def _encoder(self, image):
enc = res_encoder(image,
layer_dims=self.encoder_dims,
num_resblk=self.num_enc_resblk)
texture_enc = enc
shape_enc = enc
# first convolution on common encoding
if self.use_texture_conv:
stride = 2 if self.texture_downsample else 1
texture_enc = tf.nn.relu(conv2d(texture_enc, self.texture_dims,
3, stride,
name='enc_texture_conv'))
else:
assert self.texture_dims == self.encoder_dims, \
"Texture dim ({}) must match encoder dim ({}) " \
"if texture_conv is not used.".format(self.texture_dims,
self.encoder_dims)
assert not self.texture_downsample, \
"Must use texture_conv if texture_downsample."
if self.use_shape_conv:
shape_enc = tf.nn.relu(conv2d(shape_enc, self.shape_dims,
3, 1, name='enc_shape_conv'))
else:
assert self.shape_dims == self.encoder_dims, \
"Shape dim ({}) must match encoder dim ({}) " \
"if shape_conv is not used.".format(self.shape_dims,
self.encoder_dims)
for i in range(self.num_texture_resblk):
name = 'texture_enc_{}'.format(i)
if i == 0:
# for backward compatibility
name = 'texture_enc'
texture_enc = residual_block(texture_enc, self.texture_dims, 3, 1,
name)
for i in range(self.num_shape_resblk):
name = 'shape_enc_{}'.format(i)
if i == 0:
# for backward compatibility
name = 'shape_enc'
shape_enc = residual_block(shape_enc, self.shape_dims,
3, 1, name)
if self.use_shape_deconv:
shape_enc = deconv2d(shape_enc, 16,4,2)
# W_t3 = weight_variable([4, 4, int(self.encoder_dims/2), int(self.encoder_dims)])
# b_t3 = bias_variable([3])
# shape_enc = conv2d_transpose_strided(shape_enc, W_t3, b_t3)
up = tf.pad(shape_enc, [[0, 0], [1, 1], [1, 1], [0, 0]], "REFLECT")
shape_enc = conv2d(up, 3, 3, 1, padding='VALID', name='use_shape_deconv_conv')
return texture_enc, shape_enc
def _encoder(self, image):
enc = res_encoder(image,
layer_dims=self.encoder_dims,
num_resblk=self.num_enc_resblk)
texture_enc = enc
shape_enc = enc
# first convolution on common encoding
if self.use_texture_conv:
stride = 2 if self.texture_downsample else 1
texture_enc = tf.nn.relu(
conv2d(texture_enc,
self.texture_dims,
3,
stride,
name='enc_texture_conv'))
else:
assert self.texture_dims == self.encoder_dims, \
"Texture dim ({}) must match encoder dim ({}) " \
"if texture_conv is not used.".format(self.texture_dims,
self.encoder_dims)
assert not self.texture_downsample, \
"Must use texture_conv if texture_downsample."
if self.use_shape_conv:
shape_enc = tf.nn.relu(
conv2d(shape_enc, self.shape_dims, 3, 1,
name='enc_shape_conv'))
else:
assert self.shape_dims == self.encoder_dims, \
"Shape dim ({}) must match encoder dim ({}) " \
"if shape_conv is not used.".format(self.shape_dims,
self.encoder_dims)
for i in range(self.num_texture_resblk):
name = 'texture_enc_{}'.format(i)
if i == 0:
# for backward compatibility
name = 'texture_enc'
texture_enc = residual_block(texture_enc, self.texture_dims, 3, 1,
name)
for i in range(self.num_shape_resblk):
name = 'shape_enc_{}'.format(i)
if i == 0:
# for backward compatibility
name = 'shape_enc'
shape_enc = residual_block(shape_enc, self.shape_dims, 3, 1, name)
return texture_enc, shape_enc