxp4,
filters=init_filter * 16,
padding='SAME',
name='down_block5')
# upsampling path
up_out4 = self._block_up(down_inputs=down_out4,
up_inputs=down_out5,
filters=init_filter * 8,
padding='SAME',
name='up_block4')
up_out3 = self._block_up(down_inputs=down_out3,
up_inputs=up_out4,
filters=init_filter * 4,
padding='SAME',
name='up_block3')
up_out2 = self._block_up(down_inputs=down_out2,
up_inputs=up_out3,
filters=init_filter * 2,
padding='SAME',
name='up_block2')
up_out1 = self._block_up(down_inputs=down_out1,
up_inputs=up_out2,
filters=init_filter,
padding='SAME',
name='up_block1')
# final layers
## TODO
self.logits = layers.conv2d(inputs=up_out1,
filters=self.num_classes,
kernel_size=1,
stride_size=1,
padding='SAME',
normalization=None,
activation=None,
mode=self.mode,
name='final_layer')
self.preds = tf.nn.sigmoid(self.logits)
self.next_labels = self.datapipe.next_labels
def _build_conv2d(inputs, spec, name):
opts = _get_options(spec)
inputs = layers.conv2d(inputs=inputs,
filters=spec['filters'],
kernel_size=spec['kernel_size'],
stride_size=spec['stride_size'],
padding=opts['padding'],
kernel_initializer=opts['kernel_initializer'],
bias_initializer=opts['bias_initializer'],
activation=opts['activation'],
name=name)
return inputs
def _build_graph(self, images, log_scope):
'''
# initial log scope
if self.log_scope is None:
shape = self.images.get_shape().as_list()
B, H, W, C = 16, shape[1], shape[2], 1
# np.ones([self.datapipe.batch_size, H, W, C]).astype(np.float32)
self.log_scope = tf.get_variable('log_scope0',
shape=[B, H, W, C],
initializer=tf.zeros_initializer(),
dtype=tf.float32,
trainable=False)
'''
inputs = tf.concat([images, log_scope], axis=-1)
# downsampling path
init_filter = 16
down_out1, maxp1 = self._block_down(inputs=inputs,
filters=init_filter,
padding='SAME',
scope='down_block1')
down_out2, maxp2 = self._block_down(inputs=maxp1,
filters=init_filter * 2,
padding='SAME',
scope='down_block2')
down_out3, maxp3 = self._block_down(inputs=maxp2,
filters=init_filter * 4,
padding='SAME',
scope='down_block3')
down_out4, maxp4 = self._block_down(inputs=maxp3,
filters=init_filter * 8,
padding='SAME',
scope='down_block4')
down_out5, maxp5 = self._block_down(inputs=maxp4,
filters=init_filter * 16,
padding='SAME',
scope='down_block5')
# they put a 3-layer MLP here
shape = down_out4.get_shape().as_list()
H, W, C = shape[1], shape[2], shape[3]
print('down_out4: ', shape)
print('down_out5: ', down_out5.shape)
out = layers.flatten(down_out5)
print('flatten shape: ', out.shape)
out = layers.dense(inputs=out,
units=128,
activation=tf.nn.relu,
name='layer1')
out = layers.dense(inputs=out,
units=128,
activation=tf.nn.relu,
name='layer2')
out = layers.dense(inputs=out,
units=H * W * C,
activation=tf.nn.relu,
name='layer3')
out = tf.reshape(out, shape=[-1, H, W, C])
print('upsampling input shape: ', out.shape)
# upsampling path
up_out4 = self._block_up(down_inputs=down_out4,
up_inputs=out,
filters=init_filter * 8,
padding='SAME',
scope='up_block4',
upsampling=False)
print('built up_out4...')
up_out3 = self._block_up(down_inputs=down_out3,
up_inputs=up_out4,
filters=init_filter * 4,
padding='SAME',
scope='up_block3')
print('built up_out3...')
up_out2 = self._block_up(down_inputs=down_out2,
up_inputs=up_out3,
filters=init_filter * 2,
padding='SAME',
scope='up_block2')
print('built up_out2...')
up_out1 = self._block_up(down_inputs=down_out1,
up_inputs=up_out2,
filters=init_filter,
padding='SAME',
scope='up_block1')
print('built up_out1...')
# final layers
## TODO
logits = layers.conv2d(inputs=up_out1,
filters=1,
kernel_size=1,
stride_size=1,
padding='SAME',
normalization=None,
activation=None,
mode=self.mode,
name='final_layer')
'''
# log softmax DOES NOT WORK PROPERLY
# compute log_softmax for the current attention
shape = tf.shape(logits)
N, H, W, C = shape[0], shape[1], shape[2], shape[3]
print('logits shape before: ', logits.shape)
logits = layers.flatten(logits)
print('logits shape: ', logits.shape)
log_softmax = tf.nn.log_softmax(logits=logits,
axis=-1)
log_a_k = tf.reshape(log_softmax, [N, H, W, C])
print('log_softmax shape: ', log_a_k.shape)
'''
log_a_k = tf.log_sigmoid(logits)
return log_a_k
