From 6840e856a3158c71cd6f31c669770d6edf90acd0 Mon Sep 17 00:00:00 2001 From: vfdev Date: Mon, 4 Sep 2023 16:29:56 +0200 Subject: [PATCH 1/2] Temporary manual update for tv tutorial --- ...ion_finetuning_instance_segmentation.ipynb | 1618 +---------------- 1 file changed, 50 insertions(+), 1568 deletions(-) diff --git a/_downloads/torchvision_finetuning_instance_segmentation.ipynb b/_downloads/torchvision_finetuning_instance_segmentation.ipynb index 30184dfb6dc..2d0fdb9c04d 100644 --- a/_downloads/torchvision_finetuning_instance_segmentation.ipynb +++ b/_downloads/torchvision_finetuning_instance_segmentation.ipynb @@ -27,1673 +27,155 @@ } }, "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "DfPPQ6ztJhv4" - }, - "source": [ - "# TorchVision Instance Segmentation Finetuning Tutorial\n", - "\n", - "For this tutorial, we will be finetuning a pre-trained [Mask R-CNN](https://arxiv.org/abs/1703.06870) model in the [*Penn-Fudan Database for Pedestrian Detection and Segmentation*](https://www.cis.upenn.edu/~jshi/ped_html/). It contains 170 images with 345 instances of pedestrians, and we will use it to illustrate how to use the new features in torchvision in order to train an instance segmentation model on a custom dataset.\n", - "\n", - "First, we need to install `pycocotools`. This library will be used for computing the evaluation metrics following the COCO metric for intersection over union." - ] - }, { "cell_type": "code", + "execution_count": null, "metadata": { - "id": "DBIoe_tHTQgV", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "a8aa5ac8-fe40-46a2-d0af-106d7da6e149" + "collapsed": false }, + "outputs": [], "source": [ - "%%shell\n", - "\n", - "pip install cython\n", - "# Install pycocotools, the version by default in Colab\n", - "# has a bug fixed in https://github.com/cocodataset/cocoapi/pull/354\n", - "pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'" - ], - "execution_count": 1, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Requirement already satisfied: cython in /usr/local/lib/python3.7/dist-packages (0.29.24)\n", - "Collecting git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI\n", - " Cloning https://github.com/cocodataset/cocoapi.git to /tmp/pip-req-build-dmgt2eit\n", - " Running command git clone -q https://github.com/cocodataset/cocoapi.git /tmp/pip-req-build-dmgt2eit\n", - "Requirement already satisfied: setuptools>=18.0 in /usr/local/lib/python3.7/dist-packages (from pycocotools==2.0) (57.4.0)\n", - "Requirement already satisfied: cython>=0.27.3 in /usr/local/lib/python3.7/dist-packages (from pycocotools==2.0) (0.29.24)\n", - "Requirement already satisfied: matplotlib>=2.1.0 in /usr/local/lib/python3.7/dist-packages (from pycocotools==2.0) (3.2.2)\n", - "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.1.0->pycocotools==2.0) (0.10.0)\n", - "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.1.0->pycocotools==2.0) (2.4.7)\n", - "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.1.0->pycocotools==2.0) (1.3.2)\n", - "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.1.0->pycocotools==2.0) (2.8.2)\n", - "Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.1.0->pycocotools==2.0) (1.19.5)\n", - "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from cycler>=0.10->matplotlib>=2.1.0->pycocotools==2.0) (1.15.0)\n" - ] - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 1 - } + "# For tips on running notebooks in Google Colab, see\n# https://pytorch.org/tutorials/beginner/colab\n%matplotlib inline" ] }, { "cell_type": "markdown", - "metadata": { - "id": "5Sd4jlGp2eLm" - }, + "metadata": {}, "source": [ - "## Defining the Dataset\n", - "\n", - "The [torchvision reference scripts for training object detection, instance segmentation and person keypoint detection](https://github.com/pytorch/vision/tree/v0.3.0/references/detection) allows for easily supporting adding new custom datasets.\n", - "The dataset should inherit from the standard `torch.utils.data.Dataset` class, and implement `__len__` and `__getitem__`.\n", - "\n", - "The only specificity that we require is that the dataset `__getitem__` should return:\n", - "\n", - "* image: a PIL Image of size (H, W)\n", - "* target: a dict containing the following fields\n", - " * `boxes` (`FloatTensor[N, 4]`): the coordinates of the `N` bounding boxes in `[x0, y0, x1, y1]` format, ranging from `0` to `W` and `0` to `H`\n", - " * `labels` (`Int64Tensor[N]`): the label for each bounding box\n", - " * `image_id` (`Int64Tensor[1]`): an image identifier. It should be unique between all the images in the dataset, and is used during evaluation\n", - " * `area` (`Tensor[N]`): The area of the bounding box. This is used during evaluation with the COCO metric, to separate the metric scores between small, medium and large boxes.\n", - " * `iscrowd` (`UInt8Tensor[N]`): instances with `iscrowd=True` will be ignored during evaluation.\n", - " * (optionally) `masks` (`UInt8Tensor[N, H, W]`): The segmentation masks for each one of the objects\n", - " * (optionally) `keypoints` (`FloatTensor[N, K, 3]`): For each one of the `N` objects, it contains the `K` keypoints in `[x, y, visibility]` format, defining the object. `visibility=0` means that the keypoint is not visible. Note that for data augmentation, the notion of flipping a keypoint is dependent on the data representation, and you should probably adapt `references/detection/transforms.py` for your new keypoint representation\n", - "\n", - "If your model returns the above methods, they will make it work for both training and evaluation, and will use the evaluation scripts from pycocotools.\n", - "\n", - "\n", - "One note on the labels. The model considers class 0 as background. If your dataset does not contain the background class, you should not have 0 in your labels. For example, assuming you have just two classes, cat and dog, you can define 1 (not 0) to represent cats and 2 to represent dogs. So, for instance, if one of the images has both classes, your labels tensor should look like [1,2].\n", - "\n", - "Additionally, if you want to use aspect ratio grouping during training (so that each batch only contains images with similar aspect ratio), then it is recommended to also implement a `get_height_and_width` method, which returns the height and the width of the image. If this method is not provided, we query all elements of the dataset via `__getitem__` , which loads the image in memory and is slower than if a custom method is provided.\n" + "\n# TorchVision Object Detection Finetuning Tutorial\n" ] }, { "cell_type": "markdown", - "metadata": { - "id": "bX0rqK-A3Nbl" - }, + "metadata": {}, "source": [ - "### Writing a custom dataset for Penn-Fudan\n", - "\n", - "Let's write a dataset for the Penn-Fudan dataset.\n", - "\n", - "First, let's download and extract the data, present in a zip file at https://www.cis.upenn.edu/~jshi/ped_html/PennFudanPed.zip" + "For this tutorial, we will be finetuning a pre-trained [Mask\nR-CNN](https://arxiv.org/abs/1703.06870)_ model on the [Penn-Fudan\nDatabase for Pedestrian Detection and\nSegmentation](https://www.cis.upenn.edu/~jshi/ped_html/)_. It contains\n170 images with 345 instances of pedestrians, and we will use it to\nillustrate how to use the new features in torchvision in order to train\nan object detection and instance segmentation model on a custom dataset.\n\n\n.. note ::\n\n This tutorial works only with torchvision version >=0.16 or nightly.\n\n\n## Defining the Dataset\n\nThe reference scripts for training object detection, instance\nsegmentation and person keypoint detection allows for easily supporting\nadding new custom datasets. The dataset should inherit from the standard\n``torch.utils.data.Dataset`` class, and implement ``__len__`` and\n``__getitem__``.\n\nThe only specificity that we require is that the dataset ``__getitem__``\nshould return a tuple:\n\n- image: :class:`torchvision.tv_tensors.Image` of shape ``[3, H, W]``, a pure tensor, or a PIL Image of size ``(H, W)``\n- target: a dict containing the following fields\n\n - ``boxes``, :class:`torchvision.tv_tensors.BoundingBoxes` of shape ``[N, 4]``:\n the coordinates of the ``N`` bounding boxes in ``[x0, y0, x1, y1]`` format, ranging from ``0``\n to ``W`` and ``0`` to ``H``\n - ``labels``, integer :class:`torch.Tensor` of shape ``[N]``: the label for each bounding box.\n ``0`` represents always the background class.\n - ``image_id``, int: an image identifier. It should be\n unique between all the images in the dataset, and is used during\n evaluation\n - ``area``, float :class:`torch.Tensor` of shape ``[N]``: the area of the bounding box. This is used\n during evaluation with the COCO metric, to separate the metric\n scores between small, medium and large boxes.\n - ``iscrowd``, uint8 :class:`torch.Tensor` of shape ``[N]``: instances with ``iscrowd=True`` will be\n ignored during evaluation.\n - (optionally) ``masks``, :class:`torchvision.tv_tensors.Mask` of shape ``[N, H, W]``: the segmentation\n masks for each one of the objects\n\nIf your dataset is compliant with above requirements then it will work for both\ntraining and evaluation codes from the reference script. Evaluation code will use scripts from\n``pycocotools`` which can be installed with ``pip install pycocotools``.\n\n.. note ::\n For Windows, please install ``pycocotools`` from [gautamchitnis](https://github.com/gautamchitnis/cocoapi)_ with command\n\n ``pip install git+https://github.com/gautamchitnis/cocoapi.git@cocodataset-master#subdirectory=PythonAPI``\n\nOne note on the ``labels``. The model considers class ``0`` as background. If your dataset does not contain the background class,\nyou should not have ``0`` in your ``labels``. For example, assuming you have just two classes, *cat* and *dog*, you can\ndefine ``1`` (not ``0``) to represent *cats* and ``2`` to represent *dogs*. So, for instance, if one of the images has both\nclasses, your ``labels`` tensor should look like ``[1, 2]``.\n\nAdditionally, if you want to use aspect ratio grouping during training\n(so that each batch only contains images with similar aspect ratios),\nthen it is recommended to also implement a ``get_height_and_width``\nmethod, which returns the height and the width of the image. If this\nmethod is not provided, we query all elements of the dataset via\n``__getitem__`` , which loads the image in memory and is slower than if\na custom method is provided.\n\n### Writing a custom dataset for PennFudan\n\nLet\u2019s write a dataset for the PennFudan dataset. After [downloading and\nextracting the zip\nfile](https://www.cis.upenn.edu/~jshi/ped_html/PennFudanPed.zip)_, we\nhave the following folder structure:\n\n::\n\n PennFudanPed/\n PedMasks/\n FudanPed00001_mask.png\n FudanPed00002_mask.png\n FudanPed00003_mask.png\n FudanPed00004_mask.png\n ...\n PNGImages/\n FudanPed00001.png\n FudanPed00002.png\n FudanPed00003.png\n FudanPed00004.png\n\nHere is one example of a pair of images and segmentation masks\n\n\n\n\n\nSo each image has a corresponding\nsegmentation mask, where each color correspond to a different instance.\nLet\u2019s write a :class:`torch.utils.data.Dataset` class for this dataset.\nIn the code below, we are wrapping images, bounding boxes and masks into\n``torchvision.TVTensor`` classes so that we will be able to apply torchvision\nbuilt-in transformations ([new Transforms API](https://pytorch.org/vision/stable/transforms.html))\nfor the given object detection and segmentation task.\nNamely, image tensors will be wrapped by :class:`torchvision.tv_tensors.Image`, bounding boxes into\n:class:`torchvision.tv_tensors.BoundingBoxes` and masks into :class:`torchvision.tv_tensors.Mask`.\nAs ``torchvision.TVTensor`` are :class:`torch.Tensor` subclasses, wrapped objects are also tensors and inherit the plain\n:class:`torch.Tensor` API. For more information about torchvision ``tv_tensors`` see\n[this documentation](https://pytorch.org/vision/main/auto_examples/transforms/plot_transforms_getting_started.html#what-are-tvtensors).\n\n" ] }, { "cell_type": "code", + "execution_count": null, "metadata": { - "id": "_t4TBwhHTdkd", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "5b1f86ef-e9af-4625-ca43-92c21daf2186" + "collapsed": false }, + "outputs": [], "source": [ - "%%shell\n", - "\n", - "# download the Penn-Fudan dataset\n", - "wget https://www.cis.upenn.edu/~jshi/ped_html/PennFudanPed.zip .\n", - "# extract it in the current folder\n", - "unzip PennFudanPed.zip" - ], - "execution_count": 2, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "--2021-10-26 12:34:41-- https://www.cis.upenn.edu/~jshi/ped_html/PennFudanPed.zip\n", - "Resolving www.cis.upenn.edu (www.cis.upenn.edu)... 158.130.69.163, 2607:f470:8:64:5ea5::d\n", - "Connecting to www.cis.upenn.edu (www.cis.upenn.edu)|158.130.69.163|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 53723336 (51M) [application/zip]\n", - "Saving to: ‘PennFudanPed.zip.2’\n", - "\n", - "PennFudanPed.zip.2 100%[===================>] 51.23M 68.7MB/s in 0.7s \n", - "\n", - "2021-10-26 12:34:42 (68.7 MB/s) - ‘PennFudanPed.zip.2’ saved [53723336/53723336]\n", - "\n", - "--2021-10-26 12:34:42-- http://./\n", - "Resolving . (.)... failed: No address associated with hostname.\n", - "wget: unable to resolve host address ‘.’\n", - "FINISHED --2021-10-26 12:34:42--\n", - "Total wall clock time: 0.9s\n", - "Downloaded: 1 files, 51M in 0.7s (68.7 MB/s)\n", - "Archive: PennFudanPed.zip\n", - "replace PennFudanPed/added-object-list.txt? 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PennFudanPed/Annotation/PennPed00069.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00070.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00071.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00072.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00073.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00074.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00075.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00076.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00077.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00078.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00079.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00080.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00081.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00082.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00083.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00084.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00085.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00086.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00087.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00088.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00089.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00090.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00091.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00092.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00093.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00094.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00095.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00096.txt \n", - " inflating: PennFudanPed/PedMasks/FudanPed00001_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00002_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00003_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00004_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00005_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00006_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00007_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00008_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00009_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00010_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00011_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00012_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00013_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00014_mask.png \n", - " extracting: PennFudanPed/PedMasks/FudanPed00015_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00016_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00017_mask.png \n", - " extracting: PennFudanPed/PedMasks/FudanPed00018_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00019_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00020_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00021_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00022_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00023_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00024_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00025_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00026_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00027_mask.png \n", - " extracting: PennFudanPed/PedMasks/FudanPed00028_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00029_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00030_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00031_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00032_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00033_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00034_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00035_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00036_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00037_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00038_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00039_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00040_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00041_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00042_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00043_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00044_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00045_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00046_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00047_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00048_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00049_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00050_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00051_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00052_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00053_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00054_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00055_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00056_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00057_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00058_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00059_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00060_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00061_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00062_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00063_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00064_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00065_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00066_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00067_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00068_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00069_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00070_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00071_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00072_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00073_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00074_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00001_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00002_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00003_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00004_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00005_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00006_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00007_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00008_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00009_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00010_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00011_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00012_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00013_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00014_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00015_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00016_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00017_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00018_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00019_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00020_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00021_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00022_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00023_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00024_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00025_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00026_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00027_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00028_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00029_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00030_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00031_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00032_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00033_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00034_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00035_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00036_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00037_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00038_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00039_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00040_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00041_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00042_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00043_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00044_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00045_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00046_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00047_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00048_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00049_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00050_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00051_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00052_mask.png \n", - " extracting: PennFudanPed/PedMasks/PennPed00053_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00054_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00055_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00056_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00057_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00058_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00059_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00060_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00061_mask.png \n", - " extracting: PennFudanPed/PedMasks/PennPed00062_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00063_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00064_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00065_mask.png \n", - " extracting: PennFudanPed/PedMasks/PennPed00066_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00067_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00068_mask.png \n", - " extracting: PennFudanPed/PedMasks/PennPed00069_mask.png \n", - " extracting: PennFudanPed/PedMasks/PennPed00070_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00071_mask.png \n", - " extracting: PennFudanPed/PedMasks/PennPed00072_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00073_mask.png \n", - " extracting: PennFudanPed/PedMasks/PennPed00074_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00075_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00076_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00077_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00078_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00079_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00080_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00081_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00082_mask.png \n", - " extracting: PennFudanPed/PedMasks/PennPed00083_mask.png \n", - " extracting: PennFudanPed/PedMasks/PennPed00084_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00085_mask.png \n", - " extracting: PennFudanPed/PedMasks/PennPed00086_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00087_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00088_mask.png \n", - " extracting: PennFudanPed/PedMasks/PennPed00089_mask.png \n", - " extracting: PennFudanPed/PedMasks/PennPed00090_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00091_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00092_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00093_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00094_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00095_mask.png \n", - " extracting: PennFudanPed/PedMasks/PennPed00096_mask.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00001.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00002.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00003.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00004.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00005.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00006.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00007.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00008.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00009.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00010.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00011.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00012.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00013.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00014.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00015.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00016.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00017.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00018.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00019.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00020.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00021.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00022.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00023.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00024.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00025.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00026.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00027.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00028.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00029.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00030.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00031.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00032.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00033.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00034.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00035.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00036.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00037.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00038.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00039.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00040.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00041.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00042.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00043.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00044.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00045.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00046.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00047.png \n", - " inflating: 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self.transforms = transforms\n # load all image files, sorting them to\n # ensure that they are aligned\n self.imgs = list(sorted(os.listdir(os.path.join(root, \"PNGImages\"))))\n self.masks = list(sorted(os.listdir(os.path.join(root, \"PedMasks\"))))\n\n def __getitem__(self, idx):\n # load images and masks\n img_path = os.path.join(self.root, \"PNGImages\", self.imgs[idx])\n mask_path = os.path.join(self.root, \"PedMasks\", self.masks[idx])\n img = read_image(img_path)\n mask = read_image(mask_path)\n # instances are encoded as different colors\n obj_ids = torch.unique(mask)\n # first id is the background, so remove it\n obj_ids = obj_ids[1:]\n num_objs = len(obj_ids)\n\n # split the color-encoded mask into a set\n # of binary masks\n masks = (mask == obj_ids[:, None, None]).to(dtype=torch.uint8)\n\n # get bounding box coordinates for each mask\n boxes = masks_to_boxes(masks)\n\n # there is only one class\n labels = torch.ones((num_objs,), dtype=torch.int64)\n\n image_id = idx\n area = (boxes[:, 3] - boxes[:, 1]) * (boxes[:, 2] - boxes[:, 0])\n # suppose all instances are not crowd\n iscrowd = torch.zeros((num_objs,), dtype=torch.int64)\n\n # Wrap sample and targets into torchvision tv_tensors:\n img = tv_tensors.Image(img)\n\n target = {}\n target[\"boxes\"] = tv_tensors.BoundingBoxes(boxes, format=\"XYXY\", canvas_size=F.get_size(img))\n target[\"masks\"] = tv_tensors.Mask(masks)\n target[\"labels\"] = labels\n target[\"image_id\"] = image_id\n target[\"area\"] = area\n target[\"iscrowd\"] = iscrowd\n\n if self.transforms is not None:\n img, target = self.transforms(img, target)\n\n return img, target\n\n def __len__(self):\n return len(self.imgs)" ] }, { "cell_type": "markdown", - "metadata": { - "id": "WfwuU-jI3j93" - }, - "source": [ - "Let's have a look at the dataset and how it is layed down.\n", - "\n", - "The data is structured as follows\n", - "```\n", - "PennFudanPed/\n", - " PedMasks/\n", - " FudanPed00001_mask.png\n", - " FudanPed00002_mask.png\n", - " FudanPed00003_mask.png\n", - " FudanPed00004_mask.png\n", - " ...\n", - " PNGImages/\n", - " FudanPed00001.png\n", - " FudanPed00002.png\n", - " FudanPed00003.png\n", - " FudanPed00004.png\n", - "```\n", - "\n", - "Here is one example of an image in the dataset, with its corresponding instance segmentation mask" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "LDjuVFgexFfh", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 553 - }, - "outputId": "f2e0c5c5-a8d0-4999-c94d-1660f153cd00" - }, + "metadata": {}, "source": [ - "from PIL import Image\n", - "Image.open('PennFudanPed/PNGImages/FudanPed00001.png')" - ], - "execution_count": 3, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 3 - } + "That\u2019s all for the dataset. Now let\u2019s define a model that can perform\npredictions on this dataset.\n\n## Defining your model\n\nIn this tutorial, we will be using [Mask\nR-CNN](https://arxiv.org/abs/1703.06870)_, which is based on top of\n[Faster R-CNN](https://arxiv.org/abs/1506.01497)_. Faster R-CNN is a\nmodel that predicts both bounding boxes and class scores for potential\nobjects in the image.\n\n\n\nMask R-CNN adds an extra branch\ninto Faster R-CNN, which also predicts segmentation masks for each\ninstance.\n\n\n\nThere are two common\nsituations where one might want\nto modify one of the available models in TorchVision Model Zoo. The first\nis when we want to start from a pre-trained model, and just finetune the\nlast layer. The other is when we want to replace the backbone of the\nmodel with a different one (for faster predictions, for example).\n\nLet\u2019s go see how we would do one or another in the following sections.\n\n### 1 - Finetuning from a pretrained model\n\nLet\u2019s suppose that you want to start from a model pre-trained on COCO\nand want to finetune it for your particular classes. Here is a possible\nway of doing it:\n\n" ] }, { "cell_type": "code", + "execution_count": null, "metadata": { - "id": "cFHKCvCTxiff", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 553 - }, - "outputId": "f0309090-7704-463f-c4e5-50faceb36dbd" + "collapsed": false }, + "outputs": [], "source": [ - "mask = Image.open('PennFudanPed/PedMasks/FudanPed00001_mask.png')\n", - "# each mask instance has a different color, from zero to N, where\n", - "# N is the number of instances. In order to make visualization easier,\n", - "# let's adda color palette to the mask.\n", - "mask.putpalette([\n", - " 0, 0, 0, # black background\n", - " 255, 0, 0, # index 1 is red\n", - " 255, 255, 0, # index 2 is yellow\n", - " 255, 153, 0, # index 3 is orange\n", - "])\n", - "mask" - ], - "execution_count": 4, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAi8AAAIYAgMAAADYxeTCAAAADFBMVEUAAAD/AAD//wD/mQBMHVIjAAAGkklEQVR4nO3dTZKbPBAGYHB9bNjrEpyCTfakytxH6+8UWk5xyhiPhfkRM1Kr5e7xvO8iiSdx+SnUagns4KpCEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBSmWy0oIll2manDTC52aZPqQRj8wHZpqkFY+0mjB3i5IS/hwlJRXcfWJ0VPD0iLRjTusxVlpSLaPEXzT1OI6JT7l4C3vRNDdMrwVjbphBC+ZmGa9KMHPJpBZNMUxzx/Q6MGZMH6dimHFMH6cnhrkFPzC9Bkyt6cg0mjAGGE5MBQwwPxrTlcGMeRjehfKdME4BplWJscCcYJwmjIbZ9NxDsGJ8B+6JGFsCMxAxThOGtWj8hjzxbFsnhrWC/XkTeQ9h5TFLo2Gt4AXTJz2tUAV7zJD2tDJFY2gV3BXBVLkYy4lprg2pgv1IOU4M8UpaqUv2DaWCnxPKsmLqPAzvnqamVHCpPU1N6cGFmrDH9FQM6zjVmT24xMqdhlldSiuxcqdVcLvCOH5MWgWvLKxF4zF9wnMumjDrUWKtYAqm22AsH8ZvygcyxmnCMBbNm2EYK5hyIre1MFbwu2EcMC/DpKyUwPxujPGYPvopF2CA0YcZgAEmERO/bLfA/G7MCMx3mPgNjWoM3/ktMIyYDpiTqBqm/aotitmfN8liuvKYPvo5LTC/GVMDA8ybYAZxjNGEGRVhahKmK4MxmjCjIkzNgmF6K85owoyKMLUmzGaUEi5DFMGMijA1FTMVwDSaMAaYk8yCP5owRglmnkxXTZhhXTSSmHlmV4owVxJmf7GIA3Mrl6HWgrlBejWYylwrEqYtgamH7fIkiqmqN8B0hTBGLaanYiwPZqRg9hZgCmNqCubQgIEpjNn0vNg+074CE7vtLIUh9byXYGKf1L0AM0hjxh+POVh4MLTri6/AxM7sY897QwxTz3t3TGzTO85sHoyhYI6WN8RsLJELZWAy6cK4d8PUijGDKKb58ZjQ1ObE/P38LXI9KITxPa9XhTGKMI/xir5cXwQz+uFJvANCWUwlj/E9b8jGWFaMSVgPAph8y9Jm+lyMY8RUHjMQMVYThsHi28w1F8Py8ZkVJq3rlTgyz8mkB9MvGOr5AYOl1ohZ/TnymV25nrfC9DSMZcNcNWGGFWYQwxiFmP7+QBozHjGRXa8cZv2AhmFYm7Z3cdrIEjGWG2OomP85e96QibGc+7wtpk/GVC2DxWP6XAxLTAgzyGIejxpZzLazaMTEdT12zG5ppGJ4Pli/wyTdVY8ds53ZaXfVK4XxD2UxRhNmVyNJd9UrhBl2j5MxjsNy2PSmLE7cmObw2gk3oC2EWf1EEGM0YY4NN+HWvGUww+FHEpjQGSQJYxkwx8mUcmF6KoLZ/EwMc5xMKRemmTHHySSHCV4BEcZsX9hEd70VJt8SnEwJt2rnPTKh+hXG7BpcE931VhiXjwlNJhomf6cXvpwYv1KuMDkVXK8xfZCYiLEZmGH+NTiZiBhHx1R3QXAyETE5RWOeb48eqiN4vL7D5BSNmV8tOJmoGEvHzOXyNziZqBhHx3x1t44T4zEdV9F8gYleKdkwZsGc/FUqJqeCGwZMy1XByzgd+z4R4zIw5hTTxGK2n47LKZrVB1WCmNSVMm+3x4DZVrDNwDRnzS0ew1c0p28bx29o+IrmUcJnyqizbb6iqb/ERJ3gsrW9zxIOHIAmHsPX9u6vGsAYKsblYKpwbztbJr7F5J0iNOeTiYLJPMllxrgsTBN4Sb+eH5nfYjJP5QIYQ8dkjtN/AV8GxuZp9nlujikYV8hCwpT46joypshX10Vu9Y4YK4/5KFM0iZjHhuZjOUJlvsO5j/rXHvPc2PBi4hR7TFeo05AwywbUKcAsh4a3aIiYQt8fSsMsJSx3aFaYVvzQ+MGZ/yx+aNaYZXY7BZhlnJi/kDcT4+Qxhb5tOyHrgVGFEa9gtZhWE0Z85d50FmDOMJ0mTKsJc9GEqVRhWk2YxzhZIUy3fXlVmE4UMynCXIA5SasJ02nCTEGMjEUV5nDpQRLT7jGtIKYD5iSTIoyv3+fprBxmGaQd5vWn2ps3+6UxrSbMpAhz0YuxG4z71ZiTj1K2m0cvS3eOebnlC4zA5cXzPiOMcctPLyL1e/KfDi4i9Vs9h6Rdj4tI/Z5FFaaTfJNyn85KCxAEQRAEQRAEQRAEQRAEQRAEQRDk3fMPJ+0iGAT73hIAAAAASUVORK5CYII=\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 4 - } + "import torchvision\nfrom torchvision.models.detection.faster_rcnn import FastRCNNPredictor\n\n# load a model pre-trained on COCO\nmodel = torchvision.models.detection.fasterrcnn_resnet50_fpn(weights=\"DEFAULT\")\n\n# replace the classifier with a new one, that has\n# num_classes which is user-defined\nnum_classes = 2 # 1 class (person) + background\n# get number of input features for the classifier\nin_features = model.roi_heads.box_predictor.cls_score.in_features\n# replace the pre-trained head with a new one\nmodel.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)" ] }, { "cell_type": "markdown", - "metadata": { - "id": "C9Ee5NV54Dmj" - }, + "metadata": {}, "source": [ - "So each image has a corresponding segmentation mask, where each color correspond to a different instance. Let's write a `torch.utils.data.Dataset` class for this dataset." + "### 2 - Modifying the model to add a different backbone\n\n" ] }, { "cell_type": "code", + "execution_count": null, "metadata": { - "id": "mTgWtixZTs3X" - }, - "source": [ - "import os\n", - "import numpy as np\n", - "import torch\n", - "import torch.utils.data\n", - "from PIL import Image\n", - "\n", - "\n", - "class PennFudanDataset(torch.utils.data.Dataset):\n", - " def __init__(self, root, transforms=None):\n", - " self.root = root\n", - " self.transforms = transforms\n", - " # load all image files, sorting them to\n", - " # ensure that they are aligned\n", - " self.imgs = list(sorted(os.listdir(os.path.join(root, \"PNGImages\"))))\n", - " self.masks = list(sorted(os.listdir(os.path.join(root, \"PedMasks\"))))\n", - "\n", - " def __getitem__(self, idx):\n", - " # load images ad masks\n", - " img_path = os.path.join(self.root, \"PNGImages\", self.imgs[idx])\n", - " mask_path = os.path.join(self.root, \"PedMasks\", self.masks[idx])\n", - " img = Image.open(img_path).convert(\"RGB\")\n", - " # note that we haven't converted the mask to RGB,\n", - " # because each color corresponds to a different instance\n", - " # with 0 being background\n", - " mask = Image.open(mask_path)\n", - "\n", - " mask = np.array(mask)\n", - " # instances are encoded as different colors\n", - " obj_ids = np.unique(mask)\n", - " # first id is the background, so remove it\n", - " obj_ids = obj_ids[1:]\n", - "\n", - " # split the color-encoded mask into a set\n", - " # of binary masks\n", - " masks = mask == obj_ids[:, None, None]\n", - "\n", - " # get bounding box coordinates for each mask\n", - " num_objs = len(obj_ids)\n", - " boxes = []\n", - " for i in range(num_objs):\n", - " pos = np.where(masks[i])\n", - " xmin = np.min(pos[1])\n", - " xmax = np.max(pos[1])\n", - " ymin = np.min(pos[0])\n", - " ymax = np.max(pos[0])\n", - " boxes.append([xmin, ymin, xmax, ymax])\n", - "\n", - " boxes = torch.as_tensor(boxes, dtype=torch.float32)\n", - " # there is only one class\n", - " labels = torch.ones((num_objs,), dtype=torch.int64)\n", - " masks = torch.as_tensor(masks, dtype=torch.uint8)\n", - "\n", - " image_id = torch.tensor([idx])\n", - " area = (boxes[:, 3] - boxes[:, 1]) * (boxes[:, 2] - boxes[:, 0])\n", - " # suppose all instances are not crowd\n", - " iscrowd = torch.zeros((num_objs,), dtype=torch.int64)\n", - "\n", - " target = {}\n", - " target[\"boxes\"] = boxes\n", - " target[\"labels\"] = labels\n", - " target[\"masks\"] = masks\n", - " target[\"image_id\"] = image_id\n", - " target[\"area\"] = area\n", - " target[\"iscrowd\"] = iscrowd\n", - "\n", - " if self.transforms is not None:\n", - " img, target = self.transforms(img, target)\n", - "\n", - " return img, target\n", - "\n", - " def __len__(self):\n", - " return len(self.imgs)" - ], - "execution_count": 5, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "J6f3ZOTJ4Km9" + "collapsed": false }, + "outputs": [], "source": [ - "That's all for the dataset. Let's see how the outputs are structured for this dataset" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "ZEARO4B_ye0s", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "061226b6-45c4-4b51-b650-05687cd8f67a" - }, - "source": [ - "dataset = PennFudanDataset('PennFudanPed/')\n", - "dataset[0]" - ], - "execution_count": 6, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "(,\n", - " {'area': tensor([35358., 36225.]), 'boxes': tensor([[159., 181., 301., 430.],\n", - " [419., 170., 534., 485.]]), 'image_id': tensor([0]), 'iscrowd': tensor([0, 0]), 'labels': tensor([1, 1]), 'masks': tensor([[[0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " ...,\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0]],\n", - " \n", - " [[0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " ...,\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0]]], dtype=torch.uint8)})" - ] - }, - "metadata": {}, - "execution_count": 6 - } + "import torchvision\nfrom torchvision.models.detection import FasterRCNN\nfrom torchvision.models.detection.rpn import AnchorGenerator\n\n# load a pre-trained model for classification and return\n# only the features\nbackbone = torchvision.models.mobilenet_v2(weights=\"DEFAULT\").features\n# ``FasterRCNN`` needs to know the number of\n# output channels in a backbone. For mobilenet_v2, it's 1280\n# so we need to add it here\nbackbone.out_channels = 1280\n\n# let's make the RPN generate 5 x 3 anchors per spatial\n# location, with 5 different sizes and 3 different aspect\n# ratios. We have a Tuple[Tuple[int]] because each feature\n# map could potentially have different sizes and\n# aspect ratios\nanchor_generator = AnchorGenerator(sizes=((32, 64, 128, 256, 512),),\n aspect_ratios=((0.5, 1.0, 2.0),))\n\n# let's define what are the feature maps that we will\n# use to perform the region of interest cropping, as well as\n# the size of the crop after rescaling.\n# if your backbone returns a Tensor, featmap_names is expected to\n# be [0]. More generally, the backbone should return an\n# ``OrderedDict[Tensor]``, and in ``featmap_names`` you can choose which\n# feature maps to use.\nroi_pooler = torchvision.ops.MultiScaleRoIAlign(featmap_names=['0'],\n output_size=7,\n sampling_ratio=2)\n\n# put the pieces together inside a Faster-RCNN model\nmodel = FasterRCNN(backbone,\n num_classes=2,\n rpn_anchor_generator=anchor_generator,\n box_roi_pool=roi_pooler)" ] }, { "cell_type": "markdown", - "metadata": { - "id": "lWOhcsir9Ahx" - }, + "metadata": {}, "source": [ - "So we can see that by default, the dataset returns a `PIL.Image` and a dictionary\n", - "containing several fields, including `boxes`, `labels` and `masks`." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "RoAEkUgn4uEq" - }, - "source": [ - "## Defining your model\n", - "\n", - "In this tutorial, we will be using [Mask R-CNN](https://arxiv.org/abs/1703.06870), which is based on top of [Faster R-CNN](https://arxiv.org/abs/1506.01497). Faster R-CNN is a model that predicts both bounding boxes and class scores for potential objects in the image.\n", - "\n", - "![Faster R-CNN](https://raw.githubusercontent.com/pytorch/vision/temp-tutorial/tutorials/tv_image03.png)\n", - "\n", - "Mask R-CNN adds an extra branch into Faster R-CNN, which also predicts segmentation masks for each instance.\n", - "\n", - "![Mask R-CNN](https://raw.githubusercontent.com/pytorch/vision/temp-tutorial/tutorials/tv_image04.png)\n", - "\n", - "There are two common situations where one might want to modify one of the available models in torchvision modelzoo.\n", - "The first is when we want to start from a pre-trained model, and just finetune the last layer. The other is when we want to replace the backbone of the model with a different one (for faster predictions, for example).\n", - "\n", - "Let's go see how we would do one or another in the following sections.\n", - "\n", - "\n", - "### 1 - Finetuning from a pretrained model\n", - "\n", - "Let's suppose that you want to start from a model pre-trained on COCO and want to finetune it for your particular classes. Here is a possible way of doing it:\n", - "```\n", - "import torchvision\n", - "from torchvision.models.detection.faster_rcnn import FastRCNNPredictor\n", - "\n", - "# load a model pre-trained pre-trained on COCO\n", - "model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)\n", - "\n", - "# replace the classifier with a new one, that has\n", - "# num_classes which is user-defined\n", - "num_classes = 2 # 1 class (person) + background\n", - "# get number of input features for the classifier\n", - "in_features = model.roi_heads.box_predictor.cls_score.in_features\n", - "# replace the pre-trained head with a new one\n", - "model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes) \n", - "```\n", - "\n", - "### 2 - Modifying the model to add a different backbone\n", - "\n", - "Another common situation arises when the user wants to replace the backbone of a detection\n", - "model with a different one. For example, the current default backbone (ResNet-50) might be too big for some applications, and smaller models might be necessary.\n", - "\n", - "Here is how we would go into leveraging the functions provided by torchvision to modify a backbone.\n", - "\n", - "```\n", - "import torchvision\n", - "from torchvision.models.detection import FasterRCNN\n", - "from torchvision.models.detection.rpn import AnchorGenerator\n", - "\n", - "# load a pre-trained model for classification and return\n", - "# only the features\n", - "backbone = torchvision.models.mobilenet_v2(pretrained=True).features\n", - "# FasterRCNN needs to know the number of\n", - "# output channels in a backbone. For mobilenet_v2, it's 1280\n", - "# so we need to add it here\n", - "backbone.out_channels = 1280\n", - "\n", - "# let's make the RPN generate 5 x 3 anchors per spatial\n", - "# location, with 5 different sizes and 3 different aspect\n", - "# ratios. We have a Tuple[Tuple[int]] because each feature\n", - "# map could potentially have different sizes and\n", - "# aspect ratios \n", - "anchor_generator = AnchorGenerator(sizes=((32, 64, 128, 256, 512),),\n", - " aspect_ratios=((0.5, 1.0, 2.0),))\n", - "\n", - "# let's define what are the feature maps that we will\n", - "# use to perform the region of interest cropping, as well as\n", - "# the size of the crop after rescaling.\n", - "# if your backbone returns a Tensor, featmap_names is expected to\n", - "# be [0]. More generally, the backbone should return an\n", - "# OrderedDict[Tensor], and in featmap_names you can choose which\n", - "# feature maps to use.\n", - "roi_pooler = torchvision.ops.MultiScaleRoIAlign(featmap_names=[0],\n", - " output_size=7,\n", - " sampling_ratio=2)\n", - "\n", - "# put the pieces together inside a FasterRCNN model\n", - "model = FasterRCNN(backbone,\n", - " num_classes=2,\n", - " rpn_anchor_generator=anchor_generator,\n", - " box_roi_pool=roi_pooler)\n", - "```\n", - "\n", - "### An Instance segmentation model for PennFudan Dataset\n", - "\n", - "In our case, we want to fine-tune from a pre-trained model, given that our dataset is very small. So we will be following approach number 1.\n", - "\n", - "Here we want to also compute the instance segmentation masks, so we will be using Mask R-CNN:" + "### Object detection and instance segmentation model for PennFudan Dataset\n\nIn our case, we want to finetune from a pre-trained model, given that\nour dataset is very small, so we will be following approach number 1.\n\nHere we want to also compute the instance segmentation masks, so we will\nbe using Mask R-CNN:\n\n" ] }, { "cell_type": "code", + "execution_count": null, "metadata": { - "id": "YjNHjVMOyYlH" - }, - "source": [ - "import torchvision\n", - "from torchvision.models.detection.faster_rcnn import FastRCNNPredictor\n", - "from torchvision.models.detection.mask_rcnn import MaskRCNNPredictor\n", - "\n", - " \n", - "def get_instance_segmentation_model(num_classes):\n", - " # load an instance segmentation model pre-trained on COCO\n", - " model = torchvision.models.detection.maskrcnn_resnet50_fpn(pretrained=True)\n", - "\n", - " # get the number of input features for the classifier\n", - " in_features = model.roi_heads.box_predictor.cls_score.in_features\n", - " # replace the pre-trained head with a new one\n", - " model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)\n", - "\n", - " # now get the number of input features for the mask classifier\n", - " in_features_mask = model.roi_heads.mask_predictor.conv5_mask.in_channels\n", - " hidden_layer = 256\n", - " # and replace the mask predictor with a new one\n", - " model.roi_heads.mask_predictor = MaskRCNNPredictor(in_features_mask,\n", - " hidden_layer,\n", - " num_classes)\n", - "\n", - " return model" - ], - "execution_count": 7, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "-WXLwePV5ieP" - }, - "source": [ - "That's it, this will make model be ready to be trained and evaluated on our custom dataset.\n", - "\n", - "## Training and evaluation functions\n", - "\n", - "In `references/detection/,` we have a number of helper functions to simplify training and evaluating detection models.\n", - "Here, we will use `references/detection/engine.py`, `references/detection/utils.py` and `references/detection/transforms.py`.\n", - "\n", - "Let's copy those files (and their dependencies) in here so that they are available in the notebook" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "UYDb7PBw55b-", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "fc61f74a-e0c2-4a61-a25a-7c393871aaa8" - }, - "source": [ - "%%shell\n", - "\n", - "# Download TorchVision repo to use some files from\n", - "# references/detection\n", - "git clone https://github.com/pytorch/vision.git\n", - "cd vision\n", - "git checkout v0.8.2\n", - "\n", - "cp references/detection/utils.py ../\n", - "cp references/detection/transforms.py ../\n", - "cp references/detection/coco_eval.py ../\n", - "cp references/detection/engine.py ../\n", - "cp references/detection/coco_utils.py ../" - ], - "execution_count": 8, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "fatal: destination path 'vision' already exists and is not an empty directory.\n", - "Previous HEAD position was ca1a6207 [v0.10.1] .circleci: Bump version for pytorch 1.9.1 (#4399)\n", - "HEAD is now at 2f40a483 [v0.8.X] .circleci: Add Python 3.9 to CI (#3063)\n" - ] - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 8 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "2u9e_pdv54nG" + "collapsed": false }, + "outputs": [], "source": [ - "\n", - "\n", - "Let's write some helper functions for data augmentation / transformation, which leverages the functions in `refereces/detection` that we have just copied:\n" + "import torchvision\nfrom torchvision.models.detection.faster_rcnn import FastRCNNPredictor\nfrom torchvision.models.detection.mask_rcnn import MaskRCNNPredictor\n\n\ndef get_model_instance_segmentation(num_classes):\n # load an instance segmentation model pre-trained on COCO\n model = torchvision.models.detection.maskrcnn_resnet50_fpn(weights=\"DEFAULT\")\n\n # get number of input features for the classifier\n in_features = model.roi_heads.box_predictor.cls_score.in_features\n # replace the pre-trained head with a new one\n model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)\n\n # now get the number of input features for the mask classifier\n in_features_mask = model.roi_heads.mask_predictor.conv5_mask.in_channels\n hidden_layer = 256\n # and replace the mask predictor with a new one\n model.roi_heads.mask_predictor = MaskRCNNPredictor(in_features_mask,\n hidden_layer,\n num_classes)\n\n return model" ] }, - { - "cell_type": "code", - "metadata": { - "id": "l79ivkwKy357" - }, - "source": [ - "from engine import train_one_epoch, evaluate\n", - "import utils\n", - "import transforms as T\n", - "\n", - "\n", - "def get_transform(train):\n", - " transforms = []\n", - " # converts the image, a PIL image, into a PyTorch Tensor\n", - " transforms.append(T.ToTensor())\n", - " if train:\n", - " # during training, randomly flip the training images\n", - " # and ground-truth for data augmentation\n", - " transforms.append(T.RandomHorizontalFlip(0.5))\n", - " return T.Compose(transforms)" - ], - "execution_count": 9, - "outputs": [] - }, { "cell_type": "markdown", - "metadata": { - "id": "ovjC81FZfWat" - }, + "metadata": {}, "source": [ - "#### Testing forward() method \n", - "\n", - "Before iterating over the dataset, it’s good to see what the model expects during training and inference time on sample data.\n" + "That\u2019s it, this will make ``model`` be ready to be trained and evaluated\non your custom dataset.\n\n## Putting everything together\n\nIn ``references/detection/``, we have a number of helper functions to\nsimplify training and evaluating detection models. Here, we will use\n``references/detection/engine.py`` and ``references/detection/utils.py``.\nJust download everything under ``references/detection`` to your folder and use them here.\nOn Linux if you have ``wget``, you can download them using below commands:\n\n" ] }, { "cell_type": "code", + "execution_count": null, "metadata": { - "id": "6-NFR--2fXV3", - "outputId": "9b23511c-197d-4a36-a5a6-32d5bcb1faa1", - "colab": { - "base_uri": "https://localhost:8080/" - } + "collapsed": false }, + "outputs": [], "source": [ - "model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)\n", - "dataset = PennFudanDataset('PennFudanPed', get_transform(train=True))\n", - "data_loader = torch.utils.data.DataLoader(\n", - " dataset, batch_size=2, shuffle=True, num_workers=4,\n", - " collate_fn=utils.collate_fn\n", - ")\n", - "# For Training\n", - "images,targets = next(iter(data_loader))\n", - "images = list(image for image in images)\n", - "targets = [{k: v for k, v in t.items()} for t in targets]\n", - "output = model(images,targets) # Returns losses and detections\n", - "# For inference\n", - "model.eval()\n", - "x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)]\n", - "predictions = model(x) # Returns predictions\n", - "\n" - ], - "execution_count": 10, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n", - " cpuset_checked))\n", - "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /pytorch/c10/core/TensorImpl.h:1156.)\n", - " return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)\n" - ] - } + "os.system(\"wget https://raw.githubusercontent.com/pytorch/vision/main/references/detection/engine.py\")\nos.system(\"wget https://raw.githubusercontent.com/pytorch/vision/main/references/detection/utils.py\")\nos.system(\"wget https://raw.githubusercontent.com/pytorch/vision/main/references/detection/coco_utils.py\")\nos.system(\"wget https://raw.githubusercontent.com/pytorch/vision/main/references/detection/coco_eval.py\")\nos.system(\"wget https://raw.githubusercontent.com/pytorch/vision/main/references/detection/transforms.py\")\n\n# Since v0.15.0 torchvision provides `new Transforms API `_\n# to easily write data augmentation pipelines for Object Detection and Segmentation tasks.\n#\n# Let\u2019s write some helper functions for data augmentation /\n# transformation:\n\nfrom torchvision.transforms import v2 as T\n\n\ndef get_transform(train):\n transforms = []\n if train:\n transforms.append(T.RandomHorizontalFlip(0.5))\n transforms.append(T.ToDtype(torch.float, scale=True))\n transforms.append(T.ToPureTensor())\n return T.Compose(transforms)\n\n\n# Testing ``forward()`` method (Optional)\n# ---------------------------------------\n#\n# Before iterating over the dataset, it's good to see what the model\n# expects during training and inference time on sample data.\nimport utils\n\n\nmodel = torchvision.models.detection.fasterrcnn_resnet50_fpn(weights=\"DEFAULT\")\ndataset = PennFudanDataset('data/PennFudanPed', get_transform(train=True))\ndata_loader = torch.utils.data.DataLoader(\n dataset,\n batch_size=2,\n shuffle=True,\n num_workers=4,\n collate_fn=utils.collate_fn\n)\n\n# For Training\nimages, targets = next(iter(data_loader))\nimages = list(image for image in images)\ntargets = [{k: v for k, v in t.items()} for t in targets]\noutput = model(images, targets) # Returns losses and detections\nprint(output)\n\n# For inference\nmodel.eval()\nx = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)]\npredictions = model(x) # Returns predictions\nprint(predictions[0])" ] }, { "cell_type": "markdown", - "metadata": { - "id": "FzCLqiZk-sjf" - }, + "metadata": {}, "source": [ - "#### Note that we do not need to add a mean/std normalization nor image rescaling in the data transforms, as those are handled internally by the Mask R-CNN model." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "3YFJGJxk6XEs" - }, - "source": [ - "### Putting everything together\n", - "\n", - "We now have the dataset class, the models and the data transforms. Let's instantiate them" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "a5dGaIezze3y", - "outputId": "5cf908c4-a3c6-4c7b-b6f5-7633e8b43594", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "source": [ - "# use our dataset and defined transformations\n", - "dataset = PennFudanDataset('PennFudanPed', get_transform(train=True))\n", - "dataset_test = PennFudanDataset('PennFudanPed', get_transform(train=False))\n", - "\n", - "# split the dataset in train and test set\n", - "torch.manual_seed(1)\n", - "indices = torch.randperm(len(dataset)).tolist()\n", - "dataset = torch.utils.data.Subset(dataset, indices[:-50])\n", - "dataset_test = torch.utils.data.Subset(dataset_test, indices[-50:])\n", - "\n", - "# define training and validation data loaders\n", - "data_loader = torch.utils.data.DataLoader(\n", - " dataset, batch_size=2, shuffle=True, num_workers=4,\n", - " collate_fn=utils.collate_fn)\n", - "\n", - "data_loader_test = torch.utils.data.DataLoader(\n", - " dataset_test, batch_size=1, shuffle=False, num_workers=4,\n", - " collate_fn=utils.collate_fn)" - ], - "execution_count": 11, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n", - " cpuset_checked))\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "L5yvZUprj4ZN" - }, - "source": [ - "Now let's instantiate the model and the optimizer" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "zoenkCj18C4h" - }, - "source": [ - "device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n", - "\n", - "# our dataset has two classes only - background and person\n", - "num_classes = 2\n", - "\n", - "# get the model using our helper function\n", - "model = get_instance_segmentation_model(num_classes)\n", - "# move model to the right device\n", - "model.to(device)\n", - "\n", - "# construct an optimizer\n", - "params = [p for p in model.parameters() if p.requires_grad]\n", - "optimizer = torch.optim.SGD(params, lr=0.005,\n", - " momentum=0.9, weight_decay=0.0005)\n", - "\n", - "# and a learning rate scheduler which decreases the learning rate by\n", - "# 10x every 3 epochs\n", - "lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer,\n", - " step_size=3,\n", - " gamma=0.1)" - ], - "execution_count": 12, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "XAd56lt4kDxc" - }, - "source": [ - "And now let's train the model for 10 epochs, evaluating at the end of every epoch." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "at-h4OWK0aoc", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "outputId": "ab6753bc-6251-42e5-d65b-32efec93c61e" - }, - "source": [ - "# let's train it for 10 epochs\n", - "from torch.optim.lr_scheduler import StepLR\n", - "num_epochs = 10\n", - "\n", - "for epoch in range(num_epochs):\n", - " # train for one epoch, printing every 10 iterations\n", - " train_one_epoch(model, optimizer, data_loader, device, epoch, print_freq=10)\n", - " # update the learning rate\n", - " lr_scheduler.step()\n", - " # evaluate on the test dataset\n", - " evaluate(model, data_loader_test, device=device)" - ], - "execution_count": 13, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n", - " cpuset_checked))\n" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Epoch: [0] [ 0/60] eta: 0:02:09 lr: 0.000090 loss: 2.7890 (2.7890) loss_classifier: 0.7472 (0.7472) loss_box_reg: 0.3405 (0.3405) loss_mask: 1.6637 (1.6637) loss_objectness: 0.0351 (0.0351) loss_rpn_box_reg: 0.0025 (0.0025) time: 2.1599 data: 0.3930 max mem: 2160\n", - "Epoch: [0] [10/60] eta: 0:01:27 lr: 0.000936 loss: 1.3985 (1.7301) loss_classifier: 0.5176 (0.4831) loss_box_reg: 0.2951 (0.2971) loss_mask: 0.7160 (0.9201) loss_objectness: 0.0279 (0.0249) loss_rpn_box_reg: 0.0045 (0.0048) time: 1.7424 data: 0.0476 max mem: 3313\n", - "Epoch: [0] [20/60] eta: 0:01:06 lr: 0.001783 loss: 1.0001 (1.2326) loss_classifier: 0.2191 (0.3359) loss_box_reg: 0.2905 (0.2858) loss_mask: 0.3228 (0.5877) loss_objectness: 0.0172 (0.0188) loss_rpn_box_reg: 0.0042 (0.0045) time: 1.6308 data: 0.0124 max mem: 3313\n", - "Epoch: [0] [30/60] eta: 0:00:50 lr: 0.002629 loss: 0.5669 (1.0165) loss_classifier: 0.0969 (0.2557) loss_box_reg: 0.2649 (0.2864) loss_mask: 0.1798 (0.4538) loss_objectness: 0.0055 (0.0156) loss_rpn_box_reg: 0.0045 (0.0050) time: 1.6474 data: 0.0123 max mem: 3313\n", - "Epoch: [0] [40/60] eta: 0:00:33 lr: 0.003476 loss: 0.4475 (0.8844) loss_classifier: 0.0607 (0.2069) loss_box_reg: 0.2160 (0.2690) loss_mask: 0.1690 (0.3906) loss_objectness: 0.0028 (0.0125) loss_rpn_box_reg: 0.0057 (0.0054) time: 1.6841 data: 0.0131 max mem: 3313\n", - "Epoch: [0] [50/60] eta: 0:00:16 lr: 0.004323 loss: 0.3776 (0.7847) loss_classifier: 0.0441 (0.1748) loss_box_reg: 0.1685 (0.2457) loss_mask: 0.1643 (0.3479) loss_objectness: 0.0014 (0.0106) loss_rpn_box_reg: 0.0052 (0.0058) time: 1.5890 data: 0.0128 max mem: 3313\n", - "Epoch: [0] [59/60] eta: 0:00:01 lr: 0.005000 loss: 0.3148 (0.7147) loss_classifier: 0.0355 (0.1551) loss_box_reg: 0.1113 (0.2267) loss_mask: 0.1545 (0.3174) loss_objectness: 0.0016 (0.0096) loss_rpn_box_reg: 0.0053 (0.0060) time: 1.5877 data: 0.0117 max mem: 3313\n", - "Epoch: [0] Total time: 0:01:38 (1.6426 s / it)\n", - "creating index...\n", - "index created!\n", - "Test: [ 0/50] eta: 0:00:33 model_time: 0.4252 (0.4252) evaluator_time: 0.0120 (0.0120) time: 0.6696 data: 0.2314 max mem: 3313\n", - "Test: [49/50] eta: 0:00:00 model_time: 0.3512 (0.3551) evaluator_time: 0.0051 (0.0101) time: 0.3719 data: 0.0059 max mem: 3313\n", - "Test: Total time: 0:00:19 (0.3804 s / it)\n", - "Averaged stats: model_time: 0.3512 (0.3551) evaluator_time: 0.0051 (0.0101)\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - "IoU metric: bbox\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.699\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.976\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.850\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.384\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.719\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.319\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.752\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.753\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.625\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.762\n", - "IoU metric: segm\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.725\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.976\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.905\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.491\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.744\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.325\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.762\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.763\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.662\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.770\n", - "Epoch: [1] [ 0/60] eta: 0:02:16 lr: 0.005000 loss: 0.4690 (0.4690) loss_classifier: 0.0707 (0.0707) loss_box_reg: 0.1582 (0.1582) loss_mask: 0.2151 (0.2151) loss_objectness: 0.0064 (0.0064) loss_rpn_box_reg: 0.0184 (0.0184) time: 2.2765 data: 0.5354 max mem: 3313\n", - "Epoch: [1] [10/60] eta: 0:01:22 lr: 0.005000 loss: 0.2644 (0.3054) loss_classifier: 0.0268 (0.0387) loss_box_reg: 0.0939 (0.1009) loss_mask: 0.1602 (0.1561) loss_objectness: 0.0010 (0.0029) loss_rpn_box_reg: 0.0058 (0.0067) time: 1.6434 data: 0.0563 max mem: 3313\n", - "Epoch: [1] [20/60] eta: 0:01:06 lr: 0.005000 loss: 0.2616 (0.2989) loss_classifier: 0.0322 (0.0391) loss_box_reg: 0.0745 (0.0973) loss_mask: 0.1491 (0.1535) loss_objectness: 0.0009 (0.0028) loss_rpn_box_reg: 0.0042 (0.0063) time: 1.6418 data: 0.0105 max mem: 3355\n", - "Epoch: [1] [30/60] eta: 0:00:50 lr: 0.005000 loss: 0.2768 (0.3087) loss_classifier: 0.0391 (0.0419) loss_box_reg: 0.0901 (0.1040) loss_mask: 0.1446 (0.1536) loss_objectness: 0.0012 (0.0025) loss_rpn_box_reg: 0.0043 (0.0066) time: 1.6819 data: 0.0127 max mem: 3355\n", - "Epoch: [1] [40/60] eta: 0:00:32 lr: 0.005000 loss: 0.2658 (0.2931) loss_classifier: 0.0358 (0.0398) loss_box_reg: 0.0816 (0.0956) loss_mask: 0.1380 (0.1492) loss_objectness: 0.0013 (0.0024) loss_rpn_box_reg: 0.0043 (0.0060) time: 1.5966 data: 0.0129 max mem: 3355\n", - "Epoch: [1] [50/60] eta: 0:00:16 lr: 0.005000 loss: 0.2406 (0.2836) loss_classifier: 0.0341 (0.0380) loss_box_reg: 0.0683 (0.0911) loss_mask: 0.1333 (0.1467) loss_objectness: 0.0009 (0.0025) loss_rpn_box_reg: 0.0030 (0.0054) time: 1.5973 data: 0.0124 max mem: 3355\n", - "Epoch: [1] [59/60] eta: 0:00:01 lr: 0.005000 loss: 0.2592 (0.2819) loss_classifier: 0.0357 (0.0398) loss_box_reg: 0.0770 (0.0905) loss_mask: 0.1350 (0.1440) loss_objectness: 0.0010 (0.0023) loss_rpn_box_reg: 0.0044 (0.0053) time: 1.6656 data: 0.0115 max mem: 3355\n", - "Epoch: [1] Total time: 0:01:38 (1.6463 s / it)\n", - "creating index...\n", - "index created!\n", - "Test: [ 0/50] eta: 0:00:32 model_time: 0.4143 (0.4143) evaluator_time: 0.0047 (0.0047) time: 0.6486 data: 0.2282 max mem: 3355\n", - "Test: [49/50] eta: 0:00:00 model_time: 0.3494 (0.3511) evaluator_time: 0.0042 (0.0076) time: 0.3668 data: 0.0057 max mem: 3355\n", - "Test: Total time: 0:00:18 (0.3733 s / it)\n", - "Averaged stats: model_time: 0.3494 (0.3511) evaluator_time: 0.0042 (0.0076)\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - "IoU metric: bbox\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.748\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.977\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.894\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.404\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.763\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.347\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.811\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.811\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.725\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.817\n", - "IoU metric: segm\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.702\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.977\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.859\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.438\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.715\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.329\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.768\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.768\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.700\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.773\n", - "Epoch: [2] [ 0/60] eta: 0:01:54 lr: 0.005000 loss: 0.2616 (0.2616) loss_classifier: 0.0158 (0.0158) loss_box_reg: 0.0499 (0.0499) loss_mask: 0.1888 (0.1888) loss_objectness: 0.0011 (0.0011) loss_rpn_box_reg: 0.0061 (0.0061) time: 1.9117 data: 0.3713 max mem: 3355\n", - "Epoch: [2] [10/60] eta: 0:01:24 lr: 0.005000 loss: 0.2152 (0.2365) loss_classifier: 0.0257 (0.0332) loss_box_reg: 0.0499 (0.0606) loss_mask: 0.1316 (0.1367) loss_objectness: 0.0009 (0.0014) loss_rpn_box_reg: 0.0035 (0.0045) time: 1.6997 data: 0.0454 max mem: 3355\n", - "Epoch: [2] [20/60] eta: 0:01:07 lr: 0.005000 loss: 0.2182 (0.2497) loss_classifier: 0.0326 (0.0365) loss_box_reg: 0.0586 (0.0707) loss_mask: 0.1240 (0.1367) loss_objectness: 0.0009 (0.0015) loss_rpn_box_reg: 0.0035 (0.0043) time: 1.6672 data: 0.0125 max mem: 3355\n", - "Epoch: [2] [30/60] eta: 0:00:49 lr: 0.005000 loss: 0.2248 (0.2408) loss_classifier: 0.0357 (0.0358) loss_box_reg: 0.0585 (0.0682) loss_mask: 0.1187 (0.1309) loss_objectness: 0.0011 (0.0018) loss_rpn_box_reg: 0.0033 (0.0041) time: 1.6462 data: 0.0125 max mem: 3355\n", - "Epoch: [2] [40/60] eta: 0:00:33 lr: 0.005000 loss: 0.2166 (0.2396) loss_classifier: 0.0302 (0.0346) loss_box_reg: 0.0585 (0.0674) loss_mask: 0.1152 (0.1318) loss_objectness: 0.0006 (0.0015) loss_rpn_box_reg: 0.0036 (0.0042) time: 1.6310 data: 0.0125 max mem: 3355\n", - "Epoch: [2] [50/60] eta: 0:00:16 lr: 0.005000 loss: 0.2040 (0.2331) loss_classifier: 0.0265 (0.0329) loss_box_reg: 0.0577 (0.0658) loss_mask: 0.1156 (0.1292) loss_objectness: 0.0006 (0.0014) loss_rpn_box_reg: 0.0023 (0.0038) time: 1.6264 data: 0.0126 max mem: 3355\n", - "Epoch: [2] [59/60] eta: 0:00:01 lr: 0.005000 loss: 0.2040 (0.2340) loss_classifier: 0.0255 (0.0326) loss_box_reg: 0.0533 (0.0667) loss_mask: 0.1218 (0.1295) loss_objectness: 0.0006 (0.0014) loss_rpn_box_reg: 0.0023 (0.0037) time: 1.5902 data: 0.0129 max mem: 3355\n", - "Epoch: [2] Total time: 0:01:38 (1.6397 s / it)\n", - "creating index...\n", - "index created!\n", - "Test: [ 0/50] eta: 0:00:34 model_time: 0.4317 (0.4317) evaluator_time: 0.0053 (0.0053) time: 0.6899 data: 0.2518 max mem: 3355\n", - "Test: [49/50] eta: 0:00:00 model_time: 0.3555 (0.3510) evaluator_time: 0.0041 (0.0069) time: 0.3678 data: 0.0065 max mem: 3355\n", - "Test: Total time: 0:00:18 (0.3735 s / it)\n", - "Averaged stats: model_time: 0.3555 (0.3510) evaluator_time: 0.0041 (0.0069)\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - "IoU metric: bbox\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.805\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.987\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.932\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.515\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.815\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.370\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.848\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.848\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.762\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.854\n", - "IoU metric: segm\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.742\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.987\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.896\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.458\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.753\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.337\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.787\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.787\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.713\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.792\n", - "Epoch: [3] [ 0/60] eta: 0:01:59 lr: 0.000500 loss: 0.1559 (0.1559) loss_classifier: 0.0195 (0.0195) loss_box_reg: 0.0290 (0.0290) loss_mask: 0.1047 (0.1047) loss_objectness: 0.0002 (0.0002) loss_rpn_box_reg: 0.0024 (0.0024) time: 1.9840 data: 0.4347 max mem: 3355\n", - "Epoch: [3] [10/60] eta: 0:01:22 lr: 0.000500 loss: 0.1956 (0.2034) loss_classifier: 0.0294 (0.0312) loss_box_reg: 0.0424 (0.0435) loss_mask: 0.1096 (0.1237) loss_objectness: 0.0006 (0.0018) loss_rpn_box_reg: 0.0024 (0.0030) time: 1.6581 data: 0.0468 max mem: 3355\n" - ] - }, - { - "output_type": "error", - "ename": "KeyboardInterrupt", - "evalue": "ignored", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mepoch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_epochs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;31m# train for one epoch, printing every 10 iterations\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mtrain_one_epoch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata_loader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprint_freq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 8\u001b[0m \u001b[0;31m# update the learning rate\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0mlr_scheduler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/content/engine.py\u001b[0m in \u001b[0;36mtrain_one_epoch\u001b[0;34m(model, optimizer, data_loader, device, epoch, print_freq)\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[0mlr_scheduler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 51\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 52\u001b[0;31m \u001b[0mmetric_logger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlosses_reduced\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mloss_dict_reduced\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 53\u001b[0m \u001b[0mmetric_logger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparam_groups\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"lr\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/content/utils.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m 151\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 152\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 153\u001b[0;31m \u001b[0mv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 154\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 155\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmeters\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Z6mYGFLxkO8F" - }, - "source": [ - "Now that training has finished, let's have a look at what it actually predicts in a test image" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "YHwIdxH76uPj" - }, - "source": [ - "# pick one image from the test set\n", - "img, _ = dataset_test[0]\n", - "# put the model in evaluation mode\n", - "model.eval()\n", - "with torch.no_grad():\n", - " prediction = model([img.to(device)])" - ], - "execution_count": 14, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "DmN602iKsuey" - }, - "source": [ - "Printing the prediction shows that we have a list of dictionaries. Each element of the list corresponds to a different image. As we have a single image, there is a single dictionary in the list.\n", - "The dictionary contains the predictions for the image we passed. In this case, we can see that it contains `boxes`, `labels`, `masks` and `scores` as fields." + "Let\u2019s now write the main function which performs the training and the\nvalidation:\n\n" ] }, { "cell_type": "code", + "execution_count": null, "metadata": { - "id": "Lkmb3qUu6zw3", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "7e876402-3a0f-4696-a13e-6ddcabb5fb77" + "collapsed": false }, + "outputs": [], "source": [ - "prediction" - ], - "execution_count": 15, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "[{'boxes': tensor([[ 62.4634, 41.9885, 194.4252, 324.7122],\n", - " [276.3589, 23.3166, 291.1442, 74.5785]], device='cuda:0'),\n", - " 'labels': tensor([1, 1], device='cuda:0'),\n", - " 'masks': tensor([[[[0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]]],\n", - " \n", - " \n", - " [[[0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]]]], device='cuda:0'),\n", - " 'scores': tensor([0.9969, 0.7550], device='cuda:0')}]" - ] - }, - "metadata": {}, - "execution_count": 15 - } + "from engine import train_one_epoch, evaluate\n\n# train on the GPU or on the CPU, if a GPU is not available\ndevice = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n\n# our dataset has two classes only - background and person\nnum_classes = 2\n# use our dataset and defined transformations\ndataset = PennFudanDataset('data/PennFudanPed', get_transform(train=True))\ndataset_test = PennFudanDataset('data/PennFudanPed', get_transform(train=False))\n\n# split the dataset in train and test set\nindices = torch.randperm(len(dataset)).tolist()\ndataset = torch.utils.data.Subset(dataset, indices[:-50])\ndataset_test = torch.utils.data.Subset(dataset_test, indices[-50:])\n\n# define training and validation data loaders\ndata_loader = torch.utils.data.DataLoader(\n dataset,\n batch_size=2,\n shuffle=True,\n num_workers=4,\n collate_fn=utils.collate_fn\n)\n\ndata_loader_test = torch.utils.data.DataLoader(\n dataset_test,\n batch_size=1,\n shuffle=False,\n num_workers=4,\n collate_fn=utils.collate_fn\n)\n\n# get the model using our helper function\nmodel = get_model_instance_segmentation(num_classes)\n\n# move model to the right device\nmodel.to(device)\n\n# construct an optimizer\nparams = [p for p in model.parameters() if p.requires_grad]\noptimizer = torch.optim.SGD(\n params,\n lr=0.005,\n momentum=0.9,\n weight_decay=0.0005\n)\n\n# and a learning rate scheduler\nlr_scheduler = torch.optim.lr_scheduler.StepLR(\n optimizer,\n step_size=3,\n gamma=0.1\n)\n\n# let's train it for 5 epochs\nnum_epochs = 5\n\nfor epoch in range(num_epochs):\n # train for one epoch, printing every 10 iterations\n train_one_epoch(model, optimizer, data_loader, device, epoch, print_freq=10)\n # update the learning rate\n lr_scheduler.step()\n # evaluate on the test dataset\n evaluate(model, data_loader_test, device=device)\n\nprint(\"That's it!\")" ] }, { "cell_type": "markdown", - "metadata": { - "id": "RwT21rzotFbH" - }, + "metadata": {}, "source": [ - "Let's inspect the image and the predicted segmentation masks.\n", - "\n", - "For that, we need to convert the image, which has been rescaled to 0-1 and had the channels flipped so that we have it in `[C, H, W]` format." + "So after one epoch of training, we obtain a COCO-style mAP > 50, and\na mask mAP of 65.\n\nBut what do the predictions look like? Let\u2019s take one image in the\ndataset and verify\n\n\n\n\n" ] }, { "cell_type": "code", + "execution_count": null, "metadata": { - "id": "bpqN9t1u7B2J", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 366 - }, - "outputId": "b998eca7-5e3c-4ea6-ccfc-052fef26c5a0" + "collapsed": false }, + "outputs": [], "source": [ - "Image.fromarray(img.mul(255).permute(1, 2, 0).byte().numpy())" - ], - "execution_count": 16, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 16 - } + "import matplotlib.pyplot as plt\n\nfrom torchvision.utils import draw_bounding_boxes, draw_segmentation_masks\n\n\nimage = read_image(\"../_static/img/tv_tutorial/tv_image05.png\")\neval_transform = get_transform(train=False)\n\nmodel.eval()\nwith torch.no_grad():\n x = eval_transform(image)\n # convert RGBA -> RGB and move to device\n x = x[:3, ...].to(device)\n predictions = model([x, ])\n pred = predictions[0]\n\n\nimage = (255.0 * (image - image.min()) / (image.max() - image.min())).to(torch.uint8)\nimage = image[:3, ...]\npred_labels = [f\"pedestrian: {score:.3f}\" for label, score in zip(pred[\"labels\"], pred[\"scores\"])]\npred_boxes = pred[\"boxes\"].long()\noutput_image = draw_bounding_boxes(image, pred_boxes, pred_labels, colors=\"red\")\n\nmasks = (pred[\"masks\"] > 0.7).squeeze(1)\noutput_image = draw_segmentation_masks(output_image, masks, alpha=0.5, colors=\"blue\")\n\n\nplt.figure(figsize=(12, 12))\nplt.imshow(output_image.permute(1, 2, 0))" ] }, { "cell_type": "markdown", - "metadata": { - "id": "M58J3O9OtT1G" - }, - "source": [ - "And let's now visualize the top predicted segmentation mask. The masks are predicted as `[N, 1, H, W]`, where `N` is the number of predictions, and are probability maps between 0-1." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "5v5S3bm07SO1", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 366 - }, - "outputId": "89e92ecd-dbca-4939-fb94-8d2f1b3a7cc1" - }, - "source": [ - "Image.fromarray(prediction[0]['masks'][0, 0].mul(255).byte().cpu().numpy())" - ], - "execution_count": 17, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "image/png": 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hG2oHRIbYejd0zpCeZ0T+8UMAYkOguTAUY4qAgBxjlgNCAPweAiw8aVYRyHHKNtQtGmI2LmW32zimA9PmH8XxzY0NI2heOIoxAQBQTCnnQ5Kuz0IAEBRMeTacQ91OhoxxKip+U7llqvfp3dIRSUImYh+quq68ZSIAUFHJAAgixW0fIiViNjbzd4tTzKoyjcPQMxo0oKDctKt1mud5PkOwdESSiI0xddOu1k3lHQlnRNCyFbhUsB/21xBb73J6nDJSUJE49Tsm4xERgerV7QBDT5pETx0IHJUk61zdtOt1qILFXJq4VQBwv7flEJaQjHUuzU8u+qqa0zR01nglIkKuV/0EW9Iop094H9Pcyo6b1WrlnXeaExGiqgAq6p6lA0BsnZufjExQVZU4DTvnkxIzsdarKQFJHPPp2yqPqyRf1U27WhtrjaTS8CiCiiAHkrTsCXDuyfANLfUk82BDnRTZsIF6SooaR8N68iL4ozpu66uqaddrYuJUOrCW65rKweYGaKxz9uk4IQWRNPVUTUmI2VqskyLEYWdYTj6t85hKMtaHumlXKwQE4AefhIiqcjBHxNY/myekAJDjRNhMSYmMdZyUDI67e0un74Y7HkmID+YmKqJlsKSWqay6sHQIiK3zPygJJE4g7RQV2VhnhIzH3bdgWE4+rfOoPsn5UDftepVTyro0PKog0hICHEYTlXlCTxw3KuSIkvopKbGxzqL1M9y3wfLpuymORhKWITV1s1qt5zjPuYzfVFUAoQe/fUgagK1zxjzbI6SQNM39lATZWOdNzhn/acI5GuKPRRIisHGhrqoQAiJoetg+plgeQeTt1CQqlETCCzNgVVTyNI3jwMTGACF7752zOZ1aSkciCbHMGW/r4AwbEYmGmYhwWXgrIjkdluSGV5p2FUHz1G++pphFEBEV2QUv6dTvucchCZEKSWUJILMRawxR2dRalJRTzqIHRH6vsKSoCnnqN19zElUmZkG2IaT3DR/8BRxNSVSUVAVnWYwu5kZEpQBgX678Jks/G2yimKZ++zVnQLDGQlHSfPKnyuORxMaFqqmDM6yqUMyt5IV0MbeipJ+y9PDRF6QEqHnuNl4AEZ0HLEqafhOSEGlRUu2dYVUAy0zF3kBRl/6sg5rWXx8joJCmfmMUkTkDkmKZQvV7+KTF3HzdVMEZVgCIxpTl0SSgCqo5ZdGDzA1eGbikAHnqDQCxNUpsBdiGcPrKgOOFAMQPpxsAoC3mRmWd7WNz++m30deFVE63DhKxsRbZuOKTfheSSgng4rgNAyAaw2UPOSk+tEMeOCPiNZYA0gR5YuO8Z+szIFt/hhmLxz3dqsYvC9zJGsOEtOSrVUTy/k3pZ2LatzK9qCeFPOXJGOtDZX1MXJT0u4QAy+xN6wxhmX6QUipP20vKLMV5jimrvtEauLxvvjKbUlSQuqbrKmOtNVM6y07KY5GkWuqvKCFQjCl2m81u149zKkU4aR77rp5iFvx5ZkOBra8qZ1/Zza2oOU59xwTKm+12N4zx1L2URyRJRHImBMVpnqZus912/TiX9zZJ89iX//fWYxmSddXLmynLTwKJ07BDkEzdrtv10+9CkoKWku2kKjCMw7jbbPdKUhFK89j3w5Sy/NzaFICNrypvXmy0UQSAHKfegqRIfd/3/XjyZbnHVJKKZFBhHfqu3xVTKOa2V9IUs+AbJxyS9aFyryip5ChHy5LiTMM4Dv0UT+2YjkOSLi5JRAlzHrrdbrvd7Lp+mpOIiGCex74b5yjy5ow3Lub2mutSyHFiyHEeaZqmqf99fNJ+7hFmxDR02812u3tw3HufNEwpC/1MSQoAtJgbvri4VFHzzKhxGjzGGOdx+l3MbS8kUQCIQ7e93+66XfdwusF3x/1GDldxOd1e7rVVVMwRJdnRWkw5pXn8TcxteckWyaoqceh2m03X9d04LY4bFiXFA8rdyPjF3F6KARQ1R82R2TCKZEnxdzK3ZRSbyDR02/tN3/f9HAtJKimOpjtESYBsXVhWCr/4g0BiLjWGqAoqOf8+5iaS4zxKljx23W63HYZhiGkZMQE5zuM4TjEm5jekVGYnG3611E8gL+soAAEPfT3/AI50uoGqprG7/5oly7TZ9eNcipKLdSlIzinO0zAEoJ//TEREJmYi0p9Usysu/zrKH/BzHNFxp7HbfBHJMm92/TjPDzJSBSiXt2kcKrLyVshNVBJ2b5aL6FKLcZw/4lUc0SelqbsPIiJxs+vHaV9Kqvscd4rzNA6jcT9xIai49IKVcvCfslRKmI/1B/wER/RJaew2TkQk7Xb9NMeHJgAt/jXFeRz7xqU3/CzSHj9VnOLZWDretQTi1DkSFUlDP4xzjPvBQApQxmvN0zgMIb1xYi9KInp7eLIinKNz4njmBmnsuIytncZpnHMxt9IKqJoJi7nNP1USKi4+iQl/PoVbcXk1/00KS5fJEB1EVdUc5zinJV27FNyoZIjzNAzjlH7quMtEGGKmN8dwHzZF5wg4XsQNadQ06v6xNkvJ1pYqCRRMuijpjavWMn1heY5686eeA0fLJwEkTROrQimykX25bbEIFdU4TWP/hrnBvvOCLr4Z4DuO6ZNyRABV0JLFfxK+qKCmNM/TFN+4ReCyz4vPUZ51GI7bEaD4vQTpx6wiILN9e28gIhG70KxkAIlns6mf4KgkKS5e+qX3fkVEMubtHV24PHSuEkg8V7z4U5xqR8CLfxjRIUoCJDI2NO0scaILrgZ4wDFJenbf1GcfLBPfDzG3RUlTHBn/deYG+9Ps5UAIkcwBJAGpGhfq1Tj1BvUsHW1v/ELH/GbfW9BfLiE9zNwWJTVtU3nzKaKA4z6jf3cgz9MXCnCguZVg0oZ61QTHv76I6QQ4cq3BTw0DkYw1b29sLUWqzaqp3OdQ0gm3vD+HAv2kFOI7EEjZhXrVlObIy7N0RpIAkA9y3AhgbKjbJjiD5x3d8jLORRIRknPOeWcOqLlCZOtDCN65hOnSHJ2NJCybg7x/3qT1wqcqliWBwTtnAS61HOg7zkQSLsvMnXeGD/AyyMaF4L2zqpe/5Z5NSWysO0xJsPS8LSTJxfeXn9PcCksHkVSecUNwzkn6Y0hallB7792r79ffP1kB2bgcvHc28St1gWfERZT0NkvIViB452x87474I+J8JO190kEhALFVDN67cte7sJTOZ277043fNDcAQFbgELx18x+mpBInHdQNiowswTt3yF3v5DgHSQjIrm7Xt6vq1UrIH74GEdn6ukmT5TMVj7yKM5CEiMi+Wt3drSp3+DMRIttQtfNg6dKJ7nPMR0UidvX67m5VW4Q32iaWLwEEJOurpg6WL50JOANJiEjs672SDv8ytr5uK3+JJRxPcRafRMSuXt3erSpLByYaEQDI+qrdLtWT/3JzQ0Qyvl7f3a0q9yuGw9bXTe3tITfik+JcPsnXq7vbVW0PXPIGUE63qq1KsfJFM93n8kmLublfMDckE+p2UdJFtXQun+Tr9d2d9/Zgy0EAtr5qKm8JL3zFPU+cROzq1d0dm1/oBEVg6+u5Cq92vp0NpycJjQ/hZlUHZ5+PaVnql8qbHP7Q6I5lBRrTpZ9MTk0SAprQtnfr5odRtWXu5H5gEBLhM4eFxMY+DM+5IE5MEiKgrdqbv9a1/8FqlnK4UlVJxKxP11KWTJ15YUzQmXFyc0O0ob39a1HS448sXYTLfHYygM+GuSCxdYa5jAy+oOs+LUmIWEi6W9fOPD3Jl9bm/bxXAyjP4wNiU8YL/MuVhEjmwdyeKUm0bFsszgmJi3P6/rXExtpPUGB6ap/0xNyefbCM5ijDORSIRV9y3H/C6YZoq/b2r3Xt+NkhX5p2U06iqoCcRZ/7JJPL6XbiX/ItnPx0Q7Shvbl7QUnLuIWUREUV2Wjpqfn+xcRGrSG8tL2d2HEDIJrQ3v5VPw8BdBmFk1IUFVUuDQTPTzdYzO2il7cTkoSAxlpj121TV0/HaoiI5Hme5zmlmERUdU4pZ8PG8HebQyS2LtT1NKvI5XJKpyMJCdFVoar+umn8Mr4UAEobSooxTuM4zimlJKqqYajHyvsAiPsqcARENL5e3wwDHDw79wQ4GUlISOTqdtX+ta694cfj7CHP4zQO/dBPaZnPpdU4DHXTADM9UhKiDc3q1kCO+XJSOqWSmFy9vr27Wzf+cUSooGka+r7rut2Yc06qAFoN9ThlYO/kMUtkQrO+gRwHgvOsu3kBp1PS8kby13/tze3hQ6p5HrvtdrvbDjnnrACg1TCOk7Kr5XtXKSKi9c36VuJgSC92Nzmpkoyv13/9TzG3ByWpqqZ52G3uN/ebPksuAqnGcZzY10lwv5YKEYqSdvPgzbmWlLyAU5JUXtv++6914/nBcWtR0tBt7r99/daVyQEIWI3jNPl6FfXRoHZEtKFe92PnDZ18yv2rOKG5IbOrV3/9z3rxSfuP7JX07cuXL52ICCAgVuM0zvVqSo8GLJV3lmY97CpvSH7ekntCnFxJf/1309TefA+aVVXzNHSbb1//+WerUubjYzWOY1x1Y3o8DAFJbGimaVM7Jjr5LoDXcErHzezq9d3/eO+9eZolWZT0z/9tVFUBCbGapinedVP6vlIYAQBtaNK8KjRfqk7pNCQtD/mhbdq2tfZ7maRCTinFbrfbbXe73a5TBQVEQkVQ3O12u846Ad7fhZGtr5q6CsEjqJxlDMAPOAlJy7W2be/WtaOnljaP4zj+5+v9thvn9LCuW8vaxH777R9bVZVaJCIoVTvG+VA1zcCEkvXkO4FewGlIIiRbtbc3d6vK0fcISUVl7ne73X++3m/7YUoCD3sDJEcz9duvrWmjIDAsWiI21vuqbgZCzSj5AiZ3IiUR29De/Ndf69p9f+xQ1SzzsP327Z8v37bdMJdN1KX2qCzg3H6rKApZLvkAVEC2zoeqbgfQHA/Y63UCnIIkRCKyVXv7992q9k/3tebYb7788+XB3BS0TByRnCL128qhoA0GkGDZqmCsC1Xd9JrjrHCJaOlkSjKhvfm7+CR6TNLcb77+77f7+203TGnZZqYAoDkh9ltHQLZqHZFRBECl4pPqps9xNlkuUdV9otON2YaipGfmlud+++X/Ntvttt877tJyIxkBeseqNrRTYCP7b2W0kBTn0ebTL056Aac0t5u//1pV7tHboorkedh8/d9d13f9PMf8fWyNZFAdWKJUbTfWJperGhIrplDVbT9P3jCnfwlJe8d9+/dd9TQEEClK6sZhHHN6UBIoCKhk0jjl5qafot0P6yBG8qGqm2bsnZ3pEqXvJwoBiG3V3vx945zjh1h7MbfN1/8dp3mO8rAHUaFM2kZNYxdv/u6muB+0jcjE6qu6aYbOGZPoAq2URycJS4dEqOqmXTWGTZGRqmqep3Hqu+1mM6aYnjVEqgKg5tngdtt1A9u5vGYighoX6qbpm74W3A/ahTNmc4+/4hXJhKau71aVf5REyjmneRj64ct9P7+6W1lVReZh89VlQGOZmAGWu0k7xZj9OIxDzrmMHV4i0d9kEtd3EDG5qlmt79b1417sHOd57HZd92XTT68l9Uuuaeq33xwQOzEWCfbFyuOcMoRxGIc4xxj3W4fgDIXwJ1gWzLZqb27vVsVnF4+keR7HfrvZbr5suteVBKqS53771RLbALqMpS69ASkrhmEch2mcxilRAlDFc1jdkUlCZDYuNOu7u1XtvtcWa45Tv7u/v//2z6afXl3NUTxXv/1q2YaacBluimx9nbISV8M4DkPfE0YEhf33+U2mKO+BxNZV7c1fTxIACjmO/W7z7cvXL/fdLK9OCVYVmfuNNTbULZNZPoutT6LIphrGcegsIxCoCMpPBr8eD6fYzW2LkirP++1uoHkuJP3zZdNPr86bVlTI07BlE+p2sHYZ/4ZsgwKy9cMwjoMlkAySU1YAOcNQnGNveScyxlXtzd2jG4ku5rbdfP3yn2/bfn593rQC5Lkn5bpdD96FPUlOgdj4MA7jODBqippzZFE8R77yFEpyVbP+63ZdP+7/25vb/237YXrdbwNonnuN3K67vgpFSQhsgY1xoRrHcRxQ0zRJikygoKgnZ+moJOF+z1u7vrura8cPTRIP5vafbo5zfvVEUgCZJA60utkNY5UEStk7cnYuxDiO4zhomscxx9lEUD1HOeUxSUJENNZ6Xzer9dq7pU2yDDGdxn632dx/60V+NkNRIYNEcruuKxs8CvOkRmxKyXvvfZzGfsgpzpII8ffazc3EzE3TtjdttbwhlURRznna7bbb3TDFA1YGlzhg7LeVr6qaiIn29fDEbGz2VTtGNtYNMc4xxpMvCTjuHlxrm9VqddOWd+1lpGKeY+y32+1210/psDS+Shz7bRWqejRm73IQSYlNtr4e52ydD/00TeNEIPFYf8XLONpWrmW2f9Oubm7ayhkiKuVskOI4drvtwUoCVchx7LZ13QyjVSgvCbiXkoSqzeqcD9UwDIZB4u+xThEAkKwLVbNa39ysvj/ZqkKex3633RUlHVCKpaggcey3dd0OoyLtX9sKRwq+zoLOhyr0zhDk+PKKk+PhWEvwAKFswWvX69u28qYs5i4lJGO/+yUlAeY4daFuu2FEMvpgbkCsCj4JGB/CGLwhlDj9NkvwYNkW3K5u9j4JH5xwt32HT3LNrh9GMg8lbghEDIBekV2oxjEYQo3TK8tyjoejOm7n62a1XnxSOdwelLTbbXbDFAUOON4Achw7btZ9PxqXl44vBAAGKONefJimKRBqngf7m6x4BXhQ0t4nFXMD1RzHrpjbVF7a3sDikxibXTeMLua9+krahQjZ+jjP0+xBcxy8+T2UhIiIZX1pu7q5qctSrWJukuZhcdwpHbS/dFm8JU3XD6MPeXmaw3LACbH1kuM8R685Tt3pBwcccw+u9VXdtKv1qizwxaUzIk5jt9vtdrtO9LD3VwVJo6Su64cxxO9+DIEUy+ozjTFGM09D/1ssC0ZAYGOsublZr1ZN5ffjbRU0xXmO3+432115rj08q6EqOU5Dt7W+mjxCWRYPIFpG6+d5nubdruuHKb41T//DOIKSEMl47/3ter1u64WkciVJ4zAMX7/db/c++9DvqbpsPN36apjmsoILcNnamCXncRrH3a4bhnH+/Fu5EAnJ+LquH5TETOVkgzz12923+822O/j0X6AiOc1Dt6vqZorEBCVvJGWZRYrjMAzb7ndREiKR8VW7urlZr1dN5ZcrCahCmrrNt6/F3OKBO6cLVEXSNHbbuhmn2SgjgWJp280ppnnou3676/pxenN9xYdxLJLq1c3tYyUhqIKmqdt8+fZt8Um/lLBXlTgP/bZpxzmqogBgWUeoZQla33Xb3bJ98JMn3RARkYyr29ub9c36seOGsoPq69f7+23XFyUd+teoCkKax853wzDNCkQICqjyiKTtdtf1b+0dOgaO4LiJyPiqKKmtq1B8Uqn8n/r7L/ffNtvdMP+iT1KBNA3ervtxikiZ8bvfTnGexr7bbXfd8Bv4JFw2l/u6vbm5Wa9XwXtrcH9vS1O3+Xq/2Wx38/xr5qYCmuaxo64fpxmJBXGvJElxHod+ty0hwHzyMcvH8ElIxter25ub9WplnXsYL6aax27zZbvdbrqlzuZQKIBgmkfGbhimSGyEFFD35jY9mNv45kqdj+MojpuNr9qb29V6vWJjSpGDLOa2+bLb7XbDocH2HgoKcSKQXT9OM6csglD2NJV1ekPf7bbd4pM+s+NGQmLnvVu1TR28s0yEsLy0qeRS1PD+lkeVnOeh234bwxAMAICoiHZl9feuH5b1n5/a3MpaiCpU67apgneGaR9rl372/YLXffXor6Hc/MZu433w3pTWZhEZhn7oNpuuH6c5HnZn/hg+RBKRYRfqulmvmrpyzj4kbYsKypLX95d9qOQ0D7uNdd55flhtPY7DOGy33TBOc8z55GHSR5XE1oa6Xa3btg7eGl5ibdAHc/vABlYVlTwP3Yacc45UCkcyTeM0dl2JttOrz8HHw8dI4pJDWq9Xe3Mrr23FMPLjRbjvgUrO89h5sM5a2m+1lnmep7kfhqEs25WTl79/UEnGulC16/WD416sTWHvt/eZ/3fwpKWkeWfEWGMxSy40xRjnOE7jNMd4YB7vY/iouT1W0veJx+WS9dhvv+vvUMk0j4aSYWOg7LHMIimlFOM8z3H/z+Ejf8QB+AhJSMzG+XqvJGOYdRnUpioiaQkB3stRURLqzMyshZFcTs2cUkppP3/pA3/EIfiwudlFSXVwTEzL1ttFSHmZIfVen6QZZpQ0MBFBSmUpal78d5Zl2tlnJ2lvbm1TVQapvLYCQAkmH2LJ9313VQGImmYmJNSUYioklZl58F5f98v4MEnWWmut4e/lkaoiKimltAjpvfE2qpbVLoKEqCmllCVLhgeGzoQPk2SMYX5osilTEUWypJTTB28li9liBhQkyY+2WJ+3E/fjSjLG8KNpWbpcSGJMpXD/nc2OZYO7qpTeCRQt4fvpPdCPOIKSeD/Ar9zZlhAp7e1N33dxAwBQVEFBUEQEyA/h+2/VpbRMy+RH9gZLDv8RR+80uGWJuwCoIgLq0k7y/rDr3TiGT1omSOlDB5HkHGNKH464AUoet+yxXlpu3h12vR8fCib3Mw4fxrWUA0nkwdryXknvwDIvAFDLukAVlfdmXT6GD6ZKipL2g3/Kau7CUiwntoi+/zBSAAB5aOJ56HM7N45xujHvG9uKklSL4845fyxVgoq6rIzf5xbOLyM4lk96NB2pZJJSSvEhmHz3kfTcmV2AHwD4IEkiKc5T3+1wnqdQvGuWnMZpHKc5znM3TDHJL5WTPEUpA7wUOXt8jKQ0j12wBkfvvScAwCwiaZqmaY4xxs1umD6eqb80Rx8iSSXOg3eMOjjvykqSR2M2Y4r3u2EucySP9fteBB9SUk7TYBk1V846iwAIIio5xjnGlFIuSsofsLdPgY+ZW5xHRs0xGGvsUiRRSmNiyjnlrjuGuV0cH/RJE6HmODk2xkDpABCVnFJOkrP0XT+nnC83kPU4+JBPymlGzXHq7fc8wD4LIFlEpmGY0jkSrKfFB80NJMWxd0z7AUBLPklERUXjNM/pt7e2j4255j32bdv7uHiPnHJK58hCnxYfIglLoRs+26Wh+3+XEsff+2S74oorrrjiiiuuuOKKK6644oorrrjiiiuuuOLT4v8BAjamCXpoujIAAAAASUVORK5CYII=\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 17 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "0EZCVtCPunrT" - }, + "metadata": {}, "source": [ - "Looks pretty good!\n", - "\n", - "## Wrapping up\n", - "\n", - "In this tutorial, you have learned how to create your own training pipeline for instance segmentation models, on a custom dataset.\n", - "For that, you wrote a `torch.utils.data.Dataset` class that returns the images and the ground truth boxes and segmentation masks. You also leveraged a Mask R-CNN model pre-trained on COCO train2017 in order to perform transfer learning on this new dataset.\n", - "\n", - "For a more complete example, which includes multi-machine / multi-gpu training, check `references/detection/train.py`, which is present in the [torchvision GitHub repo](https://github.com/pytorch/vision/tree/v0.8.2/references/detection). \n", - "\n" + "The results look good!\n\n## Wrapping up\n\nIn this tutorial, you have learned how to create your own training\npipeline for object detection models on a custom dataset. For\nthat, you wrote a ``torch.utils.data.Dataset`` class that returns the\nimages and the ground truth boxes and segmentation masks. You also\nleveraged a Mask R-CNN model pre-trained on COCO train2017 in order to\nperform transfer learning on this new dataset.\n\nFor a more complete example, which includes multi-machine / multi-GPU\ntraining, check ``references/detection/train.py``, which is present in\nthe torchvision repository.\n\n\n" ] } ] From 018c44183e279ee2ffb4875d15719d45101b0a59 Mon Sep 17 00:00:00 2001 From: vfdev Date: Wed, 6 Sep 2023 10:36:26 +0200 Subject: [PATCH 2/2] Synced with merged rst and python version --- .../torchvision_finetuning_instance_segmentation.ipynb | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/_downloads/torchvision_finetuning_instance_segmentation.ipynb b/_downloads/torchvision_finetuning_instance_segmentation.ipynb index 2d0fdb9c04d..162e073b070 100644 --- a/_downloads/torchvision_finetuning_instance_segmentation.ipynb +++ b/_downloads/torchvision_finetuning_instance_segmentation.ipynb @@ -49,7 +49,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "For this tutorial, we will be finetuning a pre-trained [Mask\nR-CNN](https://arxiv.org/abs/1703.06870)_ model on the [Penn-Fudan\nDatabase for Pedestrian Detection and\nSegmentation](https://www.cis.upenn.edu/~jshi/ped_html/)_. It contains\n170 images with 345 instances of pedestrians, and we will use it to\nillustrate how to use the new features in torchvision in order to train\nan object detection and instance segmentation model on a custom dataset.\n\n\n.. note ::\n\n This tutorial works only with torchvision version >=0.16 or nightly.\n\n\n## Defining the Dataset\n\nThe reference scripts for training object detection, instance\nsegmentation and person keypoint detection allows for easily supporting\nadding new custom datasets. The dataset should inherit from the standard\n``torch.utils.data.Dataset`` class, and implement ``__len__`` and\n``__getitem__``.\n\nThe only specificity that we require is that the dataset ``__getitem__``\nshould return a tuple:\n\n- image: :class:`torchvision.tv_tensors.Image` of shape ``[3, H, W]``, a pure tensor, or a PIL Image of size ``(H, W)``\n- target: a dict containing the following fields\n\n - ``boxes``, :class:`torchvision.tv_tensors.BoundingBoxes` of shape ``[N, 4]``:\n the coordinates of the ``N`` bounding boxes in ``[x0, y0, x1, y1]`` format, ranging from ``0``\n to ``W`` and ``0`` to ``H``\n - ``labels``, integer :class:`torch.Tensor` of shape ``[N]``: the label for each bounding box.\n ``0`` represents always the background class.\n - ``image_id``, int: an image identifier. It should be\n unique between all the images in the dataset, and is used during\n evaluation\n - ``area``, float :class:`torch.Tensor` of shape ``[N]``: the area of the bounding box. This is used\n during evaluation with the COCO metric, to separate the metric\n scores between small, medium and large boxes.\n - ``iscrowd``, uint8 :class:`torch.Tensor` of shape ``[N]``: instances with ``iscrowd=True`` will be\n ignored during evaluation.\n - (optionally) ``masks``, :class:`torchvision.tv_tensors.Mask` of shape ``[N, H, W]``: the segmentation\n masks for each one of the objects\n\nIf your dataset is compliant with above requirements then it will work for both\ntraining and evaluation codes from the reference script. Evaluation code will use scripts from\n``pycocotools`` which can be installed with ``pip install pycocotools``.\n\n.. note ::\n For Windows, please install ``pycocotools`` from [gautamchitnis](https://github.com/gautamchitnis/cocoapi)_ with command\n\n ``pip install git+https://github.com/gautamchitnis/cocoapi.git@cocodataset-master#subdirectory=PythonAPI``\n\nOne note on the ``labels``. The model considers class ``0`` as background. If your dataset does not contain the background class,\nyou should not have ``0`` in your ``labels``. For example, assuming you have just two classes, *cat* and *dog*, you can\ndefine ``1`` (not ``0``) to represent *cats* and ``2`` to represent *dogs*. So, for instance, if one of the images has both\nclasses, your ``labels`` tensor should look like ``[1, 2]``.\n\nAdditionally, if you want to use aspect ratio grouping during training\n(so that each batch only contains images with similar aspect ratios),\nthen it is recommended to also implement a ``get_height_and_width``\nmethod, which returns the height and the width of the image. If this\nmethod is not provided, we query all elements of the dataset via\n``__getitem__`` , which loads the image in memory and is slower than if\na custom method is provided.\n\n### Writing a custom dataset for PennFudan\n\nLet\u2019s write a dataset for the PennFudan dataset. After [downloading and\nextracting the zip\nfile](https://www.cis.upenn.edu/~jshi/ped_html/PennFudanPed.zip)_, we\nhave the following folder structure:\n\n::\n\n PennFudanPed/\n PedMasks/\n FudanPed00001_mask.png\n FudanPed00002_mask.png\n FudanPed00003_mask.png\n FudanPed00004_mask.png\n ...\n PNGImages/\n FudanPed00001.png\n FudanPed00002.png\n FudanPed00003.png\n FudanPed00004.png\n\nHere is one example of a pair of images and segmentation masks\n\n\n\n\n\nSo each image has a corresponding\nsegmentation mask, where each color correspond to a different instance.\nLet\u2019s write a :class:`torch.utils.data.Dataset` class for this dataset.\nIn the code below, we are wrapping images, bounding boxes and masks into\n``torchvision.TVTensor`` classes so that we will be able to apply torchvision\nbuilt-in transformations ([new Transforms API](https://pytorch.org/vision/stable/transforms.html))\nfor the given object detection and segmentation task.\nNamely, image tensors will be wrapped by :class:`torchvision.tv_tensors.Image`, bounding boxes into\n:class:`torchvision.tv_tensors.BoundingBoxes` and masks into :class:`torchvision.tv_tensors.Mask`.\nAs ``torchvision.TVTensor`` are :class:`torch.Tensor` subclasses, wrapped objects are also tensors and inherit the plain\n:class:`torch.Tensor` API. For more information about torchvision ``tv_tensors`` see\n[this documentation](https://pytorch.org/vision/main/auto_examples/transforms/plot_transforms_getting_started.html#what-are-tvtensors).\n\n" + ".. tip::\n\n To get the most of this tutorial, we suggest using this\n [Colab Version](https://colab.research.google.com/github/pytorch/tutorials/blob/gh-pages/_downloads/torchvision_finetuning_instance_segmentation.ipynb)_.\n This will allow you to experiment with the information presented below.\n\n\nFor this tutorial, we will be finetuning a pre-trained [Mask\nR-CNN](https://arxiv.org/abs/1703.06870)_ model on the [Penn-Fudan\nDatabase for Pedestrian Detection and\nSegmentation](https://www.cis.upenn.edu/~jshi/ped_html/)_. It contains\n170 images with 345 instances of pedestrians, and we will use it to\nillustrate how to use the new features in torchvision in order to train\nan object detection and instance segmentation model on a custom dataset.\n\n\n.. note ::\n\n This tutorial works only with torchvision version >=0.16 or nightly.\n If you're using torchvision<=0.15, please follow\n [this tutorial instead](https://github.com/pytorch/tutorials/blob/d686b662932a380a58b7683425faa00c06bcf502/intermediate_source/torchvision_tutorial.rst).\n\n\n## Defining the Dataset\n\nThe reference scripts for training object detection, instance\nsegmentation and person keypoint detection allows for easily supporting\nadding new custom datasets. The dataset should inherit from the standard\n``torch.utils.data.Dataset`` class, and implement ``__len__`` and\n``__getitem__``.\n\nThe only specificity that we require is that the dataset ``__getitem__``\nshould return a tuple:\n\n- image: :class:`torchvision.tv_tensors.Image` of shape ``[3, H, W]``, a pure tensor, or a PIL Image of size ``(H, W)``\n- target: a dict containing the following fields\n\n - ``boxes``, :class:`torchvision.tv_tensors.BoundingBoxes` of shape ``[N, 4]``:\n the coordinates of the ``N`` bounding boxes in ``[x0, y0, x1, y1]`` format, ranging from ``0``\n to ``W`` and ``0`` to ``H``\n - ``labels``, integer :class:`torch.Tensor` of shape ``[N]``: the label for each bounding box.\n ``0`` represents always the background class.\n - ``image_id``, int: an image identifier. It should be\n unique between all the images in the dataset, and is used during\n evaluation\n - ``area``, float :class:`torch.Tensor` of shape ``[N]``: the area of the bounding box. This is used\n during evaluation with the COCO metric, to separate the metric\n scores between small, medium and large boxes.\n - ``iscrowd``, uint8 :class:`torch.Tensor` of shape ``[N]``: instances with ``iscrowd=True`` will be\n ignored during evaluation.\n - (optionally) ``masks``, :class:`torchvision.tv_tensors.Mask` of shape ``[N, H, W]``: the segmentation\n masks for each one of the objects\n\nIf your dataset is compliant with above requirements then it will work for both\ntraining and evaluation codes from the reference script. Evaluation code will use scripts from\n``pycocotools`` which can be installed with ``pip install pycocotools``.\n\n.. note ::\n For Windows, please install ``pycocotools`` from [gautamchitnis](https://github.com/gautamchitnis/cocoapi)_ with command\n\n ``pip install git+https://github.com/gautamchitnis/cocoapi.git@cocodataset-master#subdirectory=PythonAPI``\n\nOne note on the ``labels``. The model considers class ``0`` as background. If your dataset does not contain the background class,\nyou should not have ``0`` in your ``labels``. For example, assuming you have just two classes, *cat* and *dog*, you can\ndefine ``1`` (not ``0``) to represent *cats* and ``2`` to represent *dogs*. So, for instance, if one of the images has both\nclasses, your ``labels`` tensor should look like ``[1, 2]``.\n\nAdditionally, if you want to use aspect ratio grouping during training\n(so that each batch only contains images with similar aspect ratios),\nthen it is recommended to also implement a ``get_height_and_width``\nmethod, which returns the height and the width of the image. If this\nmethod is not provided, we query all elements of the dataset via\n``__getitem__`` , which loads the image in memory and is slower than if\na custom method is provided.\n\n### Writing a custom dataset for PennFudan\n\nLet\u2019s write a dataset for the PennFudan dataset. After [downloading and\nextracting the zip\nfile](https://www.cis.upenn.edu/~jshi/ped_html/PennFudanPed.zip)_, we\nhave the following folder structure:\n\n::\n\n PennFudanPed/\n PedMasks/\n FudanPed00001_mask.png\n FudanPed00002_mask.png\n FudanPed00003_mask.png\n FudanPed00004_mask.png\n ...\n PNGImages/\n FudanPed00001.png\n FudanPed00002.png\n FudanPed00003.png\n FudanPed00004.png\n\nHere is one example of a pair of images and segmentation masks\n\n\n\n\n\nSo each image has a corresponding\nsegmentation mask, where each color correspond to a different instance.\nLet\u2019s write a :class:`torch.utils.data.Dataset` class for this dataset.\nIn the code below, we are wrapping images, bounding boxes and masks into\n``torchvision.TVTensor`` classes so that we will be able to apply torchvision\nbuilt-in transformations ([new Transforms API](https://pytorch.org/vision/stable/transforms.html))\nfor the given object detection and segmentation task.\nNamely, image tensors will be wrapped by :class:`torchvision.tv_tensors.Image`, bounding boxes into\n:class:`torchvision.tv_tensors.BoundingBoxes` and masks into :class:`torchvision.tv_tensors.Mask`.\nAs ``torchvision.TVTensor`` are :class:`torch.Tensor` subclasses, wrapped objects are also tensors and inherit the plain\n:class:`torch.Tensor` API. For more information about torchvision ``tv_tensors`` see\n[this documentation](https://pytorch.org/vision/main/auto_examples/transforms/plot_transforms_getting_started.html#what-are-tvtensors).\n\n" ] }, { @@ -96,7 +96,7 @@ }, "outputs": [], "source": [ - "import torchvision\nfrom torchvision.models.detection import FasterRCNN\nfrom torchvision.models.detection.rpn import AnchorGenerator\n\n# load a pre-trained model for classification and return\n# only the features\nbackbone = torchvision.models.mobilenet_v2(weights=\"DEFAULT\").features\n# ``FasterRCNN`` needs to know the number of\n# output channels in a backbone. For mobilenet_v2, it's 1280\n# so we need to add it here\nbackbone.out_channels = 1280\n\n# let's make the RPN generate 5 x 3 anchors per spatial\n# location, with 5 different sizes and 3 different aspect\n# ratios. We have a Tuple[Tuple[int]] because each feature\n# map could potentially have different sizes and\n# aspect ratios\nanchor_generator = AnchorGenerator(sizes=((32, 64, 128, 256, 512),),\n aspect_ratios=((0.5, 1.0, 2.0),))\n\n# let's define what are the feature maps that we will\n# use to perform the region of interest cropping, as well as\n# the size of the crop after rescaling.\n# if your backbone returns a Tensor, featmap_names is expected to\n# be [0]. More generally, the backbone should return an\n# ``OrderedDict[Tensor]``, and in ``featmap_names`` you can choose which\n# feature maps to use.\nroi_pooler = torchvision.ops.MultiScaleRoIAlign(featmap_names=['0'],\n output_size=7,\n sampling_ratio=2)\n\n# put the pieces together inside a Faster-RCNN model\nmodel = FasterRCNN(backbone,\n num_classes=2,\n rpn_anchor_generator=anchor_generator,\n box_roi_pool=roi_pooler)" + "import torchvision\nfrom torchvision.models.detection import FasterRCNN\nfrom torchvision.models.detection.rpn import AnchorGenerator\n\n# load a pre-trained model for classification and return\n# only the features\nbackbone = torchvision.models.mobilenet_v2(weights=\"DEFAULT\").features\n# ``FasterRCNN`` needs to know the number of\n# output channels in a backbone. For mobilenet_v2, it's 1280\n# so we need to add it here\nbackbone.out_channels = 1280\n\n# let's make the RPN generate 5 x 3 anchors per spatial\n# location, with 5 different sizes and 3 different aspect\n# ratios. We have a Tuple[Tuple[int]] because each feature\n# map could potentially have different sizes and\n# aspect ratios\nanchor_generator = AnchorGenerator(\n sizes=((32, 64, 128, 256, 512),),\n aspect_ratios=((0.5, 1.0, 2.0),)\n)\n\n# let's define what are the feature maps that we will\n# use to perform the region of interest cropping, as well as\n# the size of the crop after rescaling.\n# if your backbone returns a Tensor, featmap_names is expected to\n# be [0]. More generally, the backbone should return an\n# ``OrderedDict[Tensor]``, and in ``featmap_names`` you can choose which\n# feature maps to use.\nroi_pooler = torchvision.ops.MultiScaleRoIAlign(\n featmap_names=['0'],\n output_size=7,\n sampling_ratio=2\n)\n\n# put the pieces together inside a Faster-RCNN model\nmodel = FasterRCNN(\n backbone,\n num_classes=2,\n rpn_anchor_generator=anchor_generator,\n box_roi_pool=roi_pooler\n)" ] }, { @@ -114,7 +114,7 @@ }, "outputs": [], "source": [ - "import torchvision\nfrom torchvision.models.detection.faster_rcnn import FastRCNNPredictor\nfrom torchvision.models.detection.mask_rcnn import MaskRCNNPredictor\n\n\ndef get_model_instance_segmentation(num_classes):\n # load an instance segmentation model pre-trained on COCO\n model = torchvision.models.detection.maskrcnn_resnet50_fpn(weights=\"DEFAULT\")\n\n # get number of input features for the classifier\n in_features = model.roi_heads.box_predictor.cls_score.in_features\n # replace the pre-trained head with a new one\n model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)\n\n # now get the number of input features for the mask classifier\n in_features_mask = model.roi_heads.mask_predictor.conv5_mask.in_channels\n hidden_layer = 256\n # and replace the mask predictor with a new one\n model.roi_heads.mask_predictor = MaskRCNNPredictor(in_features_mask,\n hidden_layer,\n num_classes)\n\n return model" + "import torchvision\nfrom torchvision.models.detection.faster_rcnn import FastRCNNPredictor\nfrom torchvision.models.detection.mask_rcnn import MaskRCNNPredictor\n\n\ndef get_model_instance_segmentation(num_classes):\n # load an instance segmentation model pre-trained on COCO\n model = torchvision.models.detection.maskrcnn_resnet50_fpn(weights=\"DEFAULT\")\n\n # get number of input features for the classifier\n in_features = model.roi_heads.box_predictor.cls_score.in_features\n # replace the pre-trained head with a new one\n model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)\n\n # now get the number of input features for the mask classifier\n in_features_mask = model.roi_heads.mask_predictor.conv5_mask.in_channels\n hidden_layer = 256\n # and replace the mask predictor with a new one\n model.roi_heads.mask_predictor = MaskRCNNPredictor(\n in_features_mask,\n hidden_layer,\n num_classes\n )\n\n return model" ] }, { @@ -175,7 +175,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The results look good!\n\n## Wrapping up\n\nIn this tutorial, you have learned how to create your own training\npipeline for object detection models on a custom dataset. For\nthat, you wrote a ``torch.utils.data.Dataset`` class that returns the\nimages and the ground truth boxes and segmentation masks. You also\nleveraged a Mask R-CNN model pre-trained on COCO train2017 in order to\nperform transfer learning on this new dataset.\n\nFor a more complete example, which includes multi-machine / multi-GPU\ntraining, check ``references/detection/train.py``, which is present in\nthe torchvision repository.\n\n\n" + "The results look good!\n\n## Wrapping up\n\nIn this tutorial, you have learned how to create your own training\npipeline for object detection models on a custom dataset. For\nthat, you wrote a ``torch.utils.data.Dataset`` class that returns the\nimages and the ground truth boxes and segmentation masks. You also\nleveraged a Mask R-CNN model pre-trained on COCO train2017 in order to\nperform transfer learning on this new dataset.\n\nFor a more complete example, which includes multi-machine / multi-GPU\ntraining, check ``references/detection/train.py``, which is present in\nthe torchvision repository.\n\nYou can download a full source file for this tutorial\n[here](https://pytorch.org/tutorials/_static/tv-training-code.py)_.\n" ] } ]