def test_elu(self): x = backend.placeholder(ndim=2) f = backend.function([x], [activations.elu(x, 0.5)]) test_values = np.random.random((2, 5)) result = f([test_values])[0] self.assertAllClose(result, test_values, rtol=1e-05) negative_values = np.array([[-1, -2]], dtype=backend.floatx()) result = f([negative_values])[0] true_result = (np.exp(negative_values) - 1) / 2 self.assertAllClose(result, true_result)
def get_encoder(self, inputs, is_reuse=False, is_training=True):
if not is_reuse:
self.g_bn0_0 = BatchNormalization(axis=self.bn_axis,
name='g_k_bn0_0',
scale=True,
fused=True)
self.g_bn0_1 = BatchNormalization(axis=self.bn_axis,
name='g_k_bn0_1',
scale=True,
fused=True)
self.g_bn0_2 = BatchNormalization(axis=self.bn_axis,
name='g_k_bn0_2',
scale=True,
fused=True)
self.g_bn0_3 = BatchNormalization(axis=self.bn_axis,
name='g_k_bn0_3',
scale=True,
fused=True)
self.g_bn1_0 = BatchNormalization(axis=self.bn_axis,
name='g_k_bn1_0',
scale=True,
fused=True)
self.g_bn1_1 = BatchNormalization(axis=self.bn_axis,
name='g_k_bn1_1',
scale=True,
fused=True)
self.g_bn1_2 = BatchNormalization(axis=self.bn_axis,
name='g_k_bn1_2',
scale=True,
fused=True)
self.g_bn1_3 = BatchNormalization(axis=self.bn_axis,
name='g_k_bn1_3',
scale=True,
fused=True)
self.g_bn2_0 = BatchNormalization(axis=self.bn_axis,
name='g_k_bn2_0',
scale=True,
fused=True)
self.g_bn2_1 = BatchNormalization(axis=self.bn_axis,
name='g_k_bn2_1',
scale=True,
fused=True)
self.g_bn2_2 = BatchNormalization(axis=self.bn_axis,
name='g_k_bn2_2',
scale=True,
fused=True)
self.g_bn2_3 = BatchNormalization(axis=self.bn_axis,
name='g_k_bn2_3',
scale=True,
fused=True)
self.g_bn3_0 = BatchNormalization(axis=self.bn_axis,
name='g_k_bn3_0',
scale=True,
fused=True)
self.g_bn3_1 = BatchNormalization(axis=self.bn_axis,
name='g_k_bn3_1',
scale=True,
fused=True)
self.g_bn3_2 = BatchNormalization(axis=self.bn_axis,
name='g_k_bn3_2',
scale=True,
fused=True)
self.g_bn3_3 = BatchNormalization(axis=self.bn_axis,
name='g_k_bn3_3',
scale=True,
fused=True)
self.g_bn4_0 = BatchNormalization(axis=self.bn_axis,
name='g_k_bn4_0',
scale=True,
fused=True)
self.g_bn4_1 = BatchNormalization(axis=self.bn_axis,
name='g_k_bn4_1',
scale=True,
fused=True)
self.g_bn4_2 = BatchNormalization(axis=self.bn_axis,
name='g_k_bn4_2',
scale=True,
fused=True)
self.g_bn4_c = BatchNormalization(axis=self.bn_axis,
name='g_h_bn4_c',
scale=True,
fused=True)
self.g_bn5 = BatchNormalization(axis=self.bn_axis,
name='g_k_bn5',
scale=True,
fused=True)
self.g_bn5_m = BatchNormalization(axis=self.bn_axis,
name='g_k_bn5_m',
scale=True,
fused=True)
self.g_bn5_ill = BatchNormalization(axis=self.bn_axis,
name='g_k_bn5_ill',
scale=True,
fused=True)
self.g_bn5_shape = BatchNormalization(axis=self.bn_axis,
name='g_k_bn5_shape',
scale=True,
fused=True)
self.g_bn5_col = BatchNormalization(axis=self.bn_axis,
name='g_k_bn5_col',
scale=True,
fused=True)
self.g_bn5_exp = BatchNormalization(axis=self.bn_axis,
name='g_k_bn5_exo',
scale=True,
fused=True)
self.g_bn5_tex = BatchNormalization(axis=self.bn_axis,
name='g_k_bn5_tex',
scale=True,
fused=True)
# inputs are of size 224 x 224 x 3
k0_1 = elu(
self.g_bn0_1(Conv2D(self.gf_dim * 1, (7, 7), (2, 2),
padding='SAME',
use_bias=False,
name='g_k01_conv')(inputs),
training=is_training))
k0_2 = elu(
self.g_bn0_2(Conv2D(self.gf_dim * 2, (3, 3), (1, 1),
padding='SAME',
use_bias=False,
name='g_k02_conv')(k0_1),
training=is_training))
k1_0 = elu(
self.g_bn1_0(Conv2D(self.gf_dim * 2, (3, 3), (2, 2),
padding='SAME',
use_bias=False,
name='g_k10_conv')(k0_2),
training=is_training))
k1_1 = elu(
self.g_bn1_1(Conv2D(self.gf_dim * 2, (3, 3), (1, 1),
padding='SAME',
use_bias=False,
name='g_k11_conv')(k1_0),
training=is_training))
k1_2 = elu(
self.g_bn1_2(Conv2D(self.gf_dim * 4, (3, 3), (1, 1),
padding='SAME',
use_bias=False,
name='g_k12_conv')(k1_1),
training=is_training))
k2_0 = elu(
self.g_bn2_0(Conv2D(self.gf_dim * 4, (3, 3), (2, 2),
padding='SAME',
use_bias=False,
name='g_k20_conv')(k1_2),
training=is_training))
k2_1 = elu(
self.g_bn2_1(Conv2D(self.gf_dim * 3, (3, 3), (1, 1),
padding='SAME',
use_bias=False,
name='g_k21_conv')(k2_0),
training=is_training))
k2_2 = elu(
self.g_bn2_2(Conv2D(self.gf_dim * 6, (3, 3), (1, 1),
padding='SAME',
use_bias=False,
name='g_k22_conv')(k2_1),
training=is_training))
k3_0 = elu(
self.g_bn3_0(Conv2D(self.gf_dim * 6, (3, 3), (2, 2),
padding='SAME',
use_bias=False,
name='g_k30_conv')(k2_2),
training=is_training))
k3_1 = elu(
self.g_bn3_1(Conv2D(self.gf_dim * 4, (3, 3), (1, 1),
padding='SAME',
use_bias=False,
name='g_k31_conv')(k3_0),
training=is_training))
k3_2 = elu(
self.g_bn3_2(Conv2D(self.gf_dim * 8, (3, 3), (1, 1),
padding='SAME',
use_bias=False,
name='g_k32_conv')(k3_1),
training=is_training))
k4_0 = elu(
self.g_bn4_0(Conv2D(self.gf_dim * 8, (3, 3), (2, 2),
padding='SAME',
use_bias=False,
name='g_k40_conv')(k3_2),
training=is_training))
k4_1 = elu(
self.g_bn4_1(Conv2D(self.gf_dim * 5, (3, 3), (1, 1),
padding='SAME',
use_bias=False,
name='g_k41_conv')(k4_0),
training=is_training))
# Pose
k51_m = self.g_bn5_m(Conv2D(int(self.gfc_dim / 8), (3, 3), (1, 1),
padding='SAME',
use_bias=False,
name='g_k5_m_conv')(k4_1),
training=is_training)
k51_shape_ = k51_m.shape
k52_m = AveragePooling2D(pool_size=[k51_shape_[1], k51_shape_[2]],
strides=[1, 1],
padding='VALID')(k51_m)
k52_m = tf.reshape(k52_m, [-1, int(self.gfc_dim / 8)])
k6_m = Dense(self.m_dim, name='g_k6_m_lin')(k52_m)
# Illumination
k51_ill = self.g_bn5_ill(Conv2D(int(self.gfc_dim / 8), (3, 3), (1, 1),
padding='SAME',
name='g_k5_il_conv')(k4_1),
training=is_training)
k52_ill = AveragePooling2D(pool_size=[k51_shape_[1], k51_shape_[2]],
strides=[1, 1],
padding='VALID')(k51_ill)
k52_ill = tf.reshape(k52_ill, [-1, int(self.gfc_dim / 8)])
k6_ill = Dense(self.il_dim, name='g_k6_ill_lin')(k52_ill)
# Shape
k51_shape = self.g_bn5_shape(Conv2D(self.sh_dim, (3, 3), (1, 1),
padding='SAME',
name='g_k5_shape_conv')(k4_1),
training=is_training)
k52_shape = AveragePooling2D(pool_size=[k51_shape_[1], k51_shape_[2]],
strides=[1, 1],
padding='VALID')(k51_shape)
k52_shape = tf.reshape(k52_shape, [-1, self.sh_dim])
# Texture
k51_tex = self.g_bn5_tex(Conv2D(self.tx_dim, (3, 3), (1, 1),
padding='SAME',
name='g_k5_tex_conv')(k4_1),
training=is_training)
k52_tex = AveragePooling2D(pool_size=[k51_shape_[1], k51_shape_[2]],
strides=[1, 1],
padding='VALID')(k51_tex)
k52_tex = tf.reshape(k52_tex, [-1, self.tx_dim])
# Expression
k51_exp = self.g_bn5_exp(Conv2D(self.ep_dim, (3, 3), (1, 1),
padding='SAME',
name='g_k5_exp_conv')(k4_1),
training=is_training)
k52_exp = AveragePooling2D(pool_size=[k51_shape_[1], k51_shape_[2]],
strides=[1, 1],
padding='VALID')(k51_exp)
k52_exp = tf.reshape(k52_exp, [-1, self.ep_dim])
# Color
k51_col = self.g_bn5_col(Conv2D(int(self.gfc_dim / 8), (3, 3), (1, 1),
padding='SAME',
name='g_k5_col_conv')(k4_1),
training=is_training)
k52_col = AveragePooling2D(pool_size=[k51_shape_[1], k51_shape_[2]],
strides=[1, 1],
padding='VALID')(k51_col)
k52_col = tf.reshape(k52_col, [-1, int(self.gfc_dim / 8)])
k6_col = Dense(self.co_dim, name='g_k6_col_lin')(k52_col)
return k52_shape, k52_tex, k52_exp, k6_m, k6_ill, k6_col