Merge pull request RobotLocomotion#149 from RobotLocomotion/lm-exp-dev

add figures to make descriptor dimension

Author: manuelli

dense_correspondence/evaluation/evaluation.py

@@ -1618,6 +1618,9 @@ def get_random_scenes_and_image_pairs(dataset):
             img_pairs.append([img_a_idx, img_b_idx])
             scene_names.append(scene_name)
 
+            img_pairs.append([0, 0])
+            scene_names.append(scene_name)
+
         return scene_names, img_pairs
 
     @staticmethod
@@ -2309,7 +2312,7 @@ def make_pixel_match_error_plot(ax, df, label=None, num_bins=100, masked=False):
         if masked:
             ax.set_xlabel('Pixel match error (masked), L2 (pixel distance)')
         else:
-            ax.set_xlabel('Pixel match error, L2 (pixel distance)')
+            ax.set_xlabel('Pixel match error (fraction of image), L2 (pixel distance)')
         ax.set_ylabel('Fraction of images')
 
         # ax.set_xlim([0,200])

dense_correspondence/evaluation/plotting.py

@@ -19,9 +19,10 @@ def normalize_descriptor(res, stats=None):
         res_min = np.array(stats['min'])
         res_max = np.array(stats['max'])
 
+    normed_res = np.clip(res, res_min, res_max)
     eps = 1e-10
     scale = (res_max - res_min) + eps
-    normed_res = (res - res_min) / scale
+    normed_res = (normed_res - res_min) / scale
     return normed_res
 
 def normalize_descriptor_pair(res_a, res_b):

dense_correspondence/experiments/bag_of_tricks/eval_bag_of_tricks.ipynb

@@ -49,15 +49,21 @@
     "for net in all_nets:\n",
     "#     if \"no_dr\" in net:\n",
     "#         continue\n",
+    "\n",
+    "    \n",
     "    nets_to_plot.append(os.path.join(folder_name,net))\n",
     "\n",
     "    \n",
-    "# nets_list = []\n",
-    "# nets_list.append(\"caterpillar_dont_scale_hard_negatives_e3_3d\")\n",
-    "# nets_list.append(\"caterpillar_scale_hard_negatives_e0_3d\")\n",
+    "nets_list = []\n",
+    "nets_to_plot = []\n",
+    "nets_list.append(\"standard_3\")\n",
+    "nets_list.append(\"dont_scale_hard_negatives_3\")\n",
+    "nets_list.append(\"dont_sample_from_mask_3\")\n",
+    "nets_list.append(\"no_tricks_3\")\n",
     "\n",
-    "# for net in nets_list:\n",
-    "#     nets_to_plot.append(os.path.join(\"l2_questions/\",net))\n",
+    "\n",
+    "for net in nets_list:\n",
+    "    nets_to_plot.append(os.path.join(folder_name,net))\n",
     "\n",
     "\n",
     "    \n",

dense_correspondence/experiments/bag_of_tricks/qualitative_plots.ipynb

@@ -0,0 +1,94 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Bag of Tricks Qualitative Plots\n",
+    "Eval class consistent shoes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "\n",
+    "import random\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import os\n",
+    "import cv2\n",
+    "import torch\n",
+    "import dense_correspondence_manipulation.utils.utils as utils\n",
+    "utils.add_dense_correspondence_to_python_path()\n",
+    "\n",
+    "import dense_correspondence\n",
+    "from dense_correspondence.evaluation.evaluation import *\n",
+    "import dense_correspondence.correspondence_tools.correspondence_plotter as correspondence_plotter\n",
+    "from dense_correspondence.dataset.dense_correspondence_dataset_masked import ImageType"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "config_filename = os.path.join(utils.getDenseCorrespondenceSourceDir(), 'config', \n",
+    "                               'dense_correspondence', 'evaluation', 'lucas_evaluation.yaml')\n",
+    "config = utils.getDictFromYamlFilename(config_filename)\n",
+    "default_config = utils.get_defaults_config()\n",
+    "\n",
+    "\n",
+    "utils.set_cuda_visible_devices([0])\n",
+    "\n",
+    "dce = DenseCorrespondenceEvaluation(config)\n",
+    "\n",
+    "DCE = DenseCorrespondenceEvaluation\n",
+    "\n",
+    "\n",
+    "\n",
+    "# network_name = \"standard_3\"\n",
+    "network_name = \"baymax_M_background_0.500_3\"\n",
+    "# network_name = \"starbot_M_background_0.500_3\"\n",
+    "# dcn = dce.load_network_from_config(network_name)\n",
+    "# dataset = dcn.load_training_dataset()\n",
+    "\n",
+    "# network_name = \"baymax_starbot_caterpillar_only_multi_3\"\n",
+    "dcn = dce.load_network_from_config(network_name)\n",
+    "dataset = dcn.load_training_dataset()\n",
+    "\n",
+    "\n",
+    "DenseCorrespondenceEvaluation.evaluate_network_qualitative(dcn, dataset=dataset, randomize=True)\n",
+    "\n",
+    "# DenseCorrespondenceEvaluation.evaluate_network_qualitative_cross_scene(dcn, dataset=dataset)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}