Iceberg Detection
[Getting Started Notebook] Iceberg Detection
A Getting Started notebook for Icebeg Detection Puzzle of BlitzX.
Starter Code for IceBerg Detection
What we are going to Learn¶
- How to semantic segmentation using PyTorch.
- Working with videos.
- Testing and Submitting the Results to the Challenge.
Note : Create a copy of the notebook and use the copy for submission. Go to File > Save a Copy in Drive to create a new copy
Setup AIcrowd Utilities 🛠¶
In [ ]:
!pip install aicrowd-cli
%load_ext aicrowd.magic
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Attempting uninstall: requests
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Successfully uninstalled requests-2.23.0
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Successfully uninstalled tqdm-4.41.1
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
google-colab 1.0.0 requires requests~=2.23.0, but you have requests 2.26.0 which is incompatible.
datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.
Successfully installed GitPython-3.1.18 aicrowd-cli-0.1.8 colorama-0.4.4 commonmark-0.9.1 gitdb-4.0.7 requests-2.26.0 requests-toolbelt-0.9.1 rich-10.6.0 smmap-4.0.0 tqdm-4.61.2
How to use this notebook? 📝¶
- Update the config parameters. You can define the common variables here
| Variable | Description |
|---|---|
AICROWD_DATASET_PATH |
Path to the file containing test data (The data will be available at /data/ on aridhia workspace). This should be an absolute path. |
AICROWD_OUTPUTS_PATH |
Path to write the output to. |
AICROWD_ASSETS_DIR |
In case your notebook needs additional files (like model weights, etc.,), you can add them to a directory and specify the path to the directory here (please specify relative path). The contents of this directory will be sent to AIcrowd for evaluation. |
AICROWD_API_KEY |
In order to submit your code to AIcrowd, you need to provide your account's API key. This key is available at https://www.aicrowd.com/participants/me |
- Installing packages. Please use the Install packages 🗃 section to install the packages
- Training your models. All the code within the Training phase ⚙️ section will be skipped during evaluation. Please make sure to save your model weights in the assets directory and load them in the predictions phase section
AIcrowd Runtime Configuration 🧷¶
Define configuration parameters. Please include any files needed for the notebook to run under ASSETS_DIR. We will copy the contents of this directory to your final submission file 🙂
The dataset is available under /data on the workspace.
In [ ]:
import os
# Please use the absolute for the location of the dataset.
# Or you can use relative path with `os.getcwd() + "test_data/test.csv"`
AICROWD_DATASET_PATH = os.getenv("DATASET_PATH", os.getcwd()+"/data/test")
AICROWD_OUTPUTS_PATH = os.getenv("OUTPUTS_DIR", "")
AICROWD_ASSETS_DIR = os.getenv("ASSETS_DIR", "assets")
Install packages 🗃¶
Installing PyTorch & some other libraries
In [ ]:
!pip install git+https://github.com/qubvel/segmentation_models.pytorch pytorch-argus scikit-video natsort
# Installing Scikit Video & FFMPEG
!pip install scikit-video
!pip install ffmpeg
!apt-get install ffmpeg gstreamer1.0-libav vlc
!apt-get update -qq && sudo apt-get -y install \
autoconf \
automake \
build-essential \
cmake \
git-core \
libass-dev \
libfreetype6-dev \
libgnutls28-dev \
libsdl2-dev \
libtool \
libva-dev \
libvdpau-dev \
libvorbis-dev \
libxcb1-dev \
libxcb-shm0-dev \
libxcb-xfixes0-dev \
meson \
ninja-build \
pkg-config \
texinfo \
wget \
yasm \
zlib1g-dev
!apt-get install ffmpeg libsm6 libxext6 -y
!pip install sk-video p-tqdm
!apt-get update
!apt-get install -y python3-opencv
!pip install --upgrade opencv
!pip install --upgrade torch torchvision
Collecting git+https://github.com/qubvel/segmentation_models.pytorch
Cloning https://github.com/qubvel/segmentation_models.pytorch to /tmp/pip-req-build-ohjrwd9q
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Building wheels for collected packages: segmentation-models-pytorch, efficientnet-pytorch, pretrainedmodels
Building wheel for segmentation-models-pytorch (setup.py) ... done
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Stored in directory: /tmp/pip-ephem-wheel-cache-pqqbgxmy/wheels/fa/c5/a8/1e8af6cb04a0974db8a4a156ebd2fdd1d99ad2558d3fce49d4
Building wheel for efficientnet-pytorch (setup.py) ... done
Created wheel for efficientnet-pytorch: filename=efficientnet_pytorch-0.6.3-py3-none-any.whl size=12419 sha256=8aa30a832e1279c14114a6fb9685431a71beac8f176d76fa216668db2deb9776
Stored in directory: /root/.cache/pip/wheels/90/6b/0c/f0ad36d00310e65390b0d4c9218ae6250ac579c92540c9097a
Building wheel for pretrainedmodels (setup.py) ... done
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Successfully built segmentation-models-pytorch efficientnet-pytorch pretrainedmodels
Installing collected packages: munch, timm, pretrainedmodels, efficientnet-pytorch, segmentation-models-pytorch, scikit-video, pytorch-argus
Successfully installed efficientnet-pytorch-0.6.3 munch-2.5.0 pretrainedmodels-0.7.4 pytorch-argus-0.2.1 scikit-video-1.1.11 segmentation-models-pytorch-0.2.0 timm-0.4.12
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Building wheels for collected packages: ffmpeg
Building wheel for ffmpeg (setup.py) ... done
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Reading package lists... Done
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ffmpeg is already the newest version (7:3.4.8-0ubuntu0.2).
The following additional packages will be installed:
fonts-freefont-ttf gstreamer1.0-plugins-base liba52-0.7.4 libaa1
libaribb24-0 libbasicusageenvironment1 libcap2-bin libcddb2 libcdparanoia0
libdca0 libdvbpsi10 libdvdnav4 libdvdread4 libebml4v5 libfaad2 libgpm2
libgroupsock8 libgstreamer-plugins-base1.0-0 libgstreamer1.0-0 libkate1
libldb1 liblirc-client0 liblivemedia62 liblua5.2-0 libmad0 libmatroska6v5
libmicrodns0 libmpcdec6 libmpeg2-4 libmtp-common libmtp-runtime libmtp9
libnfs11 libnotify4 libopenmpt-modplug1 liborc-0.4-0 libpam-cap libplacebo4
libprotobuf-lite10 libproxy-tools libqt5svg5 libqt5x11extras5
libresid-builder0c2a libsdl-image1.2 libsdl1.2debian libsecret-1-0
libsecret-common libshout3 libsidplay2 libsmbclient libspeexdsp1 libtag1v5
libtag1v5-vanilla libtalloc2 libtdb1 libtevent0 libupnp6
libusageenvironment3 libva-wayland2 libvisual-0.4-0 libvlc-bin libvlc5
libvlccore9 libvulkan1 libwbclient0 libxcb-xv0 mesa-vulkan-drivers
notification-daemon python-talloc samba-libs vlc-bin vlc-data vlc-l10n
vlc-plugin-base vlc-plugin-notify vlc-plugin-qt vlc-plugin-samba
vlc-plugin-skins2 vlc-plugin-video-output vlc-plugin-video-splitter
vlc-plugin-visualization
Suggested packages:
gvfs libdvdcss2 gpm libvisual-0.4-plugins gstreamer1.0-tools lirc
The following NEW packages will be installed:
fonts-freefont-ttf gstreamer1.0-libav gstreamer1.0-plugins-base liba52-0.7.4
libaa1 libaribb24-0 libbasicusageenvironment1 libcap2-bin libcddb2
libcdparanoia0 libdca0 libdvbpsi10 libdvdnav4 libdvdread4 libebml4v5
libfaad2 libgpm2 libgroupsock8 libgstreamer-plugins-base1.0-0
libgstreamer1.0-0 libkate1 libldb1 liblirc-client0 liblivemedia62
liblua5.2-0 libmad0 libmatroska6v5 libmicrodns0 libmpcdec6 libmpeg2-4
libmtp-common libmtp-runtime libmtp9 libnfs11 libnotify4 libopenmpt-modplug1
liborc-0.4-0 libpam-cap libplacebo4 libprotobuf-lite10 libproxy-tools
libqt5svg5 libqt5x11extras5 libresid-builder0c2a libsdl-image1.2
libsdl1.2debian libsecret-1-0 libsecret-common libshout3 libsidplay2
libsmbclient libspeexdsp1 libtag1v5 libtag1v5-vanilla libtalloc2 libtdb1
libtevent0 libupnp6 libusageenvironment3 libva-wayland2 libvisual-0.4-0
libvlc-bin libvlc5 libvlccore9 libvulkan1 libwbclient0 libxcb-xv0
mesa-vulkan-drivers notification-daemon python-talloc samba-libs vlc vlc-bin
vlc-data vlc-l10n vlc-plugin-base vlc-plugin-notify vlc-plugin-qt
vlc-plugin-samba vlc-plugin-skins2 vlc-plugin-video-output
vlc-plugin-video-splitter vlc-plugin-visualization
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Get:56 http://archive.ubuntu.com/ubuntu bionic/main amd64 libshout3 amd64 2.4.1-2build1 [43.1 kB]
Get:57 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libsidplay2 amd64 2.1.1-15ubuntu1 [87.8 kB]
Get:58 http://archive.ubuntu.com/ubuntu bionic/main amd64 libspeexdsp1 amd64 1.2~rc1.2-1ubuntu2 [40.3 kB]
Get:59 http://archive.ubuntu.com/ubuntu bionic/main amd64 libtag1v5-vanilla amd64 1.11.1+dfsg.1-0.2build2 [265 kB]
Get:60 http://archive.ubuntu.com/ubuntu bionic/main amd64 libtag1v5 amd64 1.11.1+dfsg.1-0.2build2 [10.9 kB]
Get:61 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libusageenvironment3 amd64 2018.02.18-1 [6,944 B]
Get:62 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libva-wayland2 amd64 2.1.0-3 [8,756 B]
Get:63 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 libvlccore9 amd64 3.0.8-0ubuntu18.04.1 [434 kB]
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Get:67 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 mesa-vulkan-drivers amd64 20.0.8-0ubuntu1~18.04.1 [3,059 kB]
Get:68 http://archive.ubuntu.com/ubuntu bionic/main amd64 notification-daemon amd64 3.20.0-3 [36.9 kB]
Get:69 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 vlc-bin amd64 3.0.8-0ubuntu18.04.1 [53.3 kB]
Get:70 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 vlc-data all 3.0.8-0ubuntu18.04.1 [339 kB]
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Get:73 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libupnp6 amd64 1:1.6.24-4 [122 kB]
Get:74 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 vlc-plugin-base amd64 3.0.8-0ubuntu18.04.1 [2,900 kB]
Get:75 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 vlc-plugin-qt amd64 3.0.8-0ubuntu18.04.1 [1,049 kB]
Get:76 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 vlc-plugin-video-output amd64 3.0.8-0ubuntu18.04.1 [163 kB]
Get:77 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 vlc amd64 3.0.8-0ubuntu18.04.1 [33.7 kB]
Get:78 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 vlc-l10n all 3.0.8-0ubuntu18.04.1 [5,739 kB]
Get:79 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 vlc-plugin-notify amd64 3.0.8-0ubuntu18.04.1 [18.3 kB]
Get:80 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 vlc-plugin-samba amd64 3.0.8-0ubuntu18.04.1 [18.9 kB]
Get:81 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 vlc-plugin-skins2 amd64 3.0.8-0ubuntu18.04.1 [468 kB]
Get:82 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 vlc-plugin-video-splitter amd64 3.0.8-0ubuntu18.04.1 [31.3 kB]
Get:83 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 vlc-plugin-visualization amd64 3.0.8-0ubuntu18.04.1 [39.6 kB]
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Setting up libqt5x11extras5:amd64 (5.9.5-0ubuntu1) ...
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Setting up vlc-data (3.0.8-0ubuntu18.04.1) ...
Setting up libdvbpsi10:amd64 (1.3.2-1) ...
Setting up libmpcdec6:amd64 (2:0.1~r495-1) ...
Setting up libgpm2:amd64 (1.20.7-5) ...
Setting up libaribb24-0:amd64 (1.0.3-1) ...
Setting up libcap2-bin (1:2.25-1.2) ...
Setting up libbasicusageenvironment1:amd64 (2018.02.18-1) ...
Setting up libproxy-tools (0.4.15-1ubuntu0.2) ...
Setting up libmicrodns0:amd64 (0.0.8-1) ...
Setting up libebml4v5:amd64 (1.3.5-2ubuntu0.1) ...
Setting up libva-wayland2:amd64 (2.1.0-3) ...
Setting up libfaad2:amd64 (2.8.8-1) ...
Setting up libkate1:amd64 (0.4.1-7build1) ...
Setting up liblirc-client0:amd64 (0.10.0-2) ...
Setting up libsdl1.2debian:amd64 (1.2.15+dfsg2-0.1ubuntu0.1) ...
Setting up libcdparanoia0:amd64 (3.10.2+debian-13) ...
Setting up vlc-l10n (3.0.8-0ubuntu18.04.1) ...
Setting up libnfs11:amd64 (2.0.0-1~exp1) ...
Setting up libmtp-common (1.1.13-1) ...
Setting up liba52-0.7.4:amd64 (0.7.4-19) ...
Setting up libaa1:amd64 (1.4p5-44build2) ...
Setting up libupnp6:amd64 (1:1.6.24-4) ...
Setting up libvlccore9:amd64 (3.0.8-0ubuntu18.04.1) ...
Setting up libxcb-xv0:amd64 (1.13-2~ubuntu18.04) ...
Setting up libqt5svg5:amd64 (5.9.5-0ubuntu1) ...
Setting up vlc-plugin-video-splitter:amd64 (3.0.8-0ubuntu18.04.1) ...
Setting up libcddb2 (1.3.2-5fakesync1) ...
Setting up libmtp9:amd64 (1.1.13-1) ...
Setting up libvisual-0.4-0:amd64 (0.4.0-11) ...
Setting up libgroupsock8:amd64 (2018.02.18-1) ...
Setting up vlc-plugin-visualization:amd64 (3.0.8-0ubuntu18.04.1) ...
Setting up libtalloc2:amd64 (2.1.10-2ubuntu1) ...
Setting up liblua5.2-0:amd64 (5.2.4-1.1build1) ...
Setting up liborc-0.4-0:amd64 (1:0.4.28-1) ...
Setting up libsdl-image1.2:amd64 (1.2.12-8ubuntu0.1) ...
Setting up libmad0:amd64 (0.15.1b-9ubuntu18.04.1) ...
Setting up vlc-plugin-notify:amd64 (3.0.8-0ubuntu18.04.1) ...
Setting up libresid-builder0c2a (2.1.1-15ubuntu1) ...
Setting up libsidplay2 (2.1.1-15ubuntu1) ...
Setting up libdca0:amd64 (0.0.5-10) ...
Setting up liblivemedia62:amd64 (2018.02.18-1) ...
Setting up fonts-freefont-ttf (20120503-7) ...
Setting up libprotobuf-lite10:amd64 (3.0.0-9.1ubuntu1) ...
Setting up libshout3:amd64 (2.4.1-2build1) ...
Setting up libsecret-1-0:amd64 (0.18.6-1) ...
Setting up libopenmpt-modplug1:amd64 (0.3.6-1) ...
Setting up libspeexdsp1:amd64 (1.2~rc1.2-1ubuntu2) ...
Setting up libtag1v5-vanilla:amd64 (1.11.1+dfsg.1-0.2build2) ...
Setting up notification-daemon (3.20.0-3) ...
Setting up libdvdread4:amd64 (6.0.0-1) ...
Setting up python-talloc (2.1.10-2ubuntu1) ...
Setting up libmatroska6v5:amd64 (1.4.8-1.1) ...
Setting up libvlc5:amd64 (3.0.8-0ubuntu18.04.1) ...
Setting up libtag1v5:amd64 (1.11.1+dfsg.1-0.2build2) ...
Setting up libgstreamer1.0-0:amd64 (1.14.5-0ubuntu1~18.04.2) ...
Setcap worked! gst-ptp-helper is not suid!
Setting up libdvdnav4:amd64 (6.0.0-1) ...
Setting up vlc-plugin-qt:amd64 (3.0.8-0ubuntu18.04.1) ...
Setting up libmtp-runtime (1.1.13-1) ...
Setting up libtevent0:amd64 (0.9.34-1) ...
Setting up vlc-plugin-video-output:amd64 (3.0.8-0ubuntu18.04.1) ...
Setting up libvlc-bin:amd64 (3.0.8-0ubuntu18.04.1) ...
Setting up vlc-plugin-skins2:amd64 (3.0.8-0ubuntu18.04.1) ...
Setting up vlc-plugin-base:amd64 (3.0.8-0ubuntu18.04.1) ...
Setting up vlc-bin (3.0.8-0ubuntu18.04.1) ...
Setting up libgstreamer-plugins-base1.0-0:amd64 (1.14.5-0ubuntu1~18.04.3) ...
Setting up gstreamer1.0-plugins-base:amd64 (1.14.5-0ubuntu1~18.04.3) ...
Setting up gstreamer1.0-libav:amd64 (1.14.5-0ubuntu1~18.04.1) ...
Setting up libldb1:amd64 (2:1.2.3-1ubuntu0.2) ...
Setting up vlc (3.0.8-0ubuntu18.04.1) ...
Setting up samba-libs:amd64 (2:4.7.6+dfsg~ubuntu-0ubuntu2.23) ...
Setting up libsmbclient:amd64 (2:4.7.6+dfsg~ubuntu-0ubuntu2.23) ...
Setting up vlc-plugin-samba:amd64 (3.0.8-0ubuntu18.04.1) ...
Processing triggers for udev (237-3ubuntu10.48) ...
Processing triggers for man-db (2.8.3-2ubuntu0.1) ...
Processing triggers for hicolor-icon-theme (0.17-2) ...
Processing triggers for fontconfig (2.12.6-0ubuntu2) ...
Processing triggers for mime-support (3.60ubuntu1) ...
Processing triggers for libc-bin (2.27-3ubuntu1.2) ...
/sbin/ldconfig.real: /usr/local/lib/python3.7/dist-packages/ideep4py/lib/libmkldnn.so.0 is not a symbolic link
Processing triggers for libvlc-bin:amd64 (3.0.8-0ubuntu18.04.1) ...
Reading package lists... Done
Building dependency tree
Reading state information... Done
Note, selecting 'git' instead of 'git-core'
build-essential is already the newest version (12.4ubuntu1).
libvorbis-dev is already the newest version (1.3.5-4.2).
libvorbis-dev set to manually installed.
pkg-config is already the newest version (0.29.1-0ubuntu2).
zlib1g-dev is already the newest version (1:1.2.11.dfsg-0ubuntu2).
zlib1g-dev set to manually installed.
git is already the newest version (1:2.17.1-1ubuntu0.8).
libfreetype6-dev is already the newest version (2.8.1-2ubuntu2.1).
libxcb-xfixes0-dev is already the newest version (1.13-2~ubuntu18.04).
libxcb-xfixes0-dev set to manually installed.
libxcb1-dev is already the newest version (1.13-2~ubuntu18.04).
libxcb1-dev set to manually installed.
wget is already the newest version (1.19.4-1ubuntu2.2).
The following additional packages will be installed:
autotools-dev cmake-data file gir1.2-ibus-1.0 libauthen-sasl-perl
libcapnp-0.6.1 libdata-dump-perl libdbus-1-dev libencode-locale-perl
libfile-listing-perl libfont-afm-perl libfribidi-dev libgmp-dev
libgmpxx4ldbl libgnutls-dane0 libgnutls-openssl27 libgnutlsxx28 libhogweed4
libhtml-form-perl libhtml-format-perl libhtml-parser-perl
libhtml-tagset-perl libhtml-tree-perl libhttp-cookies-perl
libhttp-daemon-perl libhttp-date-perl libhttp-message-perl
libhttp-negotiate-perl libibus-1.0-5 libibus-1.0-dev libidn2-0-dev
libidn2-dev libio-html-perl libio-socket-ssl-perl liblwp-mediatypes-perl
liblwp-protocol-https-perl libmagic-mgc libmagic1 libmailtools-perl
libmirclient-dev libmirclient9 libmircommon-dev libmircommon7
libmircookie-dev libmircookie2 libmircore-dev libmircore1 libmirprotobuf3
libnet-http-perl libnet-smtp-ssl-perl libnet-ssleay-perl libnettle6
libp11-kit-dev libp11-kit0 libprotobuf-dev libpulse-dev
libpulse-mainloop-glib0 libset-scalar-perl libsigsegv2 libsndio-dev
libtasn1-6-dev libtasn1-doc libtext-unidecode-perl libtimedate-perl
libtry-tiny-perl libudev-dev libudev1 libunbound2 liburi-perl libva-glx2
libwww-perl libwww-robotrules-perl libxcursor-dev libxinerama-dev
libxkbcommon-dev libxml-libxml-perl libxml-namespacesupport-perl
libxml-parser-perl libxml-sax-base-perl libxml-sax-expat-perl
libxml-sax-perl libxrandr-dev libxv-dev m4 netbase nettle-dev
perl-openssl-defaults tex-common udev x11proto-randr-dev
x11proto-xinerama-dev
Suggested packages:
autoconf-archive gnu-standards autoconf-doc gettext cmake-doc
libdigest-hmac-perl libgssapi-perl gmp-doc libgmp10-doc libmpfr-dev
dns-root-data gnutls-doc gnutls-bin libcrypt-ssleay-perl libtool-doc gcj-jdk
libvdpau-doc libauthen-ntlm-perl m4-doc debhelper texlive-base
texlive-latex-base texlive-generic-recommended texinfo-doc-nonfree
texlive-fonts-recommended
The following NEW packages will be installed:
autoconf automake autotools-dev file gir1.2-ibus-1.0 libass-dev
libauthen-sasl-perl libcapnp-0.6.1 libdata-dump-perl libdbus-1-dev
libencode-locale-perl libfile-listing-perl libfont-afm-perl libfribidi-dev
libgmp-dev libgmpxx4ldbl libgnutls-dane0 libgnutls-openssl27 libgnutls28-dev
libgnutlsxx28 libhtml-form-perl libhtml-format-perl libhtml-parser-perl
libhtml-tagset-perl libhtml-tree-perl libhttp-cookies-perl
libhttp-daemon-perl libhttp-date-perl libhttp-message-perl
libhttp-negotiate-perl libibus-1.0-5 libibus-1.0-dev libidn2-0-dev
libidn2-dev libio-html-perl libio-socket-ssl-perl liblwp-mediatypes-perl
liblwp-protocol-https-perl libmagic-mgc libmagic1 libmailtools-perl
libmirclient-dev libmirclient9 libmircommon-dev libmircommon7
libmircookie-dev libmircookie2 libmircore-dev libmircore1 libmirprotobuf3
libnet-http-perl libnet-smtp-ssl-perl libnet-ssleay-perl libp11-kit-dev
libprotobuf-dev libpulse-dev libpulse-mainloop-glib0 libsdl2-dev
libset-scalar-perl libsigsegv2 libsndio-dev libtasn1-6-dev libtasn1-doc
libtext-unidecode-perl libtimedate-perl libtool libtry-tiny-perl libudev-dev
libunbound2 liburi-perl libva-dev libva-glx2 libvdpau-dev libwww-perl
libwww-robotrules-perl libxcb-shm0-dev libxcursor-dev libxinerama-dev
libxkbcommon-dev libxml-libxml-perl libxml-namespacesupport-perl
libxml-parser-perl libxml-sax-base-perl libxml-sax-expat-perl
libxml-sax-perl libxrandr-dev libxv-dev m4 meson netbase nettle-dev
ninja-build perl-openssl-defaults tex-common texinfo x11proto-randr-dev
x11proto-xinerama-dev yasm
The following packages will be upgraded:
cmake cmake-data libhogweed4 libnettle6 libp11-kit0 libudev1 udev
7 upgraded, 98 newly installed, 0 to remove and 73 not upgraded.
Need to get 18.2 MB of archives.
After this operation, 65.4 MB of additional disk space will be used.
Get:1 http://ppa.launchpad.net/graphics-drivers/ppa/ubuntu bionic/main amd64 libvdpau-dev amd64 1.3-0ubuntu0~gpu18.04.2 [42.7 kB]
Get:2 http://archive.ubuntu.com/ubuntu bionic/main amd64 tex-common all 6.09 [33.0 kB]
Get:3 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 udev amd64 237-3ubuntu10.50 [1,101 kB]
Get:4 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libudev1 amd64 237-3ubuntu10.50 [56.4 kB]
Get:5 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libnettle6 amd64 3.4.1-0ubuntu0.18.04.1 [111 kB]
Get:6 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libhogweed4 amd64 3.4.1-0ubuntu0.18.04.1 [140 kB]
Get:7 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libp11-kit0 amd64 0.23.9-2ubuntu0.1 [187 kB]
Get:8 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libmagic-mgc amd64 1:5.32-2ubuntu0.4 [184 kB]
Get:9 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libmagic1 amd64 1:5.32-2ubuntu0.4 [68.6 kB]
Get:10 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 file amd64 1:5.32-2ubuntu0.4 [22.1 kB]
Get:11 http://archive.ubuntu.com/ubuntu bionic/main amd64 netbase all 5.4 [12.7 kB]
Get:12 http://archive.ubuntu.com/ubuntu bionic/main amd64 libsigsegv2 amd64 2.12-1 [14.7 kB]
Get:13 http://archive.ubuntu.com/ubuntu bionic/main amd64 m4 amd64 1.4.18-1 [197 kB]
Get:14 http://archive.ubuntu.com/ubuntu bionic/main amd64 autoconf all 2.69-11 [322 kB]
Get:15 http://archive.ubuntu.com/ubuntu bionic/main amd64 autotools-dev all 20180224.1 [39.6 kB]
Get:16 http://archive.ubuntu.com/ubuntu bionic/main amd64 automake all 1:1.15.1-3ubuntu2 [509 kB]
Get:17 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 cmake amd64 3.10.2-1ubuntu2.18.04.2 [3,152 kB]
Get:18 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 cmake-data all 3.10.2-1ubuntu2.18.04.2 [1,332 kB]
Get:19 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libibus-1.0-5 amd64 1.5.17-3ubuntu5.3 [133 kB]
Get:20 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 gir1.2-ibus-1.0 amd64 1.5.17-3ubuntu5.3 [66.5 kB]
Get:21 http://archive.ubuntu.com/ubuntu bionic/main amd64 libcapnp-0.6.1 amd64 0.6.1-1ubuntu1 [658 kB]
Get:22 http://archive.ubuntu.com/ubuntu bionic/main amd64 libdata-dump-perl all 1.23-1 [27.0 kB]
Get:23 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libdbus-1-dev amd64 1.12.2-1ubuntu1.2 [165 kB]
Get:24 http://archive.ubuntu.com/ubuntu bionic/main amd64 libencode-locale-perl all 1.05-1 [12.3 kB]
Get:25 http://archive.ubuntu.com/ubuntu bionic/main amd64 libtimedate-perl all 2.3000-2 [37.5 kB]
Get:26 http://archive.ubuntu.com/ubuntu bionic/main amd64 libhttp-date-perl all 6.02-1 [10.4 kB]
Get:27 http://archive.ubuntu.com/ubuntu bionic/main amd64 libfile-listing-perl all 6.04-1 [9,774 B]
Get:28 http://archive.ubuntu.com/ubuntu bionic/main amd64 libfont-afm-perl all 1.20-2 [13.2 kB]
Get:29 http://archive.ubuntu.com/ubuntu bionic/main amd64 libfribidi-dev amd64 0.19.7-2 [42.5 kB]
Get:30 http://archive.ubuntu.com/ubuntu bionic/main amd64 libgmpxx4ldbl amd64 2:6.1.2+dfsg-2 [8,964 B]
Get:31 http://archive.ubuntu.com/ubuntu bionic/main amd64 libgmp-dev amd64 2:6.1.2+dfsg-2 [316 kB]
Get:32 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libgnutls-openssl27 amd64 3.5.18-1ubuntu1.4 [21.1 kB]
Get:33 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libgnutlsxx28 amd64 3.5.18-1ubuntu1.4 [13.7 kB]
Get:34 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libunbound2 amd64 1.6.7-1ubuntu2.4 [267 kB]
Get:35 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libgnutls-dane0 amd64 3.5.18-1ubuntu1.4 [21.0 kB]
Get:36 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 nettle-dev amd64 3.4.1-0ubuntu0.18.04.1 [954 kB]
Get:37 http://archive.ubuntu.com/ubuntu bionic/main amd64 libtasn1-6-dev amd64 4.13-2 [86.8 kB]
Get:38 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libp11-kit-dev amd64 0.23.9-2ubuntu0.1 [60.9 kB]
Get:39 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libidn2-dev amd64 2.0.4-1.1ubuntu0.2 [61.8 kB]
Get:40 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libidn2-0-dev amd64 2.0.4-1.1ubuntu0.2 [2,848 B]
Get:41 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libgnutls28-dev amd64 3.5.18-1ubuntu1.4 [660 kB]
Get:42 http://archive.ubuntu.com/ubuntu bionic/main amd64 libhtml-tagset-perl all 3.20-3 [12.1 kB]
Get:43 http://archive.ubuntu.com/ubuntu bionic/main amd64 liburi-perl all 1.73-1 [77.2 kB]
Get:44 http://archive.ubuntu.com/ubuntu bionic/main amd64 libhtml-parser-perl amd64 3.72-3build1 [85.9 kB]
Get:45 http://archive.ubuntu.com/ubuntu bionic/main amd64 libio-html-perl all 1.001-1 [14.9 kB]
Get:46 http://archive.ubuntu.com/ubuntu bionic/main amd64 liblwp-mediatypes-perl all 6.02-1 [21.7 kB]
Get:47 http://archive.ubuntu.com/ubuntu bionic/main amd64 libhttp-message-perl all 6.14-1 [72.1 kB]
Get:48 http://archive.ubuntu.com/ubuntu bionic/main amd64 libhtml-form-perl all 6.03-1 [23.5 kB]
Get:49 http://archive.ubuntu.com/ubuntu bionic/main amd64 libhtml-tree-perl all 5.07-1 [200 kB]
Get:50 http://archive.ubuntu.com/ubuntu bionic/main amd64 libhtml-format-perl all 2.12-1 [41.3 kB]
Get:51 http://archive.ubuntu.com/ubuntu bionic/main amd64 libhttp-cookies-perl all 6.04-1 [17.2 kB]
Get:52 http://archive.ubuntu.com/ubuntu bionic/main amd64 libhttp-daemon-perl all 6.01-1 [17.0 kB]
Get:53 http://archive.ubuntu.com/ubuntu bionic/main amd64 libhttp-negotiate-perl all 6.00-2 [13.4 kB]
Get:54 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libibus-1.0-dev amd64 1.5.17-3ubuntu5.3 [145 kB]
Get:55 http://archive.ubuntu.com/ubuntu bionic/main amd64 perl-openssl-defaults amd64 3build1 [7,012 B]
Get:56 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libnet-ssleay-perl amd64 1.84-1ubuntu0.2 [283 kB]
Get:57 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libio-socket-ssl-perl all 2.060-3~ubuntu18.04.1 [173 kB]
Get:58 http://archive.ubuntu.com/ubuntu bionic/main amd64 libnet-http-perl all 6.17-1 [22.7 kB]
Get:59 http://archive.ubuntu.com/ubuntu bionic/main amd64 libtry-tiny-perl all 0.30-1 [20.5 kB]
Get:60 http://archive.ubuntu.com/ubuntu bionic/main amd64 libwww-robotrules-perl all 6.01-1 [14.1 kB]
Get:61 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libwww-perl all 6.31-1ubuntu0.1 [137 kB]
Get:62 http://archive.ubuntu.com/ubuntu bionic/main amd64 liblwp-protocol-https-perl all 6.07-2 [8,284 B]
Get:63 http://archive.ubuntu.com/ubuntu bionic/main amd64 libnet-smtp-ssl-perl all 1.04-1 [5,948 B]
Get:64 http://archive.ubuntu.com/ubuntu bionic/main amd64 libmailtools-perl all 2.18-1 [74.0 kB]
Get:65 http://archive.ubuntu.com/ubuntu bionic/main amd64 libmircore1 amd64 0.31.1-0ubuntu1 [26.5 kB]
Get:66 http://archive.ubuntu.com/ubuntu bionic/main amd64 libmircommon7 amd64 0.31.1-0ubuntu1 [73.9 kB]
Get:67 http://archive.ubuntu.com/ubuntu bionic/main amd64 libmirprotobuf3 amd64 0.31.1-0ubuntu1 [127 kB]
Get:68 http://archive.ubuntu.com/ubuntu bionic/main amd64 libmirclient9 amd64 0.31.1-0ubuntu1 [199 kB]
Get:69 http://archive.ubuntu.com/ubuntu bionic/main amd64 libmircore-dev amd64 0.31.1-0ubuntu1 [21.7 kB]
Get:70 http://archive.ubuntu.com/ubuntu bionic/main amd64 libprotobuf-dev amd64 3.0.0-9.1ubuntu1 [959 kB]
Get:71 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libxkbcommon-dev amd64 0.8.2-1~ubuntu18.04.1 [150 kB]
Get:72 http://archive.ubuntu.com/ubuntu bionic/main amd64 libmircommon-dev amd64 0.31.1-0ubuntu1 [13.9 kB]
Get:73 http://archive.ubuntu.com/ubuntu bionic/main amd64 libmircookie2 amd64 0.31.1-0ubuntu1 [19.7 kB]
Get:74 http://archive.ubuntu.com/ubuntu bionic/main amd64 libmircookie-dev amd64 0.31.1-0ubuntu1 [4,392 B]
Get:75 http://archive.ubuntu.com/ubuntu bionic/main amd64 libmirclient-dev amd64 0.31.1-0ubuntu1 [47.8 kB]
Get:76 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libpulse-mainloop-glib0 amd64 1:11.1-1ubuntu7.11 [22.1 kB]
Get:77 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libpulse-dev amd64 1:11.1-1ubuntu7.11 [81.5 kB]
Get:78 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libsndio-dev amd64 1.1.0-3 [13.3 kB]
Get:79 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libudev-dev amd64 237-3ubuntu10.50 [19.1 kB]
Get:80 http://archive.ubuntu.com/ubuntu bionic/main amd64 libxcursor-dev amd64 1:1.1.15-1 [26.5 kB]
Get:81 http://archive.ubuntu.com/ubuntu bionic/main amd64 x11proto-xinerama-dev all 2018.4-4 [2,628 B]
Get:82 http://archive.ubuntu.com/ubuntu bionic/main amd64 libxinerama-dev amd64 2:1.1.3-1 [8,404 B]
Get:83 http://archive.ubuntu.com/ubuntu bionic/main amd64 x11proto-randr-dev all 2018.4-4 [2,620 B]
Get:84 http://archive.ubuntu.com/ubuntu bionic/main amd64 libxrandr-dev amd64 2:1.5.1-1 [24.0 kB]
Get:85 http://archive.ubuntu.com/ubuntu bionic/main amd64 libxv-dev amd64 2:1.0.11-1 [32.5 kB]
Get:86 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 libsdl2-dev amd64 2.0.8+dfsg1-1ubuntu1.18.04.4 [683 kB]
Get:87 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libtext-unidecode-perl all 1.30-1 [99.0 kB]
Get:88 http://archive.ubuntu.com/ubuntu bionic/main amd64 libtool all 2.4.6-2 [194 kB]
Get:89 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libva-glx2 amd64 2.1.0-3 [10.6 kB]
Get:90 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libset-scalar-perl all 1.29-2 [27.9 kB]
Get:91 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libva-dev amd64 2.1.0-3 [84.3 kB]
Get:92 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libxcb-shm0-dev amd64 1.13-2~ubuntu18.04 [6,684 B]
Get:93 http://archive.ubuntu.com/ubuntu bionic/main amd64 libxml-namespacesupport-perl all 1.12-1 [13.2 kB]
Get:94 http://archive.ubuntu.com/ubuntu bionic/main amd64 libxml-sax-base-perl all 1.09-1 [18.8 kB]
Get:95 http://archive.ubuntu.com/ubuntu bionic/main amd64 libxml-sax-perl all 0.99+dfsg-2ubuntu1 [64.6 kB]
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Get:98 http://archive.ubuntu.com/ubuntu bionic/main amd64 libxml-sax-expat-perl all 0.40-2 [11.5 kB]
Get:99 http://archive.ubuntu.com/ubuntu bionic/universe amd64 ninja-build amd64 1.8.2-1 [93.3 kB]
Get:100 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 meson all 0.45.1-2ubuntu0.18.04.2 [216 kB]
Get:101 http://archive.ubuntu.com/ubuntu bionic/universe amd64 texinfo amd64 6.5.0.dfsg.1-2 [752 kB]
Get:102 http://archive.ubuntu.com/ubuntu bionic/universe amd64 yasm amd64 1.3.0-2build1 [406 kB]
Get:103 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libass-dev amd64 1:0.14.0-1 [106 kB]
Get:104 http://archive.ubuntu.com/ubuntu bionic/main amd64 libauthen-sasl-perl all 2.1600-1 [48.7 kB]
Get:105 http://archive.ubuntu.com/ubuntu bionic/main amd64 libtasn1-doc all 4.13-2 [300 kB]
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Define preprocessing code 💻¶
The code that is common between the training and the prediction sections should be defined here. During evaluation, we completely skip the training section. Please make sure to add any common logic between the training and prediction sections here.
In [ ]:
# Pytorch
import torch
from torch import nn
import segmentation_models_pytorch as smp
import argus
from torch.utils.data import Dataset, DataLoader
# Reading Dataset, vis and miscellaneous
from PIL import Image
import matplotlib.pyplot as plt
import os
import numpy as np
from tqdm.notebook import tqdm
import cv2
from natsort import natsorted
from glob import glob
import skvideo.io
Training phase ⚙️¶
You can define your training code here. This sections will be skipped during evaluation.
Downloading Dataset¶
Hre we are downloading the challange dataset using AIcrowd CLI
In [ ]:
%aicrowd login
Please login here: https://api.aicrowd.com/auth/QWEy5fOLpYgbEaM8eJXjcxrY9C-elZjxZxtzmYpdVeg API Key valid Saved API Key successfully!
In [ ]:
!rm -rf data
!mkdir data
%aicrowd ds dl -c iceberg-detection -o data
In [ ]:
# Unzipping the files
!unzip data/train.zip -d data/train > /dev/null
!unzip data/test.zip -d data/test > /dev/null
Dataloader¶
In [ ]:
class IcebergsSegmentationDataset(Dataset):
def __init__(self, data_directory=None):
self.data_directory = data_directory
# If the image direcotry is valid
if data_directory != None:
self.image_videos_files = natsorted(glob(data_directory+"/image*"))
self.segmentation_videos_files = natsorted(glob(data_directory+"/segmentation*"))
self.img_list = list(zip(self.image_videos_files, self.segmentation_videos_files))
def __len__(self):
return len(self.img_list)
def __getitem__(self, idx):
# Reading The oceberg video
img_video = cv2.VideoCapture(self.img_list[idx][0])
img_video.set(1, 15)
ret, img = img_video.read()
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Reading the segmentation video
seg_video = cv2.VideoCapture(self.img_list[idx][1])
seg_video.set(1, 15)
ret, mask = seg_video.read()
img = np.array(img, dtype=np.float32)
# Resizing the images
img = cv2.resize(img, (128, 128))
mask = cv2.resize(mask, (128, 128))
img = np.expand_dims(img, axis=(0))
mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)
_, mask = cv2.threshold(mask,127,255,cv2.THRESH_BINARY)
return img, mask.astype(np.float32)
In [ ]:
# Creating the training dataset
train_dataset = IcebergsSegmentationDataset(data_directory="data/train")
train_loader = DataLoader(train_dataset, batch_size=23, num_workers=1, shuffle=False, drop_last=True)
In [ ]:
# Reading the image and corrosponding segmentation
image_batch, segmentation_batch = next(iter(train_loader))
image_batch.shape, segmentation_batch.shape
Out[ ]:
(torch.Size([23, 1, 128, 128]), torch.Size([23, 128, 128]))
Visualizing Dataset¶
In [ ]:
plt.rcParams["figure.figsize"] = (30,5)
# Going through each image and segmentation
for image, segmentation in zip(image_batch, segmentation_batch):
image = image.numpy()[0]/255
# Showing the image
plt.figure()
plt.subplot(1,2,1)
plt.imshow(image, 'gray', interpolation='none')
plt.subplot(1,2,2)
plt.imshow(image, 'gray', interpolation='none')
plt.imshow(segmentation, 'jet', interpolation='none', alpha=0.7)
plt.show()
Creating the Model¶
Here we will get setting up the model architecture, optimizer and loss.
In [ ]:
class TressSegmentationModel(argus.Model):
nn_module = smp.Unet
optimizer = torch.optim.Adam
loss = nn.MSELoss
In [ ]:
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = TressSegmentationModel({ 'device': device,
'nn_module': {
'encoder_name': 'resnet18',
'classes': 1,
'in_channels': 1,
}})
model
Downloading: "https://download.pytorch.org/models/resnet18-5c106cde.pth" to /root/.cache/torch/hub/checkpoints/resnet18-5c106cde.pth
Out[ ]:
{'params': {'device': 'cuda', 'nn_module': {'encoder_name': 'resnet18', 'classes': 1, 'in_channels': 1}}, 'logger': <Logger argus.model.build_140186540000848 (INFO)>, 'nn_module': Unet(
(encoder): ResNetEncoder(
(conv1): Conv2d(1, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
(layer1): Sequential(
(0): BasicBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(1): BasicBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(layer2): Sequential(
(0): BasicBlock(
(conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(layer3): Sequential(
(0): BasicBlock(
(conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(layer4): Sequential(
(0): BasicBlock(
(conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
(decoder): UnetDecoder(
(center): Identity()
(blocks): ModuleList(
(0): DecoderBlock(
(conv1): Conv2dReLU(
(0): Conv2d(768, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(attention1): Attention(
(attention): Identity()
)
(conv2): Conv2dReLU(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(attention2): Attention(
(attention): Identity()
)
)
(1): DecoderBlock(
(conv1): Conv2dReLU(
(0): Conv2d(384, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(attention1): Attention(
(attention): Identity()
)
(conv2): Conv2dReLU(
(0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(attention2): Attention(
(attention): Identity()
)
)
(2): DecoderBlock(
(conv1): Conv2dReLU(
(0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(attention1): Attention(
(attention): Identity()
)
(conv2): Conv2dReLU(
(0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(attention2): Attention(
(attention): Identity()
)
)
(3): DecoderBlock(
(conv1): Conv2dReLU(
(0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(attention1): Attention(
(attention): Identity()
)
(conv2): Conv2dReLU(
(0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(attention2): Attention(
(attention): Identity()
)
)
(4): DecoderBlock(
(conv1): Conv2dReLU(
(0): Conv2d(32, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(attention1): Attention(
(attention): Identity()
)
(conv2): Conv2dReLU(
(0): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(attention2): Attention(
(attention): Identity()
)
)
)
)
(segmentation_head): SegmentationHead(
(0): Conv2d(16, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): Identity()
(2): Activation(
(activation): Identity()
)
)
), 'optimizer': Adam (
Parameter Group 0
amsgrad: False
betas: (0.9, 0.999)
eps: 1e-08
lr: 0.001
weight_decay: 0
), 'loss': MSELoss(), 'device': device(type='cuda'), 'prediction_transform': Identity()}
Training the Model¶
In [ ]:
model.fit(train_loader,
num_epochs=5,
metrics=['accuracy'],
metrics_on_train=True)
/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.) return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode) /usr/local/lib/python3.7/dist-packages/torch/nn/modules/loss.py:528: UserWarning: Using a target size (torch.Size([23, 128, 128])) that is different to the input size (torch.Size([23, 1, 128, 128])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size. return F.mse_loss(input, target, reduction=self.reduction)
[2021-07-24 07:18:29,386][INFO]: train - epoch: 0, lr: 0.001, train_loss: 9501.746, train_accuracy: 0.8479941
INFO train - epoch: 0, lr: 0.001, train_loss: 9501.746, train_accuracy: 0.8479941
[2021-07-24 07:19:21,454][INFO]: train - epoch: 1, lr: 0.001, train_loss: 8682.995, train_accuracy: 0.8479941
INFO train - epoch: 1, lr: 0.001, train_loss: 8682.995, train_accuracy: 0.8479941
[2021-07-24 07:20:14,207][INFO]: train - epoch: 2, lr: 0.001, train_loss: 7922.031, train_accuracy: 0.8479941
INFO train - epoch: 2, lr: 0.001, train_loss: 7922.031, train_accuracy: 0.8479941
[2021-07-24 07:21:06,596][INFO]: train - epoch: 3, lr: 0.001, train_loss: 7640.658, train_accuracy: 0.8479941
INFO train - epoch: 3, lr: 0.001, train_loss: 7640.658, train_accuracy: 0.8479941
[2021-07-24 07:21:58,710][INFO]: train - epoch: 4, lr: 0.001, train_loss: 7566.912, train_accuracy: 0.8479941
INFO train - epoch: 4, lr: 0.001, train_loss: 7566.912, train_accuracy: 0.8479941
Visualize Predictions¶
In [ ]:
predictions = model.predict(image_batch)
predictions.shape
Out[ ]:
torch.Size([23, 1, 128, 128])
In [ ]:
# Going through each image and segmentation
for image, segmentation in zip(image_batch, predictions.cpu()):
image = image.numpy()[0]/255
# Showing the image
plt.figure()
plt.subplot(1,2,1)
plt.imshow(image, 'gray', interpolation='none')
plt.subplot(1,2,2)
plt.imshow(image, 'gray', interpolation='none')
plt.imshow(segmentation[0], 'jet', interpolation='none', alpha=0.7)
plt.show()
Saving the Model¶
In [ ]:
!mkdir assets
torch.save(model, os.path.join("assets", "model.h5"))
Prediction phase 🔎¶
Generating the features in test dataset.
Loading the Model¶
In [ ]:
device = torch.device("cpu")
In [ ]:
class TressSegmentationModel(argus.Model):
nn_module = smp.Unet
optimizer = torch.optim.Adam
loss = nn.MSELoss
/usr/local/lib/python3.7/dist-packages/argus/model/build.py:96: UserWarning: <class '__main__.TressSegmentationModel'> redefined 'TressSegmentationModel' that was already registered by <class '__main__.TressSegmentationModel'>
warnings.warn(f"{current_class} redefined '{name}' "
In [ ]:
model = torch.load(os.path.join("assets", "model.h5"), map_location=torch.device('cpu'))
model
Out[ ]:
{'params': {'device': 'cuda', 'nn_module': {'encoder_name': 'resnet18', 'classes': 1, 'in_channels': 1}}, 'logger': <Logger argus.model.build_140186540000848 (INFO)>, 'nn_module': Unet(
(encoder): ResNetEncoder(
(conv1): Conv2d(1, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
(layer1): Sequential(
(0): BasicBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(1): BasicBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(layer2): Sequential(
(0): BasicBlock(
(conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(layer3): Sequential(
(0): BasicBlock(
(conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(layer4): Sequential(
(0): BasicBlock(
(conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
(decoder): UnetDecoder(
(center): Identity()
(blocks): ModuleList(
(0): DecoderBlock(
(conv1): Conv2dReLU(
(0): Conv2d(768, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(attention1): Attention(
(attention): Identity()
)
(conv2): Conv2dReLU(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(attention2): Attention(
(attention): Identity()
)
)
(1): DecoderBlock(
(conv1): Conv2dReLU(
(0): Conv2d(384, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(attention1): Attention(
(attention): Identity()
)
(conv2): Conv2dReLU(
(0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(attention2): Attention(
(attention): Identity()
)
)
(2): DecoderBlock(
(conv1): Conv2dReLU(
(0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(attention1): Attention(
(attention): Identity()
)
(conv2): Conv2dReLU(
(0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(attention2): Attention(
(attention): Identity()
)
)
(3): DecoderBlock(
(conv1): Conv2dReLU(
(0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(attention1): Attention(
(attention): Identity()
)
(conv2): Conv2dReLU(
(0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(attention2): Attention(
(attention): Identity()
)
)
(4): DecoderBlock(
(conv1): Conv2dReLU(
(0): Conv2d(32, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(attention1): Attention(
(attention): Identity()
)
(conv2): Conv2dReLU(
(0): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(attention2): Attention(
(attention): Identity()
)
)
)
)
(segmentation_head): SegmentationHead(
(0): Conv2d(16, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): Identity()
(2): Activation(
(activation): Identity()
)
)
), 'optimizer': Adam (
Parameter Group 0
amsgrad: False
betas: (0.9, 0.999)
eps: 1e-08
lr: 0.001
weight_decay: 0
), 'loss': MSELoss(), 'device': device(type='cuda'), 'prediction_transform': Identity()}
In [ ]:
# Test Dataloader Class
class IcebergsSegmentationTestDataset(Dataset):
def __init__(self, data_directory=None):
self.data_directory = data_directory
# If the image direcotry is valid
if data_directory != None:
self.image_videos_files = natsorted(glob(os.path.join(data_directory, "image")+"*"))
self.img_list = self.image_videos_files
def __len__(self):
# Number of test images * Number of frame
return len(self.img_list)*23
def __getitem__(self, idx):
# Reading the Video
img_video = cv2.VideoCapture(self.img_list[idx//23])
# Reading the specific frame of the video
img_video.set(1, idx%23)
ret, img = img_video.read()
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Chaning the dtype
img = np.array(img, dtype=np.float32)
# Resizing the image
img = cv2.resize(img, (128, 128))
img = np.expand_dims(img, axis=(0))
return img
In [ ]:
test_dataset = IcebergsSegmentationTestDataset(data_directory=AICROWD_DATASET_PATH)
test_loader = DataLoader(test_dataset, batch_size=23, num_workers=1, shuffle=False, drop_last=False)
# Reading the image and corrosponding segmentation
image_batch = next(iter(test_loader))
image_batch.shape
Out[ ]:
torch.Size([23, 1, 128, 128])
In [ ]:
# Generating Predictions Function
def gen_predictions(test_loader, model, AICROWD_OUTPUTS_PATH):
for batch_idx, image_batch in tqdm(enumerate(test_loader)):
model.nn_module = model.nn_module.to("cpu")
image_batch = image_batch.to("cpu")
predictions = model.nn_module.predict(image_batch)
writer = skvideo.io.FFmpegWriter(os.path.join(AICROWD_OUTPUTS_PATH, f"segmentation_{batch_idx}.mp4"), outputdict={
'-vcodec': 'libx264',
'-crf': '0',
'-preset':'veryslow'
})
for seg in predictions:
seg_np = seg[0].numpy()
# Setting a threshold to get the segmentation in some parts
_, seg_np = cv2.threshold(seg_np,50,255,cv2.THRESH_BINARY)
# Resizing the video
seg_np = cv2.resize(seg_np, (512, 512))
writer.writeFrame(seg_np.astype(np.uint8))
writer.close()
Generating the Predictions¶
In [ ]:
gen_predictions(test_loader, model, AICROWD_OUTPUTS_PATH)
Submit to AIcrowd 🚀¶¶
In [ ]:
!aicrowd -v notebook submit --assets-dir assets --challenge iceberg-detection --no-verify
Mounting Google Drive 💾 Your Google Drive will be mounted to access the colab notebook Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.activity.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fexperimentsandconfigs%20https%3a%2f%2fwww.googleapis.com%2fauth%2fphotos.native&response_type=code Enter your authorization code: 4/1AX4XfWgPQ0zS1ghOY-3UhCZCyusdasxuyJNX2o04rSeYa1PcXBhazTwFJvU Mounted at /content/drive Using notebook: /content/drive/MyDrive/Colab Notebooks/Icebergs Unet for submission... Scrubbing API keys from the notebook... Collecting notebook... Validating the submission... Executing install.ipynb... [NbConvertApp] Converting notebook /content/submission/install.ipynb to notebook [NbConvertApp] Executing notebook with kernel: python3 [NbConvertApp] ERROR | unhandled iopub msg: colab_request [NbConvertApp] ERROR | unhandled iopub msg: colab_request [NbConvertApp] ERROR | unhandled iopub msg: colab_request [NbConvertApp] ERROR | unhandled iopub msg: colab_request [NbConvertApp] ERROR | unhandled iopub msg: colab_request [NbConvertApp] ERROR | unhandled iopub msg: colab_request [NbConvertApp] ERROR | unhandled iopub msg: colab_request [NbConvertApp] ERROR | unhandled iopub msg: colab_request [NbConvertApp] ERROR | unhandled iopub msg: colab_request [NbConvertApp] ERROR | unhandled iopub msg: colab_request [NbConvertApp] ERROR | unhandled iopub msg: colab_request [NbConvertApp] ERROR | unhandled iopub msg: colab_request [NbConvertApp] ERROR | unhandled iopub msg: colab_request [NbConvertApp] ERROR | unhandled iopub msg: colab_request [NbConvertApp] ERROR | unhandled iopub msg: colab_request [NbConvertApp] ERROR | unhandled iopub msg: colab_request [NbConvertApp] ERROR | unhandled iopub msg: colab_request [NbConvertApp] ERROR | unhandled iopub msg: colab_request [NbConvertApp] ERROR | unhandled iopub msg: colab_request [NbConvertApp] ERROR | unhandled iopub msg: colab_request [NbConvertApp] ERROR | unhandled iopub msg: colab_request [NbConvertApp] ERROR | unhandled iopub msg: colab_request [NbConvertApp] ERROR | unhandled iopub msg: colab_request [NbConvertApp] ERROR | unhandled iopub msg: colab_request [NbConvertApp] ERROR | unhandled iopub msg: colab_request [NbConvertApp] ERROR | unhandled iopub msg: colab_request [NbConvertApp] ERROR | unhandled iopub msg: colab_request [NbConvertApp] ERROR | unhandled iopub msg: colab_request [NbConvertApp] ERROR | unhandled iopub msg: colab_request [NbConvertApp] ERROR | unhandled iopub msg: colab_request [NbConvertApp] ERROR | unhandled iopub msg: colab_request [NbConvertApp] ERROR | unhandled iopub msg: colab_request [NbConvertApp] ERROR | unhandled iopub msg: colab_request [NbConvertApp] Writing 52383 bytes to /content/submission/install.nbconvert.ipynb Executing predict.ipynb... [NbConvertApp] Converting notebook /content/submission/predict.ipynb to notebook [NbConvertApp] Executing notebook with kernel: python3 [NbConvertApp] ERROR | unhandled iopub msg: colab_request [NbConvertApp] ERROR | unhandled iopub msg: colab_request [NbConvertApp] ERROR | unhandled iopub msg: colab_request [NbConvertApp] ERROR | Error while converting '/content/submission/predict.ipynb' Traceback (most recent call last): File "/usr/local/lib/python2.7/dist-packages/nbconvert/nbconvertapp.py", line 408, in export_single_notebook output, resources = self.exporter.from_filename(notebook_filename, resources=resources) File "/usr/local/lib/python2.7/dist-packages/nbconvert/exporters/exporter.py", line 179, in from_filename return self.from_file(f, resources=resources, **kw) File "/usr/local/lib/python2.7/dist-packages/nbconvert/exporters/exporter.py", line 197, in from_file return self.from_notebook_node(nbformat.read(file_stream, as_version=4), resources=resources, **kw) File "/usr/local/lib/python2.7/dist-packages/nbconvert/exporters/notebook.py", line 32, in from_notebook_node nb_copy, resources = super(NotebookExporter, self).from_notebook_node(nb, resources, **kw) File "/usr/local/lib/python2.7/dist-packages/nbconvert/exporters/exporter.py", line 139, in from_notebook_node nb_copy, resources = self._preprocess(nb_copy, resources) File "/usr/local/lib/python2.7/dist-packages/nbconvert/exporters/exporter.py", line 316, in _preprocess nbc, resc = preprocessor(nbc, resc) File "/usr/local/lib/python2.7/dist-packages/nbconvert/preprocessors/base.py", line 47, in __call__ return self.preprocess(nb, resources) File "/usr/local/lib/python2.7/dist-packages/nbconvert/preprocessors/execute.py", line 381, in preprocess nb, resources = super(ExecutePreprocessor, self).preprocess(nb, resources) File "/usr/local/lib/python2.7/dist-packages/nbconvert/preprocessors/base.py", line 69, in preprocess nb.cells[index], resources = self.preprocess_cell(cell, resources, index) File "/usr/local/lib/python2.7/dist-packages/nbconvert/preprocessors/execute.py", line 426, in preprocess_cell raise CellExecutionError.from_cell_and_msg(cell, reply['content']) CellExecutionError: An error occurred while executing the following cell: ------------------ test_dataset = IcebergsSegmentationTestDataset(data_directory=AICROWD_DATASET_PATH) test_loader = DataLoader(test_dataset, batch_size=23, num_workers=1, shuffle=False, drop_last=False) # Reading the image and corrosponding segmentation image_batch = next(iter(test_loader)) image_batch.shape ------------------ StopIteration: Local Evaluation Error Error: predict.ipynb failed to execute
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