def network(self, image, batch_size, update_collection): h0 = lrelu( conv2d(image, self.dim, name=self.prefix + 'h0_conv', update_collection=update_collection, with_sn=self.with_sn, scale=self.scale, with_learnable_sn_scale=self.with_learnable_sn_scale, data_format=self.format, with_singular_values=True)) h1 = lrelu( self.d_bn1( conv2d(h0, self.dim * 2, name=self.prefix + 'h1_conv', update_collection=update_collection, with_sn=self.with_sn, scale=self.scale, with_learnable_sn_scale=self.with_learnable_sn_scale, data_format=self.format, with_singular_values=True))) h2 = lrelu( self.d_bn2( conv2d(h1, self.dim * 4, name=self.prefix + 'h2_conv', update_collection=update_collection, with_sn=self.with_sn, scale=self.scale, with_learnable_sn_scale=self.with_learnable_sn_scale, data_format=self.format, with_singular_values=True))) h3 = lrelu( self.d_bn3( conv2d(h2, self.dim * 8, name=self.prefix + 'h3_conv', update_collection=update_collection, with_sn=self.with_sn, scale=self.scale, with_learnable_sn_scale=self.with_learnable_sn_scale, data_format=self.format, with_singular_values=True))) hF = lrelu( self.d_bn4( conv2d(h3, self.o_dim, name=self.prefix + 'hF_conv', update_collection=update_collection, with_sn=self.with_sn, scale=self.scale, with_learnable_sn_scale=self.with_learnable_sn_scale, with_singular_values=True))) hF = tf.reshape(hF, [batch_size, -1]) return {'h0': h0, 'h1': h1, 'h2': h2, 'h3': h3, 'hF': hF}
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, image, batch_size, update_collection): c0_0 = lrelu( conv2d(image, 64, 3, 3, 1, 1, with_sn=self.with_sn, with_learnable_sn_scale=self.with_learnable_sn_scale, update_collection=update_collection, stddev=0.02, name=self.prefix + 'c0_0', data_format=self.format, with_singular_values=True)) c0_1 = lrelu( conv2d(c0_0, 128, 4, 4, 2, 2, with_sn=self.with_sn, with_learnable_sn_scale=self.with_learnable_sn_scale, update_collection=update_collection, stddev=0.02, name=self.prefix + 'c0_1', data_format=self.format, with_singular_values=True)) c1_0 = lrelu( conv2d(c0_1, 128, 3, 3, 1, 1, with_sn=self.with_sn, with_learnable_sn_scale=self.with_learnable_sn_scale, update_collection=update_collection, stddev=0.02, name=self.prefix + 'c1_0', data_format=self.format, with_singular_values=True)) c1_1 = lrelu( conv2d(c1_0, 256, 4, 4, 2, 2, with_sn=self.with_sn, with_learnable_sn_scale=self.with_learnable_sn_scale, update_collection=update_collection, stddev=0.02, name=self.prefix + 'c1_1', data_format=self.format, with_singular_values=True)) c2_0 = lrelu( conv2d(c1_1, 256, 3, 3, 1, 1, with_sn=self.with_sn, with_learnable_sn_scale=self.with_learnable_sn_scale, update_collection=update_collection, stddev=0.02, name=self.prefix + 'c2_0', data_format=self.format, with_singular_values=True)) c2_1 = lrelu( conv2d(c2_0, 512, 4, 4, 2, 2, with_sn=self.with_sn, with_learnable_sn_scale=self.with_learnable_sn_scale, update_collection=update_collection, stddev=0.02, name=self.prefix + 'c2_1', data_format=self.format, with_singular_values=True)) c3_0 = lrelu( conv2d(c2_1, 512, 3, 3, 1, 1, with_sn=self.with_sn, with_learnable_sn_scale=self.with_learnable_sn_scale, update_collection=update_collection, stddev=0.02, name=self.prefix + 'c3_0', data_format=self.format, with_singular_values=True)) c3_0 = tf.reshape(c3_0, [batch_size, -1]) l4 = linear(c3_0, self.o_dim, with_sn=self.with_sn, with_learnable_sn_scale=self.with_learnable_sn_scale, update_collection=update_collection, stddev=0.02, name=self.prefix + 'l4') return { 'h0': c0_0, 'h1': c0_1, 'h2': c1_0, 'h3': c1_1, 'h4': c2_0, 'h5': c2_1, 'h6': c3_0, 'hF': l4 }
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}