{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "In this tutorial, we give an end-to-end demo of compressing [MobileNetV2](https://arxiv.org/abs/1801.04381) for finegrained classification using [NNI Pruners](https://nni.readthedocs.io/en/stable/Compression/pruning.html). Although MobileNetV2 is already a highly optimized architecture, we show that we can further reduce its size by over 50% with minimal performance loss using iterative pruning and knowledge distillation. To similate a real usage scenario, we use the [Stanford Dogs](http://vision.stanford.edu/aditya86/ImageNetDogs/) dataset as the target task, and show how to implement and optimize the following steps:\n",
    "* Model pre-training\n",
    "* Pruning\n",
    "* Model Speedup\n",
    "* Finetuning the pruned model\n",
    "\n",
    "Also, we will compare our approach with some baseline channel compression schemes defined by the authors of the MobileNets, and show that NNI pruners can provide a superior performance while being easy-to-use. We release this notebook along with our code under the folder `examples/model_compress/pruning/mobilenet_end2end/`.\n",
    "<div>\n",
    "<img src=\"final_performance.png\" width=\"750\"/>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import xml\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import numpy as np\n",
    "\n",
    "from nni.compression.pytorch import ModelSpeedup\n",
    "from nni.compression.pytorch.utils.counter import count_flops_params\n",
    "\n",
    "from utils import create_model, get_dataloader\n",
    "\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "num_workers = 16\n",
    "torch.set_num_threads(num_workers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Background\n",
    "### Pruning MobileNetV2\n",
    "The main building block of MobileNetV2 is \"inverted residual blocks\", where a pointwise convolution first projects into a feature map with higher channels,following a depthwise convolution, and a pointwise convolution with linear activation that projects into a features map with less channels (thus called \"inverted residuals and linear bottlenecks\"). With 11 such blocks stacked together, the entire model has 3.4M parameters and takes up about 10M storage space (this number is platform-dependent).\n",
    "\n",
    "<div>\n",
    "<img src=\"mobilenet.png\" width=\"300\"/>\n",
    "</div>\n",
    "\n",
    "Now we consider compressing MobileNetV2 by **filter pruning** (also called channel pruning). Recall that in genernal, a $k\\times k$ convolutional kernel has the weight with shape $(out\\_channel, \\frac{in\\_channel}{groups}, k, k)$. If the input has shape $(B, in\\_channel, H, W)$, the convolutional layer's output (with padding) would have shape $(B, out\\_channel, H, W)$. Suppose we remove $M$ filters from this layer, then weight would have shape $(out\\_channel-M, \\frac{in\\_channel}{groups}, k, k)$, and the output would then have shape $(B, out\\_channel - M, H, W)$. Further, we have the following observations:\n",
    "* The model's number of parameters is directly reduced by $M\\times \\frac{in\\_channel}{groups} \\times k \\times k$.\n",
    "* We are performing structured pruning, as each filter's weight elements are adjacent. Compared to unstructured pruning (or fine-grained pruning), structured pruning generally allows us to directly remove weights and their connections from the network, resulting in greater compression and speed-up. For this reason, in this tutorial we solely focus on filter-level pruning. \n",
    "* Since the output channel is shrinked, we can also remove weights from the next layer corresponding to these channel dimensions. In NNI, the pruner prunes the weights by just setting the weight values to zero, and then the [ModelSpeedup](https://nni.readthedocs.io/en/stable/Compression/ModelSpeedup.html) tool infers the weight relations and removes pruned weights and connections, which we will also demonstrate later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in /home/v-diwu4/.cache/torch/hub/pytorch_vision_v0.8.1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MobileNetV2(\n",
      "  (features): Sequential(\n",
      "    (0): ConvBNActivation(\n",
      "      (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (2): ReLU6(inplace=True)\n",
      "    )\n",
      "    (1): InvertedResidual(\n",
      "      (conv): Sequential(\n",
      "        (0): ConvBNActivation(\n",
      "          (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
      "          (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (1): Conv2d(32, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (2): InvertedResidual(\n",
      "      (conv): Sequential(\n",
      "        (0): ConvBNActivation(\n",
      "          (0): Conv2d(16, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (1): ConvBNActivation(\n",
      "          (0): Conv2d(96, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=96, bias=False)\n",
      "          (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (2): Conv2d(96, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (3): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (3): InvertedResidual(\n",
      "      (conv): Sequential(\n",
      "        (0): ConvBNActivation(\n",
      "          (0): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (1): ConvBNActivation(\n",
      "          (0): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)\n",
      "          (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (2): Conv2d(144, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (3): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (4): InvertedResidual(\n",
      "      (conv): Sequential(\n",
      "        (0): ConvBNActivation(\n",
      "          (0): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (1): ConvBNActivation(\n",
      "          (0): Conv2d(144, 144, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=144, bias=False)\n",
      "          (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (2): Conv2d(144, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (5): InvertedResidual(\n",
      "      (conv): Sequential(\n",
      "        (0): ConvBNActivation(\n",
      "          (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (1): ConvBNActivation(\n",
      "          (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)\n",
      "          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (2): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (6): InvertedResidual(\n",
      "      (conv): Sequential(\n",
      "        (0): ConvBNActivation(\n",
      "          (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (1): ConvBNActivation(\n",
      "          (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)\n",
      "          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (2): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (7): InvertedResidual(\n",
      "      (conv): Sequential(\n",
      "        (0): ConvBNActivation(\n",
      "          (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (1): ConvBNActivation(\n",
      "          (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=192, bias=False)\n",
      "          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (2): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (8): InvertedResidual(\n",
      "      (conv): Sequential(\n",
      "        (0): ConvBNActivation(\n",
      "          (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (1): ConvBNActivation(\n",
      "          (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
      "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (9): InvertedResidual(\n",
      "      (conv): Sequential(\n",
      "        (0): ConvBNActivation(\n",
      "          (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (1): ConvBNActivation(\n",
      "          (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
      "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (10): InvertedResidual(\n",
      "      (conv): Sequential(\n",
      "        (0): ConvBNActivation(\n",
      "          (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (1): ConvBNActivation(\n",
      "          (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
      "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (11): InvertedResidual(\n",
      "      (conv): Sequential(\n",
      "        (0): ConvBNActivation(\n",
      "          (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (1): ConvBNActivation(\n",
      "          (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
      "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (2): Conv2d(384, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (12): InvertedResidual(\n",
      "      (conv): Sequential(\n",
      "        (0): ConvBNActivation(\n",
      "          (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (1): ConvBNActivation(\n",
      "          (0): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)\n",
      "          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (2): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (13): InvertedResidual(\n",
      "      (conv): Sequential(\n",
      "        (0): ConvBNActivation(\n",
      "          (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (1): ConvBNActivation(\n",
      "          (0): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)\n",
      "          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (2): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (14): InvertedResidual(\n",
      "      (conv): Sequential(\n",
      "        (0): ConvBNActivation(\n",
      "          (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (1): ConvBNActivation(\n",
      "          (0): Conv2d(576, 576, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=576, bias=False)\n",
      "          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (2): Conv2d(576, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (15): InvertedResidual(\n",
      "      (conv): Sequential(\n",
      "        (0): ConvBNActivation(\n",
      "          (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (1): ConvBNActivation(\n",
      "          (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n",
      "          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (2): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (16): InvertedResidual(\n",
      "      (conv): Sequential(\n",
      "        (0): ConvBNActivation(\n",
      "          (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (1): ConvBNActivation(\n",
      "          (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n",
      "          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (2): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (17): InvertedResidual(\n",
      "      (conv): Sequential(\n",
      "        (0): ConvBNActivation(\n",
      "          (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (1): ConvBNActivation(\n",
      "          (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n",
      "          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (2): Conv2d(960, 320, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (3): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (18): ConvBNActivation(\n",
      "      (0): Conv2d(320, 1280, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (1): BatchNorm2d(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (2): ReLU6(inplace=True)\n",
      "    )\n",
      "  )\n",
      "  (classifier): Sequential(\n",
      "    (0): Dropout(p=0.2, inplace=False)\n",
      "    (1): Linear(in_features=1280, out_features=1000, bias=True)\n",
      "  )\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ")\n",
      "+-------+----------------------+--------+-------------------+----------+---------+\n",
      "| Index |         Name         |  Type  |    Weight Shape   |  FLOPs   | #Params |\n",
      "+-------+----------------------+--------+-------------------+----------+---------+\n",
      "|   0   |     features.0.0     | Conv2d |   (32, 3, 3, 3)   | 10838016 |   864   |\n",
      "|   1   | features.1.conv.0.0  | Conv2d |   (32, 1, 3, 3)   | 3612672  |   288   |\n",
      "|   2   |  features.1.conv.1   | Conv2d |   (16, 32, 1, 1)  | 6422528  |   512   |\n",
      "|   3   | features.2.conv.0.0  | Conv2d |   (96, 16, 1, 1)  | 19267584 |   1536  |\n",
      "|   4   | features.2.conv.1.0  | Conv2d |   (96, 1, 3, 3)   | 2709504  |   864   |\n",
      "|   5   |  features.2.conv.2   | Conv2d |   (24, 96, 1, 1)  | 7225344  |   2304  |\n",
      "|   6   | features.3.conv.0.0  | Conv2d |  (144, 24, 1, 1)  | 10838016 |   3456  |\n",
      "|   7   | features.3.conv.1.0  | Conv2d |   (144, 1, 3, 3)  | 4064256  |   1296  |\n",
      "|   8   |  features.3.conv.2   | Conv2d |  (24, 144, 1, 1)  | 10838016 |   3456  |\n",
      "|   9   | features.4.conv.0.0  | Conv2d |  (144, 24, 1, 1)  | 10838016 |   3456  |\n",
      "|   10  | features.4.conv.1.0  | Conv2d |   (144, 1, 3, 3)  | 1016064  |   1296  |\n",
      "|   11  |  features.4.conv.2   | Conv2d |  (32, 144, 1, 1)  | 3612672  |   4608  |\n",
      "|   12  | features.5.conv.0.0  | Conv2d |  (192, 32, 1, 1)  | 4816896  |   6144  |\n",
      "|   13  | features.5.conv.1.0  | Conv2d |   (192, 1, 3, 3)  | 1354752  |   1728  |\n",
      "|   14  |  features.5.conv.2   | Conv2d |  (32, 192, 1, 1)  | 4816896  |   6144  |\n",
      "|   15  | features.6.conv.0.0  | Conv2d |  (192, 32, 1, 1)  | 4816896  |   6144  |\n",
      "|   16  | features.6.conv.1.0  | Conv2d |   (192, 1, 3, 3)  | 1354752  |   1728  |\n",
      "|   17  |  features.6.conv.2   | Conv2d |  (32, 192, 1, 1)  | 4816896  |   6144  |\n",
      "|   18  | features.7.conv.0.0  | Conv2d |  (192, 32, 1, 1)  | 4816896  |   6144  |\n",
      "|   19  | features.7.conv.1.0  | Conv2d |   (192, 1, 3, 3)  |  338688  |   1728  |\n",
      "|   20  |  features.7.conv.2   | Conv2d |  (64, 192, 1, 1)  | 2408448  |  12288  |\n",
      "|   21  | features.8.conv.0.0  | Conv2d |  (384, 64, 1, 1)  | 4816896  |  24576  |\n",
      "|   22  | features.8.conv.1.0  | Conv2d |   (384, 1, 3, 3)  |  677376  |   3456  |\n",
      "|   23  |  features.8.conv.2   | Conv2d |  (64, 384, 1, 1)  | 4816896  |  24576  |\n",
      "|   24  | features.9.conv.0.0  | Conv2d |  (384, 64, 1, 1)  | 4816896  |  24576  |\n",
      "|   25  | features.9.conv.1.0  | Conv2d |   (384, 1, 3, 3)  |  677376  |   3456  |\n",
      "|   26  |  features.9.conv.2   | Conv2d |  (64, 384, 1, 1)  | 4816896  |  24576  |\n",
      "|   27  | features.10.conv.0.0 | Conv2d |  (384, 64, 1, 1)  | 4816896  |  24576  |\n",
      "|   28  | features.10.conv.1.0 | Conv2d |   (384, 1, 3, 3)  |  677376  |   3456  |\n",
      "|   29  |  features.10.conv.2  | Conv2d |  (64, 384, 1, 1)  | 4816896  |  24576  |\n",
      "|   30  | features.11.conv.0.0 | Conv2d |  (384, 64, 1, 1)  | 4816896  |  24576  |\n",
      "|   31  | features.11.conv.1.0 | Conv2d |   (384, 1, 3, 3)  |  677376  |   3456  |\n",
      "|   32  |  features.11.conv.2  | Conv2d |  (96, 384, 1, 1)  | 7225344  |  36864  |\n",
      "|   33  | features.12.conv.0.0 | Conv2d |  (576, 96, 1, 1)  | 10838016 |  55296  |\n",
      "|   34  | features.12.conv.1.0 | Conv2d |   (576, 1, 3, 3)  | 1016064  |   5184  |\n",
      "|   35  |  features.12.conv.2  | Conv2d |  (96, 576, 1, 1)  | 10838016 |  55296  |\n",
      "|   36  | features.13.conv.0.0 | Conv2d |  (576, 96, 1, 1)  | 10838016 |  55296  |\n",
      "|   37  | features.13.conv.1.0 | Conv2d |   (576, 1, 3, 3)  | 1016064  |   5184  |\n",
      "|   38  |  features.13.conv.2  | Conv2d |  (96, 576, 1, 1)  | 10838016 |  55296  |\n",
      "|   39  | features.14.conv.0.0 | Conv2d |  (576, 96, 1, 1)  | 10838016 |  55296  |\n",
      "|   40  | features.14.conv.1.0 | Conv2d |   (576, 1, 3, 3)  |  254016  |   5184  |\n",
      "|   41  |  features.14.conv.2  | Conv2d |  (160, 576, 1, 1) | 4515840  |  92160  |\n",
      "|   42  | features.15.conv.0.0 | Conv2d |  (960, 160, 1, 1) | 7526400  |  153600 |\n",
      "|   43  | features.15.conv.1.0 | Conv2d |   (960, 1, 3, 3)  |  423360  |   8640  |\n",
      "|   44  |  features.15.conv.2  | Conv2d |  (160, 960, 1, 1) | 7526400  |  153600 |\n",
      "|   45  | features.16.conv.0.0 | Conv2d |  (960, 160, 1, 1) | 7526400  |  153600 |\n",
      "|   46  | features.16.conv.1.0 | Conv2d |   (960, 1, 3, 3)  |  423360  |   8640  |\n",
      "|   47  |  features.16.conv.2  | Conv2d |  (160, 960, 1, 1) | 7526400  |  153600 |\n",
      "|   48  | features.17.conv.0.0 | Conv2d |  (960, 160, 1, 1) | 7526400  |  153600 |\n",
      "|   49  | features.17.conv.1.0 | Conv2d |   (960, 1, 3, 3)  |  423360  |   8640  |\n",
      "|   50  |  features.17.conv.2  | Conv2d |  (320, 960, 1, 1) | 15052800 |  307200 |\n",
      "|   51  |    features.18.0     | Conv2d | (1280, 320, 1, 1) | 20070400 |  409600 |\n",
      "|   52  |     classifier.1     | Linear |    (1000, 1280)   | 1280000  | 1281000 |\n",
      "+-------+----------------------+--------+-------------------+----------+---------+\n",
      "FLOPs total: 300774272\n",
      "#Params total: 3470760\n",
      "FLOPs: 300774272, params: 3470760\n"
     ]
    }
   ],
   "source": [
    "# check model architecture\n",
    "model = torch.hub.load('pytorch/vision:v0.8.1', 'mobilenet_v2', pretrained=True).to(device)\n",
    "print(model)\n",
    "\n",
    "# check model FLOPs and parameter counts with NNI utils\n",
    "dummy_input = torch.rand([1, 3, 224, 224]).to(device)\n",
    "flops, params, results = count_flops_params(model, dummy_input)\n",
    "print(f\"FLOPs: {flops}, params: {params}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stanford Dogs\n",
    "\n",
    "The [Stanford Dogs](http://vision.stanford.edu/aditya86/ImageNetDogs/) dataset contains images of 120 breeds of dogs from around the world. It is built using images and annotation from ImageNet for the task of fine-grained image classification. We choose this task to simulate a transfer learning scenario, where a model pre-trained on the ImageNet is further transferred to an often simpler downstream task.\n",
    "\n",
    "To download and prepare the data, please run `prepare_data.sh`, which downloads the images and annotations, and preprocesses the images for training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "file_list.mat\n",
      "train_list.mat\n",
      "test_list.mat\n",
      "Directory already exists. Nothing done.\n"
     ]
    }
   ],
   "source": [
    "# Run prepare_data.sh\n",
    "!chmod u+x prepare_data.sh\n",
    "!./prepare_data.sh"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Then, you may run following code block, which shows several instances:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show several examples\n",
    "# Code adapted from https://www.kaggle.com/mclikmb4/xception-transfer-learning-120-breeds-83-acc\n",
    "image_path = './data/stanford-dogs/Images/'\n",
    "breed_list = sorted(os.listdir(image_path))\n",
    "\n",
    "plt.figure(figsize=(10, 10))\n",
    "for i in range(9):\n",
    "    plt.subplot(331 + i)\n",
    "    breed = np.random.choice(breed_list)\n",
    "    dog = np.random.choice(os.listdir('./data/stanford-dogs/Annotation/' + breed))\n",
    "    img = Image.open(image_path + breed + '/' + dog + '.jpg') \n",
    "    tree = xml.etree.ElementTree.parse('./data/stanford-dogs/Annotation/' + breed + '/' + dog)\n",
    "    root = tree.getroot()\n",
    "    objects = root.findall('object')\n",
    "    plt.imshow(img)\n",
    "    for o in objects:\n",
    "        bndbox = o.find('bndbox')\n",
    "        xmin = int(bndbox.find('xmin').text)\n",
    "        ymin = int(bndbox.find('ymin').text)\n",
    "        xmax = int(bndbox.find('xmax').text)\n",
    "        ymax = int(bndbox.find('ymax').text)\n",
    "        plt.plot([xmin, xmax, xmax, xmin, xmin], [ymin, ymin, ymax, ymax, ymin])\n",
    "        plt.text(xmin, ymin, o.find('name').text, bbox={'ec': None})\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Pre-training\n",
    "First, we obtain a MobileNetV2 model on this task, which will serve as the base model for compression. Unfortunately, although this step is often called model \"pre-training\" in the model compression teminologies, we are actually finetuning a model pre-trained on ImageNet. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This script will save the state dict of the pretrained model to \"./pretrained_mobilenet_v2_torchhub/checkpoint_best.pt\"\n",
    "\n",
    "# %run pretrain.py\n",
    "# %run test.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compression via Pruning\n",
    "In this section, we first demonstrate how to perform channel pruning with NNI pruners in three steps: \n",
    "* defining a config list\n",
    "* creating a Pruner instance\n",
    "* calling `pruner.compress` and `pruner.export_model` to calculate and export masks\n",
    "\n",
    "Then, we demonstrate the common practices after pruning:\n",
    "* model speedup\n",
    "* further finetuning (with or without knowledge distillation)\n",
    "* evaluation\n",
    "\n",
    "Finally, we present a grid search example to find the balance between model performance and the final model size. We include some of our results and discuss our observations. \n",
    "\n",
    "Note that the code blocks in this section are taken from the file `pruning_experiments.py`. You can directly run the file by specifying several command line arguments and see the end-to-end process. You can also run the file to reproduce our experiments. We will discuss that in the last section. \n",
    "\n",
    "### Using NNI Pruners"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nni.algorithms.compression.pytorch.pruning import (\n",
    "    LevelPruner,\n",
    "    SlimPruner,\n",
    "    FPGMPruner,\n",
    "    TaylorFOWeightFilterPruner,\n",
    "    L1FilterPruner,\n",
    "    L2FilterPruner,\n",
    "    AGPPruner,\n",
    "    ActivationMeanRankFilterPruner,\n",
    "    ActivationAPoZRankFilterPruner\n",
    ")\n",
    "\n",
    "pruner_name_to_class = {\n",
    "    'level': LevelPruner,\n",
    "    'l1': L1FilterPruner,\n",
    "    'l2': L2FilterPruner,\n",
    "    'slim': SlimPruner,\n",
    "    'fpgm': FPGMPruner,\n",
    "    'taylor': TaylorFOWeightFilterPruner,\n",
    "    'agp': AGPPruner,\n",
    "    'activationmeanrank': ActivationMeanRankFilterPruner,\n",
    "    'apoz': ActivationAPoZRankFilterPruner\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in /home/v-diwu4/.cache/torch/hub/pytorch_vision_v0.8.1\n"
     ]
    }
   ],
   "source": [
    "# load model from the pretrained checkpoint\n",
    "model_type = 'mobilenet_v2_torchhub'\n",
    "checkpoint = \"./pretrained_mobilenet_v2_torchhub/checkpoint_best.pt\"\n",
    "pretrained = True \n",
    "input_size = 224\n",
    "n_classes = 120\n",
    "\n",
    "model = create_model(model_type=model_type, pretrained=pretrained, n_classes=n_classes,\n",
    "                     input_size=input_size, checkpoint=checkpoint).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2021-08-31 07:17:21] INFO (nni.compression.pytorch.compressor/MainThread) Model state_dict saved to ./pruned_model.pth\n",
      "[2021-08-31 07:17:21] INFO (nni.compression.pytorch.compressor/MainThread) Mask dict saved to ./mask.pth\n"
     ]
    }
   ],
   "source": [
    "# Defining the config list.\n",
    "# Note that here we only prune the depthwise convolution and the last pointwise convolution. \n",
    "# We will let the model speedup tool propagate the sparsity to the first pointwise convolution layer. \n",
    "\n",
    "pruner_name = 'l1'\n",
    "sparsity = 0.5\n",
    "\n",
    "if pruner_name != 'slim':\n",
    "    config_list = [{\n",
    "        'op_names': ['features.{}.conv.1.0'.format(x) for x in range(2, 18)],\n",
    "        'sparsity': sparsity\n",
    "    },{\n",
    "        'op_names': ['features.{}.conv.2'.format(x) for x in range(2, 18)],\n",
    "        'sparsity': sparsity\n",
    "    }]\n",
    "else:\n",
    "    # For slim pruner, we should specify BatchNorm layers instead of the corresponding Conv2d layers\n",
    "    config_list = [{\n",
    "        'op_names': ['features.{}.conv.1.1'.format(x) for x in range(2, 18)],\n",
    "        'sparsity': sparsity\n",
    "    },{\n",
    "        'op_names': ['features.{}.conv.3'.format(x) for x in range(2, 18)],\n",
    "        'sparsity': sparsity\n",
    "    }]\n",
    "\n",
    "# Different pruners require different additional parameters, so we put them together in the kwargs dict. \n",
    "# Please check the docs for detailed information.\n",
    "kwargs = {}                                                                                                                                \n",
    "if pruner_name in ['slim', 'taylor', 'activationmeanrank', 'apoz', 'agp']:\n",
    "    from pruning_experiments import trainer_helper\n",
    "    train_dataloader = get_dataloader('train', './data/stanford-dogs/Processed/train', batch_size=32)\n",
    "    def trainer(model, optimizer, criterion, epoch):\n",
    "        return trainer_helper(model, criterion, optimizer, train_dataloader, device)\n",
    "    kwargs = {\n",
    "        'trainer': trainer,\n",
    "        'optimizer': torch.optim.Adam(model.parameters()),\n",
    "        'criterion': nn.CrossEntropyLoss()\n",
    "    }\n",
    "    if pruner_name == 'agp':\n",
    "        kwargs['pruning_algorithm'] = 'l1'\n",
    "        kwargs['num_iterations'] = 10\n",
    "        kwargs['epochs_per_iteration'] = 1\n",
    "    if pruner_name == 'slim':\n",
    "        kwargs['sparsifying_training_epochs'] = 10\n",
    "\n",
    "# Create pruner, call pruner.compress(), and export the pruned model\n",
    "pruner = pruner_name_to_class[pruner_name](model, config_list, **kwargs)\n",
    "pruner.compress()\n",
    "pruner.export_model('./pruned_model.pth', './mask.pth')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Speedup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note: must unwrap the model before speed up\n",
    "pruner._unwrap_model()\n",
    "\n",
    "dummy_input = torch.rand(1,3,224,224).to(device)\n",
    "ms = ModelSpeedup(model, dummy_input, './mask.pth')\n",
    "ms.speedup_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+----------------------+--------+-------------------+----------+---------+\n",
      "| Index |         Name         |  Type  |    Weight Shape   |  FLOPs   | #Params |\n",
      "+-------+----------------------+--------+-------------------+----------+---------+\n",
      "|   0   |     features.0.0     | Conv2d |   (32, 3, 3, 3)   | 10838016 |   864   |\n",
      "|   1   | features.1.conv.0.0  | Conv2d |   (32, 1, 3, 3)   | 3612672  |   288   |\n",
      "|   2   |  features.1.conv.1   | Conv2d |   (16, 32, 1, 1)  | 6422528  |   512   |\n",
      "|   3   | features.2.conv.0.0  | Conv2d |   (48, 16, 1, 1)  | 9633792  |   768   |\n",
      "|   4   | features.2.conv.1.0  | Conv2d |   (48, 1, 3, 3)   | 1354752  |   432   |\n",
      "|   5   |  features.2.conv.2   | Conv2d |   (16, 48, 1, 1)  | 2408448  |   768   |\n",
      "|   6   | features.3.conv.0.0  | Conv2d |   (72, 16, 1, 1)  | 3612672  |   1152  |\n",
      "|   7   | features.3.conv.1.0  | Conv2d |   (72, 1, 3, 3)   | 2032128  |   648   |\n",
      "|   8   |  features.3.conv.2   | Conv2d |   (16, 72, 1, 1)  | 3612672  |   1152  |\n",
      "|   9   | features.4.conv.0.0  | Conv2d |   (72, 16, 1, 1)  | 3612672  |   1152  |\n",
      "|   10  | features.4.conv.1.0  | Conv2d |   (72, 1, 3, 3)   |  508032  |   648   |\n",
      "|   11  |  features.4.conv.2   | Conv2d |   (25, 72, 1, 1)  | 1411200  |   1800  |\n",
      "|   12  | features.5.conv.0.0  | Conv2d |   (96, 25, 1, 1)  | 1881600  |   2400  |\n",
      "|   13  | features.5.conv.1.0  | Conv2d |   (96, 1, 3, 3)   |  677376  |   864   |\n",
      "|   14  |  features.5.conv.2   | Conv2d |   (25, 96, 1, 1)  | 1881600  |   2400  |\n",
      "|   15  | features.6.conv.0.0  | Conv2d |   (96, 25, 1, 1)  | 1881600  |   2400  |\n",
      "|   16  | features.6.conv.1.0  | Conv2d |   (96, 1, 3, 3)   |  677376  |   864   |\n",
      "|   17  |  features.6.conv.2   | Conv2d |   (25, 96, 1, 1)  | 1881600  |   2400  |\n",
      "|   18  | features.7.conv.0.0  | Conv2d |   (96, 25, 1, 1)  | 1881600  |   2400  |\n",
      "|   19  | features.7.conv.1.0  | Conv2d |   (96, 1, 3, 3)   |  169344  |   864   |\n",
      "|   20  |  features.7.conv.2   | Conv2d |   (59, 96, 1, 1)  | 1110144  |   5664  |\n",
      "|   21  | features.8.conv.0.0  | Conv2d |  (192, 59, 1, 1)  | 2220288  |  11328  |\n",
      "|   22  | features.8.conv.1.0  | Conv2d |   (192, 1, 3, 3)  |  338688  |   1728  |\n",
      "|   23  |  features.8.conv.2   | Conv2d |  (59, 192, 1, 1)  | 2220288  |  11328  |\n",
      "|   24  | features.9.conv.0.0  | Conv2d |  (192, 59, 1, 1)  | 2220288  |  11328  |\n",
      "|   25  | features.9.conv.1.0  | Conv2d |   (192, 1, 3, 3)  |  338688  |   1728  |\n",
      "|   26  |  features.9.conv.2   | Conv2d |  (59, 192, 1, 1)  | 2220288  |  11328  |\n",
      "|   27  | features.10.conv.0.0 | Conv2d |  (192, 59, 1, 1)  | 2220288  |  11328  |\n",
      "|   28  | features.10.conv.1.0 | Conv2d |   (192, 1, 3, 3)  |  338688  |   1728  |\n",
      "|   29  |  features.10.conv.2  | Conv2d |  (59, 192, 1, 1)  | 2220288  |  11328  |\n",
      "|   30  | features.11.conv.0.0 | Conv2d |  (192, 59, 1, 1)  | 2220288  |  11328  |\n",
      "|   31  | features.11.conv.1.0 | Conv2d |   (192, 1, 3, 3)  |  338688  |   1728  |\n",
      "|   32  |  features.11.conv.2  | Conv2d |  (87, 192, 1, 1)  | 3273984  |  16704  |\n",
      "|   33  | features.12.conv.0.0 | Conv2d |  (288, 87, 1, 1)  | 4910976  |  25056  |\n",
      "|   34  | features.12.conv.1.0 | Conv2d |   (288, 1, 3, 3)  |  508032  |   2592  |\n",
      "|   35  |  features.12.conv.2  | Conv2d |  (87, 288, 1, 1)  | 4910976  |  25056  |\n",
      "|   36  | features.13.conv.0.0 | Conv2d |  (288, 87, 1, 1)  | 4910976  |  25056  |\n",
      "|   37  | features.13.conv.1.0 | Conv2d |   (288, 1, 3, 3)  |  508032  |   2592  |\n",
      "|   38  |  features.13.conv.2  | Conv2d |  (87, 288, 1, 1)  | 4910976  |  25056  |\n",
      "|   39  | features.14.conv.0.0 | Conv2d |  (288, 87, 1, 1)  | 4910976  |  25056  |\n",
      "|   40  | features.14.conv.1.0 | Conv2d |   (288, 1, 3, 3)  |  127008  |   2592  |\n",
      "|   41  |  features.14.conv.2  | Conv2d |  (134, 288, 1, 1) | 1891008  |  38592  |\n",
      "|   42  | features.15.conv.0.0 | Conv2d |  (480, 134, 1, 1) | 3151680  |  64320  |\n",
      "|   43  | features.15.conv.1.0 | Conv2d |   (480, 1, 3, 3)  |  211680  |   4320  |\n",
      "|   44  |  features.15.conv.2  | Conv2d |  (134, 480, 1, 1) | 3151680  |  64320  |\n",
      "|   45  | features.16.conv.0.0 | Conv2d |  (480, 134, 1, 1) | 3151680  |  64320  |\n",
      "|   46  | features.16.conv.1.0 | Conv2d |   (480, 1, 3, 3)  |  211680  |   4320  |\n",
      "|   47  |  features.16.conv.2  | Conv2d |  (134, 480, 1, 1) | 3151680  |  64320  |\n",
      "|   48  | features.17.conv.0.0 | Conv2d |  (480, 134, 1, 1) | 3151680  |  64320  |\n",
      "|   49  | features.17.conv.1.0 | Conv2d |   (480, 1, 3, 3)  |  211680  |   4320  |\n",
      "|   50  |  features.17.conv.2  | Conv2d |  (160, 480, 1, 1) | 3763200  |  76800  |\n",
      "|   51  |    features.18.0     | Conv2d | (1280, 160, 1, 1) | 10035200 |  204800 |\n",
      "|   52  |     classifier.1     | Linear |    (120, 1280)    |  153600  |  153720 |\n",
      "+-------+----------------------+--------+-------------------+----------+---------+\n",
      "FLOPs total: 139206976\n",
      "#Params total: 1074880\n",
      "MobileNetV2(\n",
      "  (features): Sequential(\n",
      "    (0): ConvBNActivation(\n",
      "      (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (2): ReLU6(inplace=True)\n",
      "    )\n",
      "    (1): InvertedResidual(\n",
      "      (conv): Sequential(\n",
      "        (0): ConvBNActivation(\n",
      "          (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
      "          (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (1): Conv2d(32, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (2): InvertedResidual(\n",
      "      (conv): Sequential(\n",
      "        (0): ConvBNActivation(\n",
      "          (0): Conv2d(16, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (1): ConvBNActivation(\n",
      "          (0): Conv2d(48, 48, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=48, bias=False)\n",
      "          (1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (2): Conv2d(48, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (3): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (3): InvertedResidual(\n",
      "      (conv): Sequential(\n",
      "        (0): ConvBNActivation(\n",
      "          (0): Conv2d(16, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (1): ConvBNActivation(\n",
      "          (0): Conv2d(72, 72, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=72, bias=False)\n",
      "          (1): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (2): Conv2d(72, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (3): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (4): InvertedResidual(\n",
      "      (conv): Sequential(\n",
      "        (0): ConvBNActivation(\n",
      "          (0): Conv2d(16, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (1): ConvBNActivation(\n",
      "          (0): Conv2d(72, 72, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=72, bias=False)\n",
      "          (1): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (2): Conv2d(72, 25, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (3): BatchNorm2d(25, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (5): InvertedResidual(\n",
      "      (conv): Sequential(\n",
      "        (0): ConvBNActivation(\n",
      "          (0): Conv2d(25, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (1): ConvBNActivation(\n",
      "          (0): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=96, bias=False)\n",
      "          (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (2): Conv2d(96, 25, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (3): BatchNorm2d(25, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (6): InvertedResidual(\n",
      "      (conv): Sequential(\n",
      "        (0): ConvBNActivation(\n",
      "          (0): Conv2d(25, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (1): ConvBNActivation(\n",
      "          (0): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=96, bias=False)\n",
      "          (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (2): Conv2d(96, 25, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (3): BatchNorm2d(25, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (7): InvertedResidual(\n",
      "      (conv): Sequential(\n",
      "        (0): ConvBNActivation(\n",
      "          (0): Conv2d(25, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (1): ConvBNActivation(\n",
      "          (0): Conv2d(96, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=96, bias=False)\n",
      "          (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (2): Conv2d(96, 59, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (3): BatchNorm2d(59, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (8): InvertedResidual(\n",
      "      (conv): Sequential(\n",
      "        (0): ConvBNActivation(\n",
      "          (0): Conv2d(59, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (1): ConvBNActivation(\n",
      "          (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)\n",
      "          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (2): Conv2d(192, 59, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (3): BatchNorm2d(59, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (9): InvertedResidual(\n",
      "      (conv): Sequential(\n",
      "        (0): ConvBNActivation(\n",
      "          (0): Conv2d(59, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (1): ConvBNActivation(\n",
      "          (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)\n",
      "          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (2): Conv2d(192, 59, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (3): BatchNorm2d(59, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (10): InvertedResidual(\n",
      "      (conv): Sequential(\n",
      "        (0): ConvBNActivation(\n",
      "          (0): Conv2d(59, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (1): ConvBNActivation(\n",
      "          (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)\n",
      "          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (2): Conv2d(192, 59, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (3): BatchNorm2d(59, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (11): InvertedResidual(\n",
      "      (conv): Sequential(\n",
      "        (0): ConvBNActivation(\n",
      "          (0): Conv2d(59, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (1): ConvBNActivation(\n",
      "          (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)\n",
      "          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (2): Conv2d(192, 87, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (3): BatchNorm2d(87, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (12): InvertedResidual(\n",
      "      (conv): Sequential(\n",
      "        (0): ConvBNActivation(\n",
      "          (0): Conv2d(87, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (1): ConvBNActivation(\n",
      "          (0): Conv2d(288, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=288, bias=False)\n",
      "          (1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (2): Conv2d(288, 87, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (3): BatchNorm2d(87, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (13): InvertedResidual(\n",
      "      (conv): Sequential(\n",
      "        (0): ConvBNActivation(\n",
      "          (0): Conv2d(87, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (1): ConvBNActivation(\n",
      "          (0): Conv2d(288, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=288, bias=False)\n",
      "          (1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (2): Conv2d(288, 87, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (3): BatchNorm2d(87, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (14): InvertedResidual(\n",
      "      (conv): Sequential(\n",
      "        (0): ConvBNActivation(\n",
      "          (0): Conv2d(87, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (1): ConvBNActivation(\n",
      "          (0): Conv2d(288, 288, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=288, bias=False)\n",
      "          (1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (2): Conv2d(288, 134, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (3): BatchNorm2d(134, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (15): InvertedResidual(\n",
      "      (conv): Sequential(\n",
      "        (0): ConvBNActivation(\n",
      "          (0): Conv2d(134, 480, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (1): ConvBNActivation(\n",
      "          (0): Conv2d(480, 480, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=480, bias=False)\n",
      "          (1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (2): Conv2d(480, 134, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (3): BatchNorm2d(134, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (16): InvertedResidual(\n",
      "      (conv): Sequential(\n",
      "        (0): ConvBNActivation(\n",
      "          (0): Conv2d(134, 480, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (1): ConvBNActivation(\n",
      "          (0): Conv2d(480, 480, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=480, bias=False)\n",
      "          (1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (2): Conv2d(480, 134, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (3): BatchNorm2d(134, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (17): InvertedResidual(\n",
      "      (conv): Sequential(\n",
      "        (0): ConvBNActivation(\n",
      "          (0): Conv2d(134, 480, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (1): ConvBNActivation(\n",
      "          (0): Conv2d(480, 480, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=480, bias=False)\n",
      "          (1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (2): ReLU6(inplace=True)\n",
      "        )\n",
      "        (2): Conv2d(480, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (18): ConvBNActivation(\n",
      "      (0): Conv2d(160, 1280, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (1): BatchNorm2d(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (2): ReLU6(inplace=True)\n",
      "    )\n",
      "  )\n",
      "  (classifier): Sequential(\n",
      "    (0): Dropout(p=0.2, inplace=False)\n",
      "    (1): Linear(in_features=1280, out_features=120, bias=True)\n",
      "  )\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ")\n",
      "FLOPs: 139206976, params: 1074880\n"
     ]
    }
   ],
   "source": [
    "flops, params, results = count_flops_params(model, dummy_input)\n",
    "print(model)\n",
    "print(f\"FLOPs: {flops}, params: {params}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fine-tuning after Pruning\n",
    "\n",
    "Usually, after pruning out some weights from the model, we need further fine-tuning to let the model recover its performance as much as possible. For finetuning, we can either use the same setting during pretraining, or use an additional technique called [**Knowledge Distillation**](https://arxiv.org/pdf/1503.02531.pdf). The key idea is that the model learns on both the original hard labels and the soft labels produced by a teacher model running on the same input. In our setting, **the model before pruning can conveniently serve as the teacher model**. Empirically, we found that using distillation during fine-tuning consistently improves the performance of the pruned model. We will further discuss related experiments in the following section.\n",
    "\n",
    "Note that knowledge distillation can easily be done with the following lines of code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sample code: training with knowledge distillation\n",
    "\"\"\"\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "def train_with_distillation(student_model, teacher_model, optimizer, train_dataloader, device, alpha=0.99, temperature=8):\n",
    "    student_model.train()\n",
    "    for i, (inputs, labels) in enumerate(tqdm(train_dataloader)):\n",
    "        optimizer.zero_grad()\n",
    "        inputs, labels = inputs.float().to(device), labels.to(device)\n",
    "        with torch.no_grad():\n",
    "            teacher_preds = teacher_model(inputs)\n",
    "\n",
    "        student_preds = student_model(inputs)\n",
    "        soft_loss = nn.KLDivLoss()(F.log_softmax(student_preds/temperature, dim=1),\n",
    "                                   F.softmax(teacher_preds/temperature, dim=1))\n",
    "        hard_loss = F.cross_entropy(student_preds, labels)\n",
    "        loss = soft_loss * (alpha * temperature * temperature) + hard_loss * (1. - alpha)\n",
    "\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finetuning after pruning:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in /home/v-diwu4/.cache/torch/hub/pytorch_vision_v0.8.1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start finetuning with distillation epoch 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/338 [00:00<?, ?it/s]/home/v-diwu4/miniconda3/envs/seg/lib/python3.7/site-packages/torch/nn/functional.py:2611: UserWarning: reduction: 'mean' divides the total loss by both the batch size and the support size.'batchmean' divides only by the batch size, and aligns with the KL div math definition.'mean' will be changed to behave the same as 'batchmean' in the next major release.\n",
      "  \"reduction: 'mean' divides the total loss by both the batch size and the support size.\"\n",
      "100%|██████████| 338/338 [00:42<00:00,  7.92it/s]\n",
      "100%|██████████| 38/38 [00:01<00:00, 22.23it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0: train loss 0.0597, valid loss 2.6602, valid acc 0.2673\n",
      "Start finetuning with distillation epoch 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 338/338 [00:41<00:00,  8.20it/s]\n",
      "100%|██████████| 38/38 [00:01<00:00, 22.26it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1: train loss 0.0394, valid loss 2.0302, valid acc 0.4400\n",
      "Start finetuning with distillation epoch 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 338/338 [00:43<00:00,  7.85it/s]\n",
      "100%|██████████| 38/38 [00:01<00:00, 23.37it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2: train loss 0.0309, valid loss 1.6968, valid acc 0.5238\n",
      "Start finetuning with distillation epoch 3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 338/338 [00:43<00:00,  7.85it/s]\n",
      "100%|██████████| 38/38 [00:01<00:00, 25.58it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3: train loss 0.0260, valid loss 1.5152, valid acc 0.5609\n",
      "Start finetuning with distillation epoch 4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 338/338 [00:42<00:00,  8.00it/s]\n",
      "100%|██████████| 38/38 [00:01<00:00, 25.56it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4: train loss 0.0231, valid loss 1.3936, valid acc 0.6053\n",
      "Start finetuning with distillation epoch 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 338/338 [00:41<00:00,  8.06it/s]\n",
      "100%|██████████| 38/38 [00:01<00:00, 25.32it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5: train loss 0.0208, valid loss 1.3012, valid acc 0.6168\n",
      "Start finetuning with distillation epoch 6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 338/338 [00:43<00:00,  7.77it/s]\n",
      "100%|██████████| 38/38 [00:01<00:00, 22.12it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 6: train loss 0.0191, valid loss 1.2575, valid acc 0.6242\n",
      "Start finetuning with distillation epoch 7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 338/338 [00:44<00:00,  7.62it/s]\n",
      "100%|██████████| 38/38 [00:01<00:00, 22.82it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7: train loss 0.0178, valid loss 1.1819, valid acc 0.6456\n",
      "Start finetuning with distillation epoch 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 338/338 [00:43<00:00,  7.76it/s]\n",
      "100%|██████████| 38/38 [00:01<00:00, 25.44it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 8: train loss 0.0167, valid loss 1.1424, valid acc 0.6620\n",
      "Start finetuning with distillation epoch 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 338/338 [00:42<00:00,  7.93it/s]\n",
      "100%|██████████| 38/38 [00:01<00:00, 24.92it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 9: train loss 0.0158, valid loss 1.1762, valid acc 0.6464\n",
      "Best validation accuracy: 0.6620065789473685\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 269/269 [00:12<00:00, 21.63it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 1.2283264702137517\n",
      "Test accuracy: 0.6349907063197026\n"
     ]
    }
   ],
   "source": [
    "from pruning_experiments import run_finetune, run_finetune_distillation, run_eval\n",
    "\n",
    "use_distillation = True\n",
    "n_epochs = 10                  # set for demo purposes; increase this number for your experiments\n",
    "learning_rate = 1e-4\n",
    "weight_decay = 0.0\n",
    "\n",
    "train_dataloader = get_dataloader('train', './data/stanford-dogs/Processed/train', batch_size=32)\n",
    "valid_dataloader = get_dataloader('eval', './data/stanford-dogs/Processed/valid', batch_size=32)\n",
    "test_dataloader = get_dataloader('eval', './data/stanford-dogs/Processed/test', batch_size=32)\n",
    "\n",
    "if not use_distillation:\n",
    "    run_finetune(model, train_dataloader, valid_dataloader, device, \n",
    "                 n_epochs=n_epochs, learning_rate=learning_rate, weight_decay=weight_decay)\n",
    "else:\n",
    "    alpha = 0.99\n",
    "    temperature = 8\n",
    "    # use model with the original checkpoint as the teacher\n",
    "    teacher_model = create_model(model_type=model_type, pretrained=pretrained, n_classes=n_classes,\n",
    "                                 input_size=input_size, checkpoint=checkpoint).to(device)\n",
    "    run_finetune_distillation(model, teacher_model, train_dataloader, valid_dataloader, device, \n",
    "                              alpha, temperature,\n",
    "                              n_epochs=n_epochs, learning_rate=learning_rate, weight_decay=weight_decay)\n",
    "\n",
    "test_loss, test_acc = run_eval(model, test_dataloader, device)\n",
    "print('Test loss: {}\\nTest accuracy: {}'.format(test_loss, test_acc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Steps of Optimizing Pruning Parameters\n",
    "So far, we have shown the end-to-end process of compressing a MobileNetV2 model on Stanford Dogs dataset using NNI pruners. It is crucial to mention that to make sure that the final model has a satisfactory performance, several trials on different sparsity values and pruner settings are necessary. To simplify this process for you, in this section we discuss how we approach the problem and mention some empirical observations. We hope that this section can serve as a good reference of the general process of optimizing the pruning with NNI Pruners.\n",
    "\n",
    "To help you reproduce some of the experiments, we implement `pruning_experiments.py`. Please find examples in the following code blocks for how to run experiments with this script."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1: selecting the layer to prune\n",
    "Just as the first step of using the pruning is writing a `config_list`, the first thing you should consider when pruning a model is **which layer to prune**. This is crucial because some layers are not as sensitive to pruning as the others. In our example, we have several candidates for pruning:\n",
    "* the first pointwise convolution in all layers (the `conv 0.0`'s)\n",
    "* the depthwise convolution in all layers (the `conv 1.0`'s)\n",
    "* the second pointwise convolution in all layers (the `conv 2`'s)\n",
    "* some combination of the previous choices\n",
    "\n",
    "The following figure shows our experiment results. We run `L1FilterPruner` to explore some of the previous choices with layer sparsity ranging from 0.1 to 0.9. The x-axis shows the effective global sparsity after `ModelSpeedup`. We observe that jointly pruning the depthwise convolution and the second pointwise convolution often gives higher scores at large global sparsities. \n",
    "\n",
    "<div>\n",
    "<img src=\"step1.png\" width=\"600\"/>\n",
    "</div>\n",
    "\n",
    "Therefore, in the following experiments, we limit the modules to prune to the `conv 1.0`'s and the `conv 2`'s. Thus the config list is always written in the following way:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "config_list = [{\n",
    "    'op_names': ['features.{}.conv.1.0'.format(x) for x in range(2, 18)],\n",
    "    'sparsity': sparsity\n",
    "},{\n",
    "    'op_names': ['features.{}.conv.2'.format(x) for x in range(2, 18)],\n",
    "    'sparsity': sparsity\n",
    "}]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To run some experiments for this step, please run `pruning_experiments.py` and specify the following arguments:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Example shell script: \n",
    "\"\"\"\n",
    "for sparsity in 0.2 0.4 0.6 0.8; do\n",
    "    for pruning_mode in 'conv0' 'conv1' 'conv2' 'conv1andconv2' 'all'; do\n",
    "        python pruning_experiments.py \\\n",
    "            --experiment_dir pretrained_mobilenet_v2_torchhub/ \\\n",
    "            --checkpoint_name 'checkpoint_best.pt' \\\n",
    "            --sparsity $sparsity \\\n",
    "            --pruning_mode $pruning_mode \\\n",
    "            --pruner_name l1 \\\n",
    "            --speedup \\\n",
    "            --finetune_epochs 30\n",
    "    done\n",
    "done\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: trying one-shot pruners\n",
    "After determining which modules to prune, we consider the next two questions:\n",
    "* **Which global sparsity range should we aim at?**\n",
    "* **Is there any one-shot pruning algorithm outperforming others at a large margin?**\n",
    "\n",
    "The first problem stems from the natural tradeoff between model size and accuracy. As long as we have acceptable performance, we wish the model to be as small as possible. Therefore, in this step, we can run some one-shot pruners with different sparsity settings, and find a range of sparsities that the model seem to maintain acceptable performance. \n",
    "\n",
    "The following figure summarizes our experiments on three pruners. We perform 30 epoch final finetuning for each experiment. Starting from the original model (with accuracy 0.8), we observe that when the sparsity is below 0.4, the pruned model can easily recover, with the performance approaching the model before pruning. On the other hand, when the sparsity is above 0.7, the model's performance drops too much even after finetuning. Therefore, we limit our search space to sparsity settings between 0.4 and 0.7 in the experiments for the following step 3 and step 4.\n",
    "\n",
    "<div>\n",
    "<img src=\"step2.png\" width=\"900\"/>\n",
    "</div>\n",
    "\n",
    "In addition, we observe that the slim pruner has better performance in the one-shot pruning setting. However, as we will show later, when we consider iterative pruning, the importance of choosing base pruning algorithms seem to be overwhelmed by choosing a correct pruning schedule.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To run some experiments for this step, please run `pruning_experiments.py` and specify the following arguments:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example shell script: \n",
    "\"\"\"\n",
    "for sparsity in 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9; do\n",
    "    for pruning_mode in 'conv1', 'conv1andconv2'; do\n",
    "        python pruning_experiments.py \\\n",
    "            --experiment_dir pretrained_mobilenet_v2_torchhub/ \\\n",
    "            --checkpoint_name 'checkpoint_best.pt' \\\n",
    "            --sparsity $sparsity \\\n",
    "            --pruning_mode $pruning_mode \\\n",
    "            --pruner_name l1 \\\n",
    "            --speedup \\\n",
    "            --finetune_epochs 30\n",
    "    done\n",
    "done\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3: determining iterative pruning strategy\n",
    "\n",
    "Now that we have found a good set of modules to prune and a good range of sparsity settings to experiment on, we can shift our focus to iterative pruning. Iterative pruning interleaves pruning with finetuning, and is often shown be more performant than one-shot pruning, which prunes the model once to the target sparsity. The following figure establishes that the superiority of iterative pruning under the same other settings.\n",
    "<img src=\"step3-1.png\" width=\"900\"/>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we consider the following two important hyperparameters for iterative pruning:\n",
    "* the total number of pruning iterations\n",
    "* the number of finetuning epochs between pruning iterations\n",
    "\n",
    "We experiment we 2, 4, and 8 iterations, with 1 or 3 intermediate finetuning epochs. The results are summarized in the following figure. We clearly observe that increasing the number of pruning iterations significantly improves the final performance, while increasing the number of epochs only helps slightly. Therefore, we recommend that you should spend effort in **determining a correct (often large) number of pruning iterations**, while need not to spend a lot of effort tuning the number of finetuning epochs in between. In our case, we found iteration numbers between 64 and 128 gives the best performance. \n",
    "<img src=\"step3-2.png\" width=\"600\"/>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To run some experiments for this step, please run `pruning_experiments.py` and specify the following arguments:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example shell script: \n",
    "\"\"\"\n",
    "for sparsity in 0.4 0.5 0.6 0.7; do\n",
    "    for n_iters in 2 4 8 16; do\n",
    "        python pruning_experiments.py \\\n",
    "            --experiment_dir pretrained_mobilenet_v2_torchhub/ \\\n",
    "            --checkpoint_name 'checkpoint_best.pt' \\\n",
    "            --sparsity $sparsity \\\n",
    "            --pruning_mode 'conv1andconv2' \\\n",
    "            --pruner_name 'agp' \\\n",
    "            --agp_n_iters $n_iters \\\n",
    "            --speedup \\\n",
    "            --finetune_epochs 30 \\\n",
    "    done\n",
    "done\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4: determining finetuning strategy\n",
    "Finally, after pruning the model, we recommend **using knowledge distillation for finetuning**, which only involves changing several lines of code computing the loss (if we reuse the model before pruning as the teacher model). As shown in the following figure, using knowledge distillation during finetuning can bring about 5 percentage performance improvement in our task. \n",
    "<div>\n",
    "<img src=\"step4.png\" width=\"600\"/>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To run some experiments for this step, please run `pruning_experiments.py` and specify the following arguments:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example shell script: \n",
    "\"\"\"\n",
    "for sparsity in 0.4 0.5 0.6 0.7; do\n",
    "    python pruning_experiments.py \\\n",
    "        --experiment_dir pretrained_mobilenet_v2_torchhub/ \\\n",
    "        --checkpoint_name 'checkpoint_best.pt' \\\n",
    "        --sparsity $sparsity \\\n",
    "        --pruning_mode 'conv1andconv2' \\\n",
    "        --pruner_name 'agp' \\\n",
    "        --speedup \\\n",
    "        --finetune_epochs 80\n",
    "done\n",
    "\n",
    "for sparsity in 0.4 0.5 0.6 0.7; do\n",
    "    python pruning_experiments.py \\\n",
    "        --experiment_dir pretrained_mobilenet_v2_torchhub/ \\\n",
    "        --checkpoint_name 'checkpoint_best.pt' \\\n",
    "        --sparsity $sparsity \\\n",
    "        --pruning_mode 'conv1andconv2' \\\n",
    "        --pruner_name 'agp' \\\n",
    "        --speedup \\\n",
    "        --finetune_epochs 80 \\\n",
    "        -- kd\n",
    "done\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparison with Baseline Methods\n",
    "To confirm that using NNI Pruners indeed results in a model with good performance. We implement and compare with the following baseline methods:\n",
    "1. Shrink the number of channel of all layers to half. This is a basic compression method mentioned by the MobileNet authors. We experiment with the following two settings:\n",
    "    * randomly initialize the weights and train with knowledge distillation\n",
    "    * use `L1FilterPruner` to prune the ImageNet pretrained model to 0.5 sparsity, and then train with knowledge distillation. \n",
    "2. Random pruning to 0.5 sparsity. \n",
    "\n",
    "In the first baseline, we observe that the randomly initialized model only has 0.45 test accuracy, while the model initialized with ImageNet weights has 0.7197 test accuracy after training. However, as shown in the table at the beginning of the notebook, using NNI pruners we can achieve 0.7703 test accuracy with the same amount of finetuning with knowledge distillation. This established the superiority of our approach. As a side remark, this observation is also consistent with the AGP authors' claim that \"large sparse\" models obtained by pruning often outperform \"small dense\" models with similar amount of parameters trained from scratch. \n",
    "\n",
    "In the second baseline, we observe that random pruning performs worse than our one-shot baselines, giving 0.7385 validation accuracy and 0.7182 test accuracy for 0.5 sparsity. This establishes that the pruning has its unique values that cannot be replaced by the final knowledge distillation process. \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this end-to-end example, we have shown the process of using NNI Pruners to compress MobileNetV2 on Stanford Dogs. With iterative pruning and knowledge distillation, we have pruned the MobileNetV2 architecture to 1/3 of its size, with 95% accuracy retained. In the last sections, we also introduce our approach to the problem, and wish that it could be a useful reference if you want to solve a similar problem with NNI Pruners. "
   ]
  }
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