6935a111d9
Closes: https://github.com/aai-institute/tianshou/issues/1116 ### API Extensions - Batch received new method: `to_torch_`. #1117 ### Breaking Changes - The method `to_torch` in `data.utils.batch.Batch` is not in-place anymore. Instead, a new method `to_torch_` does the conversion in-place. #1117
410 lines
11 KiB
Plaintext
410 lines
11 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "69y6AHvq1S3f"
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},
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"source": [
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"# Batch\n",
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"In this tutorial, we will introduce the **Batch** to you, which is the most basic data structure in Tianshou. You can consider Batch as a numpy version of python dictionary. It is also similar to pytorch's tensordict,\n",
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"although with a somehow different type structure."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"editable": true,
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"id": "NkfiIe_y2FI-",
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"outputId": "5008275f-8f77-489a-af64-b35af4448589",
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"slideshow": {
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"slide_type": ""
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},
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"tags": [
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"remove-output",
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"hide-cell"
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]
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},
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"outputs": [],
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"source": [
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"%%capture\n",
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"\n",
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"import pickle\n",
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"\n",
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"import numpy as np\n",
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"import torch\n",
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"\n",
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"from tianshou.data import Batch"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"data = Batch(a=4, b=[5, 5], c=\"2312312\", d=(\"a\", -2, -3))\n",
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"print(data)\n",
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"print(data.b)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "S6e6OuXe3UT-"
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},
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"source": [
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"A batch stores all passed in data as key-value pairs, and automatically turns the value into a numpy array if possible.\n",
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"\n",
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"## Why do we need Batch in Tianshou?\n",
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"The motivation behind the implementation of Batch module is simple. In DRL, you need to handle a lot of dictionary-format data. For instance, most algorithms would require you to store state, action, and reward data for every step when interacting with the environment. All of them can be organized as a dictionary, and the\n",
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" Batch class helps Tianshou in unifying the interfaces of a diverse set of algorithms. In addition, Batch supports advanced indexing, concatenation and splitting, formatting print just like any other numpy array, which proved to be helpful for developers.\n",
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"<div align=center>\n",
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"<img src=\"https://tianshou.readthedocs.io/en/master/_images/concepts_arch.png\", title=\"The data flow is converted into a Batch in Tianshou\">\n",
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"\n",
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"<a> Data flow is converted into a Batch in Tianshou </a>\n",
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"</div>\n",
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"\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "_Xenx64M9HhV"
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},
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"source": [
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"## Basic Usages"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "4YGX_f1Z9Uil"
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},
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"source": [
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"### Initialization\n",
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"Batch can be constructed directly from a python dictionary, and all data structures\n",
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" will be converted to numpy arrays if possible."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "Jl3-4BRbp3MM",
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"outputId": "a8b225f6-2893-4716-c694-3c2ff558b7f0"
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},
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"outputs": [],
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"source": [
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"# converted from a python library\n",
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"print(\"========================================\")\n",
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"batch1 = Batch({\"a\": [4, 4], \"b\": (5, 5)})\n",
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"print(batch1)\n",
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"\n",
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"# initialization of batch2 is equivalent to batch1\n",
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"print(\"========================================\")\n",
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"batch2 = Batch(a=[4, 4], b=(5, 5))\n",
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"print(batch2)\n",
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"\n",
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"# the dictionary can be nested, and it will be turned into a nested Batch\n",
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"print(\"========================================\")\n",
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"data = {\n",
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" \"action\": np.array([1.0, 2.0, 3.0]),\n",
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" \"reward\": 3.66,\n",
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" \"obs\": {\n",
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" \"rgb_obs\": np.zeros((3, 3)),\n",
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" \"flatten_obs\": np.ones(5),\n",
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" },\n",
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"}\n",
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"\n",
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"batch3 = Batch(data, extra=\"extra_string\")\n",
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"print(batch3)\n",
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"# batch3.obs is also a Batch\n",
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"print(type(batch3.obs))\n",
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"print(batch3.obs.rgb_obs)\n",
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"\n",
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"# a list of dictionary/Batch will automatically be concatenated/stacked, providing convenience if you\n",
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"# want to use parallelized environments to collect data.\n",
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"print(\"========================================\")\n",
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"batch4 = Batch([data] * 3)\n",
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"print(batch4)\n",
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"print(batch4.obs.rgb_obs.shape)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "JCf6bqY3uf5L"
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},
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"source": [
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"### Getting access to data\n",
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"You can conveniently search or change the key-value pair in a Batch just as if it were a python dictionary."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "2TNIY90-vU9b",
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"outputId": "de52ffe9-03c2-45f2-d95a-4071132daa4a"
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},
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"outputs": [],
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"source": [
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"batch1 = Batch({\"a\": [4, 4], \"b\": (5, 5)})\n",
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"print(batch1)\n",
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"\n",
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"# add or delete key-value pair in batch1\n",
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"print(\"========================================\")\n",
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"batch1.c = Batch(c1=np.arange(3), c2=False)\n",
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"del batch1.a\n",
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"print(batch1)\n",
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"\n",
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"# access value by key\n",
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"print(\"========================================\")\n",
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"assert batch1[\"c\"] is batch1.c\n",
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"print(\"c\" in batch1)\n",
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"\n",
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"# traverse the Batch\n",
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"print(\"========================================\")\n",
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"for key, value in batch1.items():\n",
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" print(str(key) + \": \" + str(value))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "bVywStbV9jD2"
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},
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"source": [
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"### Indexing and Slicing\n",
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"If all values in Batch share the same shape in certain dimensions, Batch can support array-like indexing and slicing."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "gKza3OJnzc_D",
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"outputId": "4f240bfe-4a69-4c1b-b40e-983c5c4d0cbc"
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},
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"outputs": [],
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"source": [
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"# Let us suppose we have collected the data from stepping from 4 environments\n",
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"step_outputs = [\n",
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" {\n",
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" \"act\": np.random.randint(10),\n",
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" \"rew\": 0.0,\n",
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" \"obs\": np.ones((3, 3)),\n",
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" \"info\": {\"done\": np.random.choice(2), \"failed\": False},\n",
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" \"terminated\": False,\n",
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" \"truncated\": False,\n",
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" }\n",
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" for _ in range(4)\n",
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"]\n",
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"batch = Batch(step_outputs)\n",
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"print(batch)\n",
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"print(batch.shape)\n",
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"\n",
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"# advanced indexing is supported, if we only want to select data in a given set of environments\n",
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"print(\"========================================\")\n",
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"print(batch[0])\n",
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"print(batch[[0, 3]])\n",
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"\n",
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"# slicing is also supported\n",
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"print(\"========================================\")\n",
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"print(batch[-2:])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Aggregation and Splitting\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "1vUwQ-Hw9jtu"
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},
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"source": [
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"Again, just like a numpy array. Play the example code below."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "f5UkReyn3_kb",
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"outputId": "e7bb3324-7f20-4810-a328-479117efca55"
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},
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"outputs": [],
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"source": [
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"# concat batches with compatible keys\n",
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"# try incompatible keys yourself if you feel curious\n",
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"print(\"========================================\")\n",
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"b1 = Batch(a=[{\"b\": np.float64(1.0), \"d\": Batch(e=np.array(3.0))}])\n",
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"b2 = Batch(a=[{\"b\": np.float64(4.0), \"d\": {\"e\": np.array(6.0)}}])\n",
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"b12_cat_out = Batch.cat([b1, b2])\n",
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"print(b1)\n",
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"print(b2)\n",
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"print(b12_cat_out)\n",
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"\n",
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"# stack batches with compatible keys\n",
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"# try incompatible keys yourself if you feel curious\n",
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"print(\"========================================\")\n",
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"b3 = Batch(a=np.zeros((3, 2)), b=np.ones((2, 3)), c=Batch(d=[[1], [2]]))\n",
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"b4 = Batch(a=np.ones((3, 2)), b=np.ones((2, 3)), c=Batch(d=[[0], [3]]))\n",
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"b34_stack = Batch.stack((b3, b4), axis=1)\n",
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"print(b3)\n",
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"print(b4)\n",
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"print(b34_stack)\n",
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"\n",
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"# split the batch into small batches of size 1, breaking the order of the data\n",
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"print(\"========================================\")\n",
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"print(type(b34_stack.split(1)))\n",
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"print(list(b34_stack.split(1, shuffle=True)))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "Smc_W1Cx6zRS"
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},
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"source": [
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"### Data type converting\n",
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"Besides numpy array, Batch actually also supports Torch Tensor. The usages are exactly the same. Cool, isn't it?"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "Y6im_Mtb7Ody",
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"outputId": "898e82c4-b940-4c35-a0f9-dedc4a9bc500"
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},
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"outputs": [],
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"source": [
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"batch1 = Batch(a=np.arange(2), b=torch.zeros((2, 2)))\n",
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"batch2 = Batch(a=np.arange(2), b=torch.ones((2, 2)))\n",
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"batch_cat = Batch.cat([batch1, batch2, batch1])\n",
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"print(batch_cat)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "1wfTUVKb6xki"
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},
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"source": [
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"You can convert the data type easily, if you no longer want to use hybrid data type anymore."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "F7WknVs98DHD",
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"outputId": "cfd0712a-1df3-4208-e6cc-9149840bdc40"
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},
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"outputs": [],
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"source": [
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"batch_cat.to_numpy_()\n",
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"print(batch_cat)\n",
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"batch_cat.to_torch_()\n",
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"print(batch_cat)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "NTFVle1-9Biz"
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},
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"source": [
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"Batch is even serializable, just in case you may need to save it to disk or restore it."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "Lnf17OXv9YRb",
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"outputId": "753753f2-3f66-4d4b-b4ff-d57f9c40d1da"
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},
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"outputs": [],
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"source": [
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"batch = Batch(obs=Batch(a=0.0, c=torch.Tensor([1.0, 2.0])), np=np.zeros([3, 4]))\n",
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"batch_pk = pickle.loads(pickle.dumps(batch))\n",
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"print(batch_pk)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "-vPMiPZ-9kJN"
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},
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"source": [
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"## Further Reading"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "8Oc1p8ud9kcu"
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},
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"source": [
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"Would you like to learn more advanced usages of Batch? Feel curious about how data is organized inside the Batch? Check the [documentation](https://tianshou.readthedocs.io/en/master/03_api/data/batch.html) and other [tutorials](https://tianshou.readthedocs.io/en/master/01_tutorials/03_batch.html#) for more details."
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]
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}
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],
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"metadata": {
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"colab": {
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"provenance": []
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},
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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