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Panos Achlioptas
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‎notebooks/train_single_class_ae.ipynb‎

Lines changed: 149 additions & 57 deletions
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 23,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"import os.path as osp\n",
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"\n",
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"from latent_3d_points.src.ae_templates import mlp_architecture_ala_iclr_18, default_train_params\n",
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"from latent_3d_points.src.autoencoder import Configuration as Conf\n",
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"from latent_3d_points.src.point_net_ae import PointNetAutoEncoder\n",
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"from latent_3d_points.src.in_out import snc_category_to_synth_id"
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"from latent_3d_points.src.in_out import snc_category_to_synth_id\n",
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"\n",
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"from latent_3d_points.external.general_tools.in_out.basics import create_dir\n",
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"from latent_3d_points.external.general_tools.notebook.tf import reset_tf_graph"
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]
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},
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{
@@ -23,23 +28,13 @@
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"outputs": [],
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"source": [
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"# from tf_lab.in_out.basics import Data_Splitter\n",
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"\n",
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"# from tf_lab.point_clouds.in_out import load_point_clouds_from_filenames, PointCloudDataSet\n",
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"\n",
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"# from tf_lab.data_sets.shape_net import pc_loader as snc_loader\n",
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"# from tf_lab.iclr.helper import load_multiple_version_of_pcs, find_best_validation_epoch_from_train_stats\n",
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"\n",
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"# from tf_lab.iclr.helper import load_multiple_version_of_pcs, find_best_validation_epoch_from_train_stats"
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"# TODO : DATA LOADING"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
@@ -53,23 +48,151 @@
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"%matplotlib inline"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Define Basic Parameters"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 26,
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"metadata": {
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"collapsed": true
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"collapsed": false
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},
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"outputs": [],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Give me the class name (e.g. \"chair\"): chair\n"
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]
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}
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],
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"source": [
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"top_data_dir = '/orions4-zfs/projects/optas/DATA/'\n",
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"experiment_tag = 'mlp_with_split_1pc_usampled_bnorm_on_encoder_only'\n",
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"top_data_dir = '../data/'\n",
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"experiment_name = 'single_class_ae'\n",
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"n_pc_points = 2048\n",
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"\n",
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"class_name = raw_input('Give me the class type: ').lower()\n",
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"class_name = raw_input('Give me the class name (e.g. \"chair\"): ').lower()\n",
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"bneck_size = 128\n",
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"ae_loss = 'emd'"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Use Default Training Parameters\n",
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"\n",
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"{'batch_size': 50,\n",
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" 'denoising': False, # By default our AE is not denoising.\n",
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" 'learning_rate': 0.0005,\n",
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" 'loss_display_step': 1,\n",
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" 'saver_step': 10,\n",
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" 'training_epochs': 500,\n",
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" 'z_rotate': False}\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 38,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"train_dir = create_dir(osp.join(top_data_dir, experiment_name))\n",
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"train_params = default_train_params()\n",
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"encoder, decoder, enc_args, dec_args = mlp_architecture_ala_iclr_18(n_pc_points, bneck_size)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 39,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"conf = Conf(n_input = [n_pc_points, 3],\n",
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" loss = ae_loss,\n",
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" training_epochs = train_params['training_epochs'],\n",
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" batch_size = train_params['batch_size'],\n",
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" denoising = train_params['denoising'],\n",
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" learning_rate = train_params['learning_rate'],\n",
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" train_dir = train_dir,\n",
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" loss_display_step = train_params['loss_display_step'],\n",
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" saver_step = train_params['saver_step'],\n",
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" z_rotate = train_params['z_rotate'],\n",
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" encoder = encoder,\n",
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" decoder = decoder,\n",
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" encoder_args = enc_args,\n",
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" decoder_args = dec_args\n",
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" )\n",
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"conf.experiment_name = experiment_name\n",
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"conf.held_out_step = 5\n",
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"conf.save(osp.join(train_dir, 'configuration'))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Build AE Model."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 33,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Building Encoder\n",
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"encoder_conv_layer_0 conv params = 256 bnorm params = 128\n",
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"Tensor(\"single_class_ae_2/Relu:0\", shape=(?, 2048, 64), dtype=float32)\n",
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"output size: 131072 \n",
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"\n",
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"encoder_conv_layer_1 conv params = 8320 bnorm params = 256\n",
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"Tensor(\"single_class_ae_2/Relu_1:0\", shape=(?, 2048, 128), dtype=float32)\n",
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"output size: 262144 \n",
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"\n",
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"encoder_conv_layer_2 conv params = 16512 bnorm params = 256\n",
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"Tensor(\"single_class_ae_2/Relu_2:0\", shape=(?, 2048, 128), dtype=float32)\n",
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"output size: 262144 \n",
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"\n",
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"encoder_conv_layer_3 conv params = 33024 bnorm params = 512\n",
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"Tensor(\"single_class_ae_2/Relu_3:0\", shape=(?, 2048, 256), dtype=float32)\n",
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"output size: 524288 \n",
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"\n",
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"encoder_conv_layer_4 conv params = 32896 bnorm params = 256\n",
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"Tensor(\"single_class_ae_2/Relu_4:0\", shape=(?, 2048, 128), dtype=float32)\n",
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"output size: 262144 \n",
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"\n",
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"Tensor(\"single_class_ae_2/Max:0\", shape=(?, 128), dtype=float32)\n",
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"Building Decoder\n",
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"decoder_fc_0 FC params = 33024 Tensor(\"single_class_ae_2/Relu_5:0\", shape=(?, 256), dtype=float32)\n",
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"output size: 256 \n",
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"\n",
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"decoder_fc_1 FC params = 65792 Tensor(\"single_class_ae_2/Relu_6:0\", shape=(?, 256), dtype=float32)\n",
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"output size: 256 \n",
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"\n",
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"decoder_fc_2 FC params = 1579008 Tensor(\"single_class_ae_2/decoder_fc_2/BiasAdd:0\", shape=(?, 6144), dtype=float32)\n",
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"output size: 6144 \n",
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"\n"
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]
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}
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],
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"source": [
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"reset_tf_graph()\n",
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"ae = PointNetAutoEncoder(conf.experiment_name, conf)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
@@ -78,42 +201,11 @@
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},
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"outputs": [],
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"source": [
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"top_lin_dir = '/orions4-zfs/projects/lins2/Panos_Space/DATA/'\n",
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"train_params = default_train_params()\n",
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"\n",
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"for bneck in bneck_list:\n",
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" experiment_id = '_'.join(['ae', class_name, experiment_tag, str(n_pc_points), 'pts', str(bneck), 'bneck', loss])\n",
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" train_dir = osp.join(top_lin_dir, 'OUT/iclr/nn_models/', experiment_id)\n",
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" create_dir(train_dir)\n",
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"\n",
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" reset_tf_graph() \n",
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" encoder, decoder, enc_args, dec_args = mlp_architecture_ala_iclr_18(n_pc_points, bneck) \n",
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" conf = Conf(n_input = [n_pc_points, 3],\n",
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" loss = loss,\n",
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" training_epochs = 500,\n",
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" batch_size = train_params['batch_size'],\n",
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" denoising = False,\n",
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" learning_rate = train_params['learning_rate'],\n",
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" train_dir = train_dir,\n",
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" loss_display_step = 1,\n",
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" saver_step = train_params['saver_step'],\n",
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" z_rotate = False,\n",
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" encoder = encoder,\n",
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" decoder = decoder,\n",
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" encoder_args = enc_args,\n",
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" decoder_args = dec_args\n",
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" )\n",
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" print conf\n",
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" conf.experiment_name = 'experiment_' + str(experiment_id)\n",
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" conf.held_out_step = 5\n",
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" conf.save(osp.join(train_dir, 'configuration'))\n",
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" ae = PointNetAutoEncoder(conf.experiment_name, conf)\n",
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"\n",
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" buf_size = 1 # flush each line\n",
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" fout = open(osp.join(conf.train_dir, 'train_stats.txt'), 'a', buf_size)\n",
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"# train_stats = ae.train(in_data['train'], conf, log_file=fout, held_out_data=in_data['val'])\n",
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" train_stats = ae.train(in_data['train'], conf, log_file=fout, held_out_data=in_data['test'])\n",
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" fout.close()"
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"# Start training\n",
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"buf_size = 1 # flush each line\n",
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"fout = open(osp.join(conf.train_dir, 'train_stats.txt'), 'a', buf_size)\n",
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"train_stats = ae.train(in_data['train'], conf, log_file=fout, held_out_data=in_data['val'])\n",
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"fout.close()"
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]
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}
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],

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