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, seed, batch_size, update_collection): 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) h4 = tf.nn.relu(h4) # h5 = lib.ops.conv2d.Conv2D('g_h5', dim, 3, 3, h4) if self.format == 'NHWC': h4 = tf.transpose(h4, [0, 2, 3, 1]) # NCHW to NHWC h5 = deconv2d(h4, self.data_format(batch_size, s1, s1, self.c_dim), name=self.prefix + 'g_h5') return tf.nn.sigmoid(h5)
def network(self, seed, y, batch_size, update_collection): 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 if self.output_size == 64: s32 = 4 z_ = linear(seed, self.dim * 16 * s32 * s32, self.prefix + 'h0_lin') h0 = tf.reshape(z_, [-1, self.dim * 16, s32, s32]) # NCHW format if self.output_size == 64: h0_bis = h0 else: h0_bis = block.ResidualBlock(self.prefix + 'res0_bis', 16 * self.dim, 16 * self.dim, 3, h0, y=y, num_classes=self.num_classes, resample='up', mode='cond_batchnorm') h1 = block.ResidualBlock(self.prefix + 'res1', 16 * self.dim, 8 * self.dim, 3, h0_bis, y=y, num_classes=self.num_classes, resample='up', mode='cond_batchnorm') h2 = block.ResidualBlock(self.prefix + 'res2', 8 * self.dim, 4 * self.dim, 3, h1, y=y, num_classes=self.num_classes, resample='up', mode='cond_batchnorm') h3 = block.ResidualBlock(self.prefix + 'res3', 4 * self.dim, 2 * self.dim, 3, h2, y=y, num_classes=self.num_classes, resample='up', mode='cond_batchnorm') h4 = block.ResidualBlock(self.prefix + 'res4', 2 * self.dim, self.dim, 3, h3, y=y, num_classes=self.num_classes, resample='up', mode='cond_batchnorm') h4 = ops.batchnorm.Batchnorm('g_h4', [0, 2, 3], h4) h4 = tf.nn.relu(h4) if self.format == 'NHWC': h4 = tf.transpose(h4, [0, 2, 3, 1]) # NCHW to NHWC h5 = deconv2d(h4, self.data_format(batch_size, s1, s1, self.c_dim), k_h=3, k_w=3, d_h=1, d_w=1, name=self.prefix + 'g_h5') return tf.nn.sigmoid(h5)
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}