From 54818d253649ff588ed0054d10dabb2a3a170309 Mon Sep 17 00:00:00 2001
From: natanielruiz <nataniel777@hotmail.com>
Date: 星期四, 10 八月 2017 04:08:12 +0800
Subject: [PATCH] Doing pretty well now with resnet50 and adam with low learning rate. Also fixed test script to use large batches.

---
 practice/.ipynb_checkpoints/smoothing_ypr-checkpoint.ipynb |   42 +++++++++++++++++++++++++-----------------
 1 files changed, 25 insertions(+), 17 deletions(-)

diff --git a/practice/.ipynb_checkpoints/smoothing_ypr-checkpoint.ipynb b/practice/.ipynb_checkpoints/smoothing_ypr-checkpoint.ipynb
index a411c30..8102abf 100644
--- a/practice/.ipynb_checkpoints/smoothing_ypr-checkpoint.ipynb
+++ b/practice/.ipynb_checkpoints/smoothing_ypr-checkpoint.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 156,
+   "execution_count": 187,
    "metadata": {
     "collapsed": false
    },
@@ -17,7 +17,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 157,
+   "execution_count": 188,
    "metadata": {
     "collapsed": false
    },
@@ -26,13 +26,13 @@
     "video_path = '../data/video/SGT036_2016_07_25_pivothead_AVI.avi'\n",
     "bbox_path = '../data/video/annotations/SGT036_childface.txt'\n",
     "\n",
-    "annot_path = '../output/video/output-SGT036_resnet18_cr_epoch_1.txt'\n",
-    "output_string = 'SGT036_resnet18_cr_epoch_1_flat_smoothed'"
+    "annot_path = '../output/video/output-SGT036_resnet50_lowlr_epoch_20.txt'\n",
+    "output_string = 'SGT036_resnet50_lowlr_epoch_20_smoothed'"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 158,
+   "execution_count": 189,
    "metadata": {
     "collapsed": false
    },
@@ -41,7 +41,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[-6.069214 -0.831665  0.53318  ..., -3.836042 -3.868275 -2.377155]\n",
+      "[ 4.170376  0.790443 -0.178368 ..., -3.437805  0.396835 -1.276176]\n",
       "(8508,)\n",
       "(53464,)\n"
      ]
@@ -93,7 +93,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 159,
+   "execution_count": 190,
    "metadata": {
     "collapsed": false
    },
@@ -107,31 +107,39 @@
     }
    ],
    "source": [
-    "window_len = 6\n",
+    "window_len = 5\n",
     "pad = window_len / 2\n",
     "window = 'flat'\n",
+    "window_2 = 'flat'\n",
+    "window_len_2 = 7\n",
+    "pad_2 = window_len_2 / 2\n",
     "\n",
     "s = np.r_[y[window_len-1:0:-1],y,y[-2:-window_len-1:-1]]\n",
     "t = np.r_[p[window_len-1:0:-1],p,p[-2:-window_len-1:-1]]\n",
     "u = np.r_[r[window_len-1:0:-1],r,r[-2:-window_len-1:-1]]\n",
     "\n",
-    "xa = np.r_[x_min_arr[window_len-1:0:-1],x_min_arr,x_min_arr[-2:-window_len-1:-1]]\n",
-    "xb = np.r_[x_max_arr[window_len-1:0:-1],x_max_arr,x_max_arr[-2:-window_len-1:-1]]\n",
-    "ya = np.r_[y_min_arr[window_len-1:0:-1],y_min_arr,y_min_arr[-2:-window_len-1:-1]]\n",
-    "yb = np.r_[y_max_arr[window_len-1:0:-1],y_max_arr,y_max_arr[-2:-window_len-1:-1]]\n",
+    "xa = np.r_[x_min_arr[window_len_2-1:0:-1],x_min_arr,x_min_arr[-2:-window_len_2-1:-1]]\n",
+    "xb = np.r_[x_max_arr[window_len_2-1:0:-1],x_max_arr,x_max_arr[-2:-window_len_2-1:-1]]\n",
+    "ya = np.r_[y_min_arr[window_len_2-1:0:-1],y_min_arr,y_min_arr[-2:-window_len_2-1:-1]]\n",
+    "yb = np.r_[y_max_arr[window_len_2-1:0:-1],y_max_arr,y_max_arr[-2:-window_len_2-1:-1]]\n",
     "\n",
     "if window == 'flat':\n",
     "    w=np.ones(window_len, 'd')\n",
     "else:\n",
     "    w=eval('np.' + window + '(window_len)')\n",
+    "    \n",
+    "if window_2 == 'flat':\n",
+    "    w_2=np.ones(window_len_2, 'd')\n",
+    "else:\n",
+    "    w_2=eval('np.' + window_2 + '(window_len_2)')    \n",
     "\n",
     "y = np.convolve(w / w.sum(), s, mode='valid')[pad:-pad]\n",
     "p = np.convolve(w / w.sum(), t, mode='valid')[pad:-pad]\n",
     "r = np.convolve(w / w.sum(), u, mode='valid')[pad:-pad]\n",
-    "x_min_arr = np.convolve(w / w.sum(), xa, mode='valid')[pad:-pad]\n",
-    "x_max_arr = np.convolve(w / w.sum(), xb, mode='valid')[pad:-pad]\n",
-    "y_min_arr = np.convolve(w / w.sum(), ya, mode='valid')[pad:-pad]\n",
-    "y_max_arr = np.convolve(w / w.sum(), yb, mode='valid')[pad:-pad]\n",
+    "x_min_arr = np.convolve(w_2 / w_2.sum(), xa, mode='valid')[pad_2:-pad_2]\n",
+    "x_max_arr = np.convolve(w_2 / w_2.sum(), xb, mode='valid')[pad_2:-pad_2]\n",
+    "y_min_arr = np.convolve(w_2 / w_2.sum(), ya, mode='valid')[pad_2:-pad_2]\n",
+    "y_max_arr = np.convolve(w_2 / w_2.sum(), yb, mode='valid')[pad_2:-pad_2]\n",
     "\n",
     "pose_dict = {}\n",
     "bbox_dict = {}\n",
@@ -151,7 +159,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 160,
+   "execution_count": 191,
    "metadata": {
     "collapsed": false
    },

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