def network(self, seed, batch_size): s1, s2, s4, s8, s16, s32 = conv_sizes(self.output_size, layers=5, stride=2) # project `z` and reshape z_ = linear(seed, self.dim * 16 * s32 * s32, self.prefix + 'h0_lin') h0 = tf.reshape(z_, [-1, s32, s32, self.dim * 16]) h0 = tf.nn.relu(self.g_bn0(h0)) h1 = deconv2d(h0, [batch_size, s16, s16, self.dim * 8], name=self.prefix + 'h1') h1 = tf.nn.relu(self.g_bn1(h1)) h2 = deconv2d(h1, [batch_size, s8, s8, self.dim * 4], name=self.prefix + 'h2') h2 = tf.nn.relu(self.g_bn2(h2)) h3 = deconv2d(h2, [batch_size, s4, s4, self.dim * 2], name=self.prefix + 'h3') h3 = tf.nn.relu(self.g_bn3(h3)) h4 = deconv2d(h3, [batch_size, s2, s2, self.dim], name=self.prefix + 'h4') h4 = tf.nn.relu(self.g_bn4(h4)) h5 = deconv2d(h4, [batch_size, s1, s1, self.c_dim], name=self.prefix + 'h5') return tf.nn.sigmoid(h5)
def network(self, seed, batch_size): s1, s2, s4, s8, s16 = conv_sizes(self.output_size, layers=4, stride=2) # 64, 32, 16, 8, 4 - for self.output_size = 64 # default architecture # For Cramer: self.gf_dim = 64 z_ = linear(seed, self.dim * 8 * s16 * s16, self.prefix + 'h0_lin') # project random noise seed and reshape h0 = tf.reshape(z_, [batch_size, s16, s16, self.dim * 8]) h0 = tf.nn.relu(self.g_bn0(h0)) h1 = deconv2d(h0, [batch_size, s8, s8, self.dim * 4], name=self.prefix + 'h1') h1 = tf.nn.relu(self.g_bn1(h1)) h2 = deconv2d(h1, [batch_size, s4, s4, self.dim * 2], name=self.prefix + 'h2') h2 = tf.nn.relu(self.g_bn2(h2)) h3 = deconv2d(h2, [batch_size, s2, s2, self.dim * 1], name=self.prefix + 'h3') h3 = tf.nn.relu(self.g_bn3(h3)) h4 = deconv2d(h3, [batch_size, s1, s1, self.c_dim], name=self.prefix + 'h4') return tf.nn.sigmoid(h4)
def network(self, image, batch_size): o_dim = self.o_dim if (self.o_dim > 0) else 8 * self.dim h0 = lrelu(conv2d(image, self.dim, name=self.prefix + 'h0_conv')) h1 = lrelu( self.d_bn1(conv2d(h0, self.dim * 2, name=self.prefix + 'h1_conv'))) h2 = lrelu( self.d_bn2(conv2d(h1, self.dim * 4, name=self.prefix + 'h2_conv'))) h3 = lrelu( self.d_bn3(conv2d(h2, self.dim * 8, name=self.prefix + 'h3_conv'))) hF = linear(tf.reshape(h3, [batch_size, -1]), o_dim, self.prefix + 'h4_lin') return {'h0': h0, 'h1': h1, 'h2': h2, 'h3': h3, 'hF': hF}
def network(self, image, batch_size): from core.resnet import block, ops 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, he_init=False)) 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') hF = tf.reshape(h4, [-1, 4 * 4 * 8 * self.dim]) hF = linear(hF, self.o_dim, self.prefix + 'h5_lin') return {'h0': h0, 'h1': h1, 'h2': h2, 'h3': h3, 'h4': h4, 'hF': hF}
def network(self, seed, batch_size): from core.resnet import block, ops s1, s2, s4, s8, s16, s32 = conv_sizes(self.output_size, layers=5, stride=2) # project `z` and reshape z_ = linear(seed, self.dim * 16 * s32 * s32, self.prefix + 'h0_lin') h0 = tf.reshape(z_, [-1, self.dim * 16, s32, s32]) # NCHW format h1 = block.ResidualBlock(self.prefix + 'res1', 16 * self.dim, 8 * self.dim, 3, h0, resample='up') h2 = block.ResidualBlock(self.prefix + 'res2', 8 * self.dim, 4 * self.dim, 3, h1, resample='up') h3 = block.ResidualBlock(self.prefix + 'res3', 4 * self.dim, 2 * self.dim, 3, h2, resample='up') h4 = block.ResidualBlock(self.prefix + 'res4', 2 * self.dim, self.dim, 3, h3, resample='up') h4 = ops.batchnorm.Batchnorm('g_h4', [0, 2, 3], h4, fused=True) h4 = tf.nn.relu(h4) # h5 = lib.ops.conv2d.Conv2D('g_h5', dim, 3, 3, h4) h5 = tf.transpose(h4, [0, 2, 3, 1]) # NCHW to NHWC h5 = deconv2d(h5, [batch_size, s1, s1, self.c_dim], name='g_h5') return tf.nn.sigmoid(h5)