setupmodules

dsgiitr · Oct 18, 2020 · f95cca9 · f95cca9
1 parent 71b72c8
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diff --git a/.gitignore b/.gitignore
@@ -1,3 +1,2 @@
 .ipynb_checkpoints
 data/
-.pth
diff --git a/Capsule Network Train.ipynb b/Capsule Network Train.ipynb
@@ -23,13 +23,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "USE_CUDA = True if torch.cuda.is_available() else False\n",
     "\n",
-    "\n",
     "class ConvLayer(nn.Module):\n",
     "    def __init__(self, in_channels=1, out_channels=256, kernel_size=9):\n",
     "        super(ConvLayer, self).__init__()\n",

diff --git a/L2_recon.ipynb b/L2_recon.ipynb
@@ -0,0 +1,221 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import numpy\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.nn.functional as F\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "from model.net import *\n",
+    "from utils.training import *\n",
+    "from data.data import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Experiment Config"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "args = {\n",
+    "    'USE_CUDA': True if torch.cuda.is_available() else False,\n",
+    "    'BATCH_SIZE': 32,\n",
+    "    'N_EPOCHS': 30,\n",
+    "    'LEARNING_RATE': 0.01,\n",
+    "    'MOMENTUM': 0.9,\n",
+    "    'DATASET_NAME':'mnist',\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model Loading"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Config for 49 16d vectors in the Primary Capsule. Set Softmax dimension to 0 in this case\n",
+    "class Config:\n",
+    "    def __init__(self):\n",
+    "        # CNN (cnn)\n",
+    "        self.cnn_in_channels = 1\n",
+    "        self.cnn_out_channels = 12\n",
+    "        self.cnn_kernel_size = 15\n",
+    "\n",
+    "        # Primary Capsule (pc)\n",
+    "        self.pc_num_capsules = 16\n",
+    "        self.pc_in_channels = 12\n",
+    "        self.pc_out_channels = 1\n",
+    "        self.pc_kernel_size = 8\n",
+    "        self.pc_num_routes = 1 * 7 * 7\n",
+    "\n",
+    "        # Digit Capsule 1 (dc)\n",
+    "        self.dc_num_capsules = 49\n",
+    "        self.dc_num_routes = 1 * 7 * 7\n",
+    "        self.dc_in_channels = 16\n",
+    "        self.dc_out_channels = 16\n",
+    "        \n",
+    "        # Digit Capsule 2 (dc)\n",
+    "        self.dc_2_num_capsules = 10\n",
+    "        self.dc_2_num_routes = 1 * 7 * 7\n",
+    "        self.dc_2_in_channels = 16\n",
+    "        self.dc_2_out_channels = 16\n",
+    "\n",
+    "        # Decoder\n",
+    "        self.input_width = 28\n",
+    "        self.input_height = 28\n",
+    "\n",
+    "torch.manual_seed(1)\n",
+    "config = Config()\n",
+    "\n",
+    "net = CapsNet(config)\n",
+    "# capsule_net = torch.nn.DataParallel(capsule_net)\n",
+    "if args['USE_CUDA']:\n",
+    "    net = net.cuda()\n",
+    "    \n",
+    "net.load_state_dict(torch.load('./CapsNetMNIST.pth'), map_location='cpu')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trainloader, testloader = dataset(args)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training CapsuleNet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# torch.save(capsule_net.state_dict(), \"./CapsNetMNIST.pth \")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Config for 16 1d vectors in Capsule Layer. Set the Softmax Dimension to 1 in this case\n",
+    "# class Config:\n",
+    "#     def __init__(self, dataset='mnist'):\n",
+    "#         # CNN (cnn)\n",
+    "#         self.cnn_in_channels = 1\n",
+    "#         self.cnn_out_channels = 12\n",
+    "#         self.cnn_kernel_size = 15\n",
+    "\n",
+    "#         # Primary Capsule (pc)\n",
+    "#         self.pc_num_capsules = 1\n",
+    "#         self.pc_in_channels = 12\n",
+    "#         self.pc_out_channels = 16\n",
+    "#         self.pc_kernel_size = 8\n",
+    "#         self.pc_num_routes = 16 * 7 * 7\n",
+    "\n",
+    "#         # Digit Capsule 1 (dc)\n",
+    "#         self.dc_num_capsules = 49\n",
+    "#         self.dc_num_routes = 16 * 7 * 7\n",
+    "#         self.dc_in_channels = 1\n",
+    "#         self.dc_out_channels = 1 #16\n",
+    "        \n",
+    "#         # Digit Capsule 2 (dc)\n",
+    "#         self.dc_2_num_capsules = 10\n",
+    "#         self.dc_2_num_routes = 7 * 7\n",
+    "#         self.dc_2_in_channels = 1 #16\n",
+    "#         self.dc_2_out_channels = 16\n",
+    "\n",
+    "#         # Decoder\n",
+    "#         self.input_width = 28\n",
+    "#         self.input_height = 28"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}