From aba6e49b8bf43d6c3527efe2f3518fbb46dc3c66 Mon Sep 17 00:00:00 2001
From: Neta Zmora <neta.zmora@intel.com>
Date: Wed, 3 Oct 2018 18:40:46 +0300
Subject: [PATCH] Jupyter notebooks: Add details to the Performance notebook
Showing various details about the performance of ResNet50
---
jupyter/performance.ipynb | 87 ++++++++++++++++++++++++++++++++++++++-
1 file changed, 85 insertions(+), 2 deletions(-)
diff --git a/jupyter/performance.ipynb b/jupyter/performance.ipynb
index 29a00d5..c26a5cb 100644
--- a/jupyter/performance.ipynb
+++ b/jupyter/performance.ipynb
@@ -38,7 +38,7 @@
"metadata": {},
"outputs": [],
"source": [
- "model = models.create_model(pretrained=False, dataset='imagenet', arch='resnet18', parallel=False)"
+ "model = models.create_model(pretrained=False, dataset='imagenet', arch='resnet50', parallel=False)"
]
},
{
@@ -56,6 +56,49 @@
"print(\"Total MACs: \" + \"{:,}\".format(total_macs))"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Let's take a look at how our compute is distibuted:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(\"MAC distribution:\")\n",
+ "counts = df['MACs'].value_counts()\n",
+ "print(counts)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Let's look at which convolutions kernel sizes we're using, and how many instances:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(\"Convolution kernel size distribution:\")\n",
+ "counts = df['Attrs'].value_counts()\n",
+ "print(counts)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Let's look at how the MACs are distributed between the layers and the convolution kernel sizes"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -64,11 +107,30 @@
},
"outputs": [],
"source": [
+ "def get_layer_color(layer_type, attrs):\n",
+ " if layer_type == \"Conv2d\":\n",
+ " if attrs == 'k=(1, 1)':\n",
+ " return 'tomato'\n",
+ " elif attrs == 'k=(3, 3)':\n",
+ " return 'limegreen'\n",
+ " else:\n",
+ " return 'steelblue'\n",
+ " return 'indigo'\n",
+ "\n",
"df_compute = df['MACs']\n",
- "ax = df_compute.plot.bar(figsize=[15,10], title=\"MACs\");\n",
+ "ax = df_compute.plot.bar(figsize=[15,10], title=\"MACs\", \n",
+ " color=[get_layer_color(layer_type, attrs) for layer_type,attrs in zip(df['Type'], df['Attrs'])])\n",
+ "\n",
"ax.set_xticklabels(df.Name, rotation=90);"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### How do the Weights and Feature-maps footprints distribute across the layers:"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -81,6 +143,27 @@
"ax.set_xticklabels(df.Name, rotation=90);"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### How the Arithmetic Intensity distributes across the layers:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_performance = df\n",
+ "df_performance['raw traffic'] = df_footprint['FM volume'] + df_footprint['Weights volume']\n",
+ "df_performance['arithmetic intensity'] = df['MACs'] / df_performance['raw traffic']\n",
+ "df_performance2 = df_performance['arithmetic intensity']\n",
+ "ax = df_performance2.plot.bar(figsize=[15,10], title=\"Arithmetic Intensity\");\n",
+ "ax.set_xticklabels(df.Name, rotation=90);"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
--
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