From b4a07e46d3e367b5f8f38d4caf478cd7592d98e5 Mon Sep 17 00:00:00 2001
From: Neta Zmora <neta.zmora@intel.com>
Date: Mon, 29 Oct 2018 13:43:43 +0200
Subject: [PATCH] Jupyter: utility notebook to save intermediate activations to
file
This short notebook performs a forward-pass on a single ImageNet image,
and saves the intermediate results to a file for later inspection
---
jupyter/save_intermediate_feature_maps.ipynb | 182 +++++++++++++++++++
1 file changed, 182 insertions(+)
create mode 100644 jupyter/save_intermediate_feature_maps.ipynb
diff --git a/jupyter/save_intermediate_feature_maps.ipynb b/jupyter/save_intermediate_feature_maps.ipynb
new file mode 100644
index 0000000..ed971b9
--- /dev/null
+++ b/jupyter/save_intermediate_feature_maps.ipynb
@@ -0,0 +1,182 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Save intermediate feature-maps to file\n",
+ "\n",
+ "This short notebook performs a forward-pass on a single ImageNet image, and save the intermediate results to a file for later inspection."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import torch\n",
+ "import torchvision\n",
+ "import torch.nn as nn\n",
+ "from torch.autograd import Variable\n",
+ "\n",
+ "# Relative import of code from distiller, w/o installing the package\n",
+ "import os\n",
+ "import sys\n",
+ "import numpy as np \n",
+ "import matplotlib.pyplot as plt\n",
+ "from PIL import Image\n",
+ "\n",
+ "module_path = os.path.abspath(os.path.join('..'))\n",
+ "if module_path not in sys.path:\n",
+ " sys.path.append(module_path)\n",
+ "\n",
+ "import distiller\n",
+ "import apputils\n",
+ "import models\n",
+ "from apputils import *"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Load ResNet50\n",
+ "resnet50 = models.create_model(pretrained=True, dataset='imagenet', arch='resnet50', parallel=False)\n",
+ "\n",
+ "# Load the \"sparse\" compressed model \n",
+ "resnet50_sparse = models.create_model(pretrained=True, dataset='imagenet', arch='resnet50', parallel=True)\n",
+ "load_checkpoint(resnet50_sparse , \"../examples/classifier_compression/resnet50_checkpoint_70.66-sparse_76.09-top1.pth.tar\");\n",
+ "resnet50_sparse = distiller.make_non_parallel_copy(resnet50_sparse)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from torchvision import transforms\n",
+ "\n",
+ "normalize = transforms.Normalize(\n",
+ " mean=[0.485, 0.456, 0.406],\n",
+ " std=[0.229, 0.224, 0.225]\n",
+ ")\n",
+ "preprocess = transforms.Compose([\n",
+ " transforms.Resize(256),\n",
+ " transforms.CenterCrop(224),\n",
+ " transforms.ToTensor(),\n",
+ " normalize\n",
+ "])\n",
+ "\n",
+ "# Open some file and do a forward-pass. We do this to check everything is cool.\n",
+ "img = Image.open(\"../../data.imagenet/val/n02669723/ILSVRC2012_val_00029372.JPEG\")\n",
+ "img = preprocess(img)\n",
+ "img = img.unsqueeze_(0)\n",
+ "\n",
+ "sample = Variable(img)\n",
+ "\n",
+ "resnet50_sparse.eval()\n",
+ "predictions = resnet50_sparse(sample.cuda())\n",
+ "\n",
+ "top1_vals, top1_indices = torch.max(predictions, 1)\n",
+ "print(top1_vals.item())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [],
+ "source": [
+ "# This is where the work gets done:\n",
+ "# We register for forward callbacks, perform a forward pass, and in the callbacks we cache the\n",
+ "# intermediate feature-maps.\n",
+ "\n",
+ "intermediary_results = {}\n",
+ "cb_handles = []\n",
+ "\n",
+ "def save_conv_output(m, i, o):\n",
+ " intermediary_results[m.distiller_name] = o.data.cpu().numpy()\n",
+ " \n",
+ "def register_forward_hook(m):\n",
+ " if hasattr(m, 'distiller_name'):\n",
+ " h = m.register_forward_hook(save_conv_output)\n",
+ " cb_handles.append(h)\n",
+ "\n",
+ "distiller.utils.assign_layer_fq_names(resnet50_sparse)\n",
+ "resnet50_sparse.apply(register_forward_hook)\n",
+ "predictions = resnet50_sparse(sample.cuda())\n",
+ "for h in cb_handles:\n",
+ " h.remove()\n",
+ "print(len(intermediary_results))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Now save the feature-maps to file...\n",
+ "import pickle \n",
+ "\n",
+ "with open('resnet50_sparse_fms.pickle', 'wb') as f:\n",
+ " pickle.dump(intermediary_results, f)\n",
+ " #np_weights = pickle.save(handle)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [],
+ "source": [
+ "# This is how you load results later...\n",
+ "\n",
+ "intermediary_results_from_file = {}\n",
+ "\n",
+ "with open('resnet50_sparse_fms.pickle', 'rb') as f:\n",
+ " intermediary_results_from_file = pickle.load(f)\n",
+ " \n",
+ "# To print all layer names:\n",
+ "for name, tensor in intermediary_results_from_file.items():\n",
+ " print(name, tensor.size)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "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": "3.5.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
--
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