test-trainers.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pylab import *\n",
    "rc(\"image\", cmap=\"hot\")\n",
    "import pylab\n",
    "import os\n",
    "import glob\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F\n",
    "import time\n",
    "import resource\n",
    "import psutil\n",
    "from contextlib import closing\n",
    "from torch.autograd import Variable\n",
    "import uuid\n",
    "import dlinputs as dli\n",
    "import dltrainers as dlt\n",
    "from dltrainers import layers as dlnn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ImageClassifierTrainer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "optimizer got an empty parameter list",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-6e35ad93eac2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSigmoid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m )\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mtr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdlt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mImageClassifierTrainer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnet\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrandn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m28\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m28\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrandn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/work/tmb/dltrainers/dltrainers/trainers.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, *args, **kw)\u001b[0m\n\u001b[1;32m    227\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mImageClassifierTrainer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mBasicTrainer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    228\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 229\u001b[0;31m         \u001b[0mBasicTrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    230\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    231\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mset_inputs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimages\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdepth1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/work/tmb/dltrainers/dltrainers/trainers.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, model, use_cuda, fields, input_axes, output_axes)\u001b[0m\n\u001b[1;32m     62\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mno_display\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcurrent_lr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_lr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1e-3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweighted\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mWeighted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mntrain\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/work/tmb/dltrainers/dltrainers/trainers.pyc\u001b[0m in \u001b[0;36mset_lr\u001b[0;34m(self, lr, momentum, weight_decay)\u001b[0m\n\u001b[1;32m     96\u001b[0m                                    \u001b[0mlr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     97\u001b[0m                                    \u001b[0mmomentum\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmomentum\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 98\u001b[0;31m                                    weight_decay=weight_decay)\n\u001b[0m\u001b[1;32m     99\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    100\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_outputs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/torch/optim/sgd.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, params, lr, momentum, dampening, weight_decay, nesterov)\u001b[0m\n\u001b[1;32m     55\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mnesterov\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mmomentum\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mdampening\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Nesterov momentum requires a momentum and zero dampening\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 57\u001b[0;31m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mSGD\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdefaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     58\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setstate__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/torch/optim/optimizer.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, params, defaults)\u001b[0m\n\u001b[1;32m     32\u001b[0m         \u001b[0mparam_groups\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     33\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparam_groups\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 34\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"optimizer got an empty parameter list\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     35\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparam_groups\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m             \u001b[0mparam_groups\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'params'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mparam_groups\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: optimizer got an empty parameter list"
     ]
    }
   ],
   "source": [
    "net = nn.Sequential(\n",
    "    dlnn.Conv2d(7, 3),\n",
    "    dlnn.Flat(),\n",
    "    dlnn.Linear(10),\n",
    "    nn.Sigmoid()\n",
    ")\n",
    "tr = dlt.ImageClassifierTrainer(net)\n",
    "x = randn(1, 28, 28, 1)\n",
    "y = randn(1, 10)\n",
    "tr.train_batch(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Image2ImageTrainer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(numpy.ndarray, float)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with dlm.specops:\n",
    "    model = Cr(7) | Mp(2) | Cl(10)\n",
    "net = model.create(1, 1, 28, 28)\n",
    "tr = dlt.Image2ImageTrainer(net)\n",
    "x = randn(1, 28, 28, 1)\n",
    "y = randn(1, 28, 28, 10)\n",
    "z, l = tr.train_batch(x, y)\n",
    "type(z), type(l)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Image2SeqTrainer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 1, 100, 28) -> (1L, 10L, 100L)\n",
      "(1, 1, 100, 28) -> (1L, 10L, 100L)\n",
      "Sequence(\n",
      "  (0): Conv2d(1, 7, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (1): ReLU()\n",
      "  (2): Img2Seq(\n",
      "  )\n",
      "  (3): Conv1d(196, 10, kernel_size=(3,), stride=(1,), padding=(1,))\n",
      ")\n",
      "\n",
      "( 0 ,.,.) = \n",
      "  0.1327  0.1116  0.1927  ...  -0.0458 -0.0143 -0.0647\n",
      "  0.0428 -0.1556  0.0588  ...  -0.1216 -0.2754 -0.1811\n",
      "  0.0979  0.3004  0.2792  ...   0.2474  0.1289  0.1319\n",
      "           ...             ⋱             ...          \n",
      "  0.0630  0.1060  0.0750  ...   0.2565  0.2461  0.3753\n",
      " -0.0551 -0.1478  0.0675  ...  -0.2060 -0.0533  0.2319\n",
      "  0.2597  0.3514 -0.3566  ...   0.0346 -0.2311 -0.1510\n",
      "[torch.FloatTensor of size 1x10x100]\n",
      " 327.064758301\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "dltrainers/trainers.py:299: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
      "  smbatch = F.softmax(batch)\n"
     ]
    }
   ],
   "source": [
    "with dlm.specops:\n",
    "    model = Cr(7) | Img2Seq() | Cl(10)\n",
    "net = model.create(1, 1, 100, 28)\n",
    "print net\n",
    "tr = dlt.Image2SeqTrainer(net)\n",
    "x = randn(1, 100, 28, 1)\n",
    "y = rand(1, 10, 23)\n",
    "y /= sum(y, 1)[:, newaxis, :]\n",
    "z, l = tr.train_batch(x, y)\n",
    "print z, l"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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