diff --git a/colabs/Errors_raised_in_HUB.ipynb b/colabs/Errors_raised_in_HUB.ipynb new file mode 100644 index 0000000..3c4b832 --- /dev/null +++ b/colabs/Errors_raised_in_HUB.ipynb @@ -0,0 +1,650 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Errors raised in HUB.ipynb", + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + }, + "accelerator": "TPU" + }, + "cells": [ + { + "cell_type": "code", + "metadata": { + "id": "CADuLZmrUYyK", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "b689b2df-bc5a-44da-9bbc-c3636038aebc" + }, + "source": [ + "!pip install hub" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting hub\n", + " Downloading hub-2.0.11-py3-none-any.whl (188 kB)\n", + "\u001b[K |████████████████████████████████| 188 kB 5.3 MB/s \n", + "\u001b[?25hCollecting humbug>=0.2.6\n", + " Downloading humbug-0.2.7-py3-none-any.whl (11 kB)\n", + "Collecting types-click\n", + " Downloading types_click-7.1.5-py3-none-any.whl (12 kB)\n", + "Collecting google-cloud-storage~=1.42.0\n", + " Downloading google_cloud_storage-1.42.3-py2.py3-none-any.whl (105 kB)\n", + "\u001b[K |████████████████████████████████| 105 kB 60.2 MB/s \n", + "\u001b[?25hCollecting lz4\n", + " Downloading lz4-3.1.3-cp37-cp37m-manylinux2010_x86_64.whl (1.8 MB)\n", + "\u001b[K |████████████████████████████████| 1.8 MB 50.1 MB/s \n", + "\u001b[?25hCollecting pillow==8.2.0\n", + " Downloading Pillow-8.2.0-cp37-cp37m-manylinux1_x86_64.whl (3.0 MB)\n", + "\u001b[K |████████████████████████████████| 3.0 MB 33.3 MB/s \n", + "\u001b[?25hCollecting typing-extensions>=3.10.0.0\n", + " Downloading typing_extensions-3.10.0.2-py3-none-any.whl (26 kB)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from hub) (1.19.5)\n", + "Collecting google-auth~=2.0.1\n", + " Downloading google_auth-2.0.2-py2.py3-none-any.whl (152 kB)\n", + "\u001b[K |████████████████████████████████| 152 kB 60.3 MB/s \n", + "\u001b[?25hRequirement already satisfied: google-auth-oauthlib~=0.4.5 in /usr/local/lib/python3.7/dist-packages (from hub) (0.4.6)\n", + "Collecting types-requests\n", + " Downloading types_requests-2.25.9-py3-none-any.whl (22 kB)\n", + "Collecting boto3-stubs[essential]\n", + " Downloading boto3_stubs-1.18.58-py3-none-any.whl (53 kB)\n", + "\u001b[K |████████████████████████████████| 53 kB 2.5 MB/s \n", + "\u001b[?25hCollecting pathos\n", + " Downloading pathos-0.2.8-py2.py3-none-any.whl (81 kB)\n", + "\u001b[K |████████████████████████████████| 81 kB 9.8 MB/s \n", + "\u001b[?25hRequirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from hub) (4.62.3)\n", + "Collecting boto3\n", + " Downloading boto3-1.18.58-py3-none-any.whl (131 kB)\n", + "\u001b[K |████████████████████████████████| 131 kB 72.4 MB/s 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google_crc32c-1.3.0-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (38 kB)\n", + "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth~=2.0.1->hub) (0.4.8)\n", + "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0dev,>=2.18.0->google-cloud-storage~=1.42.0->hub) (3.0.4)\n", + "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0dev,>=2.18.0->google-cloud-storage~=1.42.0->hub) (2021.5.30)\n", + "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0dev,>=2.18.0->google-cloud-storage~=1.42.0->hub) (1.24.3)\n", + "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0dev,>=2.18.0->google-cloud-storage~=1.42.0->hub) (2.10)\n", + "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib~=0.4.5->hub) (3.1.1)\n", + "Collecting botocore<1.22.0,>=1.21.58\n", + " Downloading botocore-1.21.58-py3-none-any.whl (8.0 MB)\n", + "\u001b[K |████████████████████████████████| 8.0 MB 21.9 MB/s \n", + "\u001b[?25hCollecting jmespath<1.0.0,>=0.7.1\n", + " Downloading jmespath-0.10.0-py2.py3-none-any.whl (24 kB)\n", + "Collecting s3transfer<0.6.0,>=0.5.0\n", + " Downloading s3transfer-0.5.0-py3-none-any.whl (79 kB)\n", + "\u001b[K |████████████████████████████████| 79 kB 8.6 MB/s \n", + "\u001b[?25hCollecting urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1\n", + " Downloading urllib3-1.25.11-py2.py3-none-any.whl (127 kB)\n", + "\u001b[K |████████████████████████████████| 127 kB 64.4 MB/s \n", + "\u001b[?25hRequirement already satisfied: python-dateutil<3.0.0,>=2.1 in /usr/local/lib/python3.7/dist-packages (from botocore<1.22.0,>=1.21.58->boto3->hub) (2.8.2)\n", + "Collecting botocore-stubs\n", + " Downloading botocore_stubs-1.21.58-py3-none-any.whl (41 kB)\n", + "\u001b[K |████████████████████████████████| 41 kB 186 kB/s \n", + "\u001b[?25hCollecting mypy-boto3-sqs>=1.18.53\n", + " Downloading mypy_boto3_sqs-1.18.58-py3-none-any.whl (28 kB)\n", + "Collecting mypy-boto3-dynamodb>=1.18.53\n", + " Downloading mypy_boto3_dynamodb-1.18.58-py3-none-any.whl (55 kB)\n", + "\u001b[K |████████████████████████████████| 55 kB 3.3 MB/s \n", + "\u001b[?25hCollecting mypy-boto3-s3>=1.18.53\n", + " Downloading mypy_boto3_s3-1.18.58-py3-none-any.whl (84 kB)\n", + "\u001b[K |████████████████████████████████| 84 kB 2.9 MB/s \n", + "\u001b[?25hCollecting mypy-boto3-lambda>=1.18.53\n", + " Downloading mypy_boto3_lambda-1.18.58-py3-none-any.whl (40 kB)\n", + "\u001b[K |████████████████████████████████| 40 kB 5.8 MB/s \n", + "\u001b[?25hCollecting mypy-boto3-rds>=1.18.53\n", + " Downloading mypy_boto3_rds-1.18.58-py3-none-any.whl (83 kB)\n", + "\u001b[K |████████████████████████████████| 83 kB 1.6 MB/s \n", + "\u001b[?25hCollecting mypy-boto3-ec2>=1.18.53\n", + " Downloading mypy_boto3_ec2-1.18.58-py3-none-any.whl (308 kB)\n", + "\u001b[K |████████████████████████████████| 308 kB 52.2 MB/s \n", + "\u001b[?25hCollecting mypy-boto3-cloudformation>=1.18.53\n", + " Downloading mypy_boto3_cloudformation-1.18.58-py3-none-any.whl (58 kB)\n", + "\u001b[K |████████████████████████████████| 58 kB 6.1 MB/s \n", + "\u001b[?25hRequirement already satisfied: multiprocess>=0.70.12 in /usr/local/lib/python3.7/dist-packages (from pathos->hub) (0.70.12.2)\n", + "Collecting ppft>=1.6.6.4\n", + " Downloading ppft-1.6.6.4-py3-none-any.whl (65 kB)\n", + "\u001b[K |████████████████████████████████| 65 kB 3.9 MB/s \n", + "\u001b[?25hCollecting pox>=0.3.0\n", + " Downloading pox-0.3.0-py2.py3-none-any.whl (30 kB)\n", + "Requirement already satisfied: dill>=0.3.4 in /usr/local/lib/python3.7/dist-packages (from pathos->hub) (0.3.4)\n", + "Installing collected packages: urllib3, typing-extensions, jmespath, google-auth, google-crc32c, google-api-core, botocore-stubs, botocore, s3transfer, ppft, pox, mypy-boto3-sqs, mypy-boto3-s3, mypy-boto3-rds, mypy-boto3-lambda, mypy-boto3-ec2, mypy-boto3-dynamodb, mypy-boto3-cloudformation, google-resumable-media, google-cloud-core, boto3-stubs, types-requests, types-click, pillow, pathos, lz4, humbug, google-cloud-storage, boto3, hub\n", + " Attempting uninstall: urllib3\n", + " Found existing installation: urllib3 1.24.3\n", + " Uninstalling urllib3-1.24.3:\n", + " Successfully uninstalled urllib3-1.24.3\n", + " Attempting uninstall: typing-extensions\n", + " Found existing installation: typing-extensions 3.7.4.3\n", + " Uninstalling typing-extensions-3.7.4.3:\n", + " Successfully uninstalled typing-extensions-3.7.4.3\n", + " Attempting uninstall: google-auth\n", + " Found existing installation: google-auth 1.35.0\n", + " Uninstalling google-auth-1.35.0:\n", + " Successfully uninstalled google-auth-1.35.0\n", + " Attempting uninstall: google-api-core\n", + " Found existing installation: google-api-core 1.26.3\n", + " Uninstalling google-api-core-1.26.3:\n", + " Successfully uninstalled google-api-core-1.26.3\n", + " Attempting uninstall: google-resumable-media\n", + " Found existing installation: google-resumable-media 0.4.1\n", + " Uninstalling google-resumable-media-0.4.1:\n", + " Successfully uninstalled google-resumable-media-0.4.1\n", + " Attempting uninstall: google-cloud-core\n", + " Found existing installation: google-cloud-core 1.0.3\n", + " Uninstalling google-cloud-core-1.0.3:\n", + " Successfully uninstalled google-cloud-core-1.0.3\n", + " Attempting uninstall: pillow\n", + " Found existing installation: Pillow 7.1.2\n", + " Uninstalling Pillow-7.1.2:\n", + " Successfully uninstalled Pillow-7.1.2\n", + " Attempting uninstall: google-cloud-storage\n", + " Found existing installation: google-cloud-storage 1.18.1\n", + " Uninstalling google-cloud-storage-1.18.1:\n", + " Successfully uninstalled google-cloud-storage-1.18.1\n", + "\u001b[31mERROR: 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.\n", + "tensorflow 2.6.0 requires typing-extensions~=3.7.4, but you have typing-extensions 3.10.0.2 which is incompatible.\n", + "tensorboard 2.6.0 requires google-auth<2,>=1.6.3, but you have google-auth 2.0.2 which is incompatible.\n", + "google-cloud-translate 1.5.0 requires google-api-core[grpc]<2.0.0dev,>=1.6.0, but you have google-api-core 2.1.0 which is incompatible.\n", + "google-cloud-translate 1.5.0 requires google-cloud-core<2.0dev,>=1.0.0, but you have google-cloud-core 2.1.0 which is incompatible.\n", + "google-cloud-language 1.2.0 requires google-api-core[grpc]<2.0.0dev,>=1.6.0, but you have google-api-core 2.1.0 which is incompatible.\n", + "google-cloud-firestore 1.7.0 requires google-api-core[grpc]<2.0.0dev,>=1.14.0, but you have google-api-core 2.1.0 which is incompatible.\n", + "google-cloud-firestore 1.7.0 requires google-cloud-core<2.0dev,>=1.0.3, but you have google-cloud-core 2.1.0 which is incompatible.\n", + "google-cloud-datastore 1.8.0 requires google-api-core[grpc]<2.0.0dev,>=1.6.0, but you have google-api-core 2.1.0 which is incompatible.\n", + "google-cloud-datastore 1.8.0 requires google-cloud-core<2.0dev,>=1.0.0, but you have google-cloud-core 2.1.0 which is incompatible.\n", + "google-cloud-bigquery 1.21.0 requires google-cloud-core<2.0dev,>=1.0.3, but you have google-cloud-core 2.1.0 which is incompatible.\n", + "google-cloud-bigquery 1.21.0 requires google-resumable-media!=0.4.0,<0.5.0dev,>=0.3.1, but you have google-resumable-media 2.0.3 which is incompatible.\n", + "google-cloud-bigquery-storage 1.1.0 requires google-api-core[grpc]<2.0.0dev,>=1.14.0, but you have google-api-core 2.1.0 which is incompatible.\n", + "google-api-python-client 1.12.8 requires google-api-core<2dev,>=1.21.0, but you have google-api-core 2.1.0 which is incompatible.\n", + "firebase-admin 4.4.0 requires google-api-core[grpc]<2.0.0dev,>=1.14.0; platform_python_implementation != \"PyPy\", but you have google-api-core 2.1.0 which is incompatible.\n", + "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\n", + "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\u001b[0m\n", + "Successfully installed boto3-1.18.58 boto3-stubs-1.18.58 botocore-1.21.58 botocore-stubs-1.21.58 google-api-core-2.1.0 google-auth-2.0.2 google-cloud-core-2.1.0 google-cloud-storage-1.42.3 google-crc32c-1.3.0 google-resumable-media-2.0.3 hub-2.0.11 humbug-0.2.7 jmespath-0.10.0 lz4-3.1.3 mypy-boto3-cloudformation-1.18.58 mypy-boto3-dynamodb-1.18.58 mypy-boto3-ec2-1.18.58 mypy-boto3-lambda-1.18.58 mypy-boto3-rds-1.18.58 mypy-boto3-s3-1.18.58 mypy-boto3-sqs-1.18.58 pathos-0.2.8 pillow-8.2.0 pox-0.3.0 ppft-1.6.6.4 s3transfer-0.5.0 types-click-7.1.5 types-requests-2.25.9 typing-extensions-3.10.0.2 urllib3-1.25.11\n" + ] + }, + { + "output_type": "display_data", + "data": { + "application/vnd.colab-display-data+json": { + "pip_warning": { + "packages": [ + "PIL", + "google" + ] + } + } + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mE15LG7eVPko" + }, + "source": [ + "# **Attribute Error:** \n", + "After installing hub package using pip, restart the runtime and then run 'import hub' command." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "aH8t0AsIX3rL" + }, + "source": [ + "import hub" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CB_LeiwJa7_B" + }, + "source": [ + "Load Dataset" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "4iAqQf4jYCkK", + "outputId": "b6d2c31a-8e52-4e93-bf9e-d9e2acd91c13" + }, + "source": [ + "# USING GPU\n", + "dataset_path = 'hub://activeloop/mnist-train'\n", + "ds = hub.load(dataset_path)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Opening dataset in read-only mode as you don't have write permissions.\n", + "hub://activeloop/mnist-train loaded successfully.\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8zomipMya_5l" + }, + "source": [ + "# **ReadOnlyModeError:**\n", + "The above command does not download dataset on local and is present only in read-only mode. So, if you want to make modifications, you have to first get it into writing format. \n", + "Although you could try using the read_only parameter of `hub.load `instead of `hub.dataset` which toggles between read mode and write." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 365 + }, + "id": "1aWgnwxUZYVE", + "outputId": "2189cd8d-453b-47ac-f06f-40d63b670cea" + }, + "source": [ + "ps = hub.dataset(dataset_path)\n", + "ps.create_tensor('test')" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Opening dataset in read-only mode as you don't have write permissions.\n", + "hub://activeloop/mnist-train loaded successfully.\n" + ] + }, + { + "output_type": "error", + "ename": "ReadOnlyModeError", + "evalue": "ignored", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mReadOnlyModeError\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 1\u001b[0m \u001b[0mps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhub\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mps\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'test'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/humbug/report.py\u001b[0m in \u001b[0;36mwrapped_callable\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 443\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeature_report\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcallable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 444\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 445\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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 446\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 447\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped_callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/hub/core/dataset.py\u001b[0m in \u001b[0;36mcreate_tensor\u001b[0;34m(self, name, 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"WHVA1VGLzMOU" + }, + "source": [ + "#To create dataset\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "MZSK8YJAo3FE" + }, + "source": [ + "def empty(path, overwrite=False, \n", + " public=True, memory_cache_size=256, \n", + " local_cache_size=0, creds=None, token=None)\n", + "\n", + "#DatasetHandlerError: If a Dataset already exists at the given path and overwrite is False.\n", + "\n", + "#path can be The full path to the dataset. Can be:-\n", + "# - a Hub cloud path of the form hub://username/datasetname. To write to Hub cloud datasets, ensure that you are logged in to Hub (use 'activeloop login' from command line)\n", + "# - an s3 path of the form s3://bucketname/path/to/dataset. Credentials are required in either the environment or passed to the creds argument.\n", + "# - a local file system path of the form ./path/to/dataset or ~/path/to/dataset or path/to/dataset.\n", + "# - a memory path of the form mem://path/to/dataset which doesn't save the dataset but keeps it in memory instead. Should be used only for testing as it does not persist." + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AZz1aadXJuxG" + }, + "source": [ + "#Ingests a dataset from a source and stores it as a structured dataset to destination\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "9xFbmxjdKCa8" + }, + "source": [ + "\n", + "\n", + "def ingest(src, dest, images_compression='auto', \n", + " dest_creds=None, progress_bar=True, \n", + " summary=True, **dataset_kwargs)\n", + "\n", + "#SamePathException: If the source and destination path(src) are same.\n", + "#AutoCompressionError: If the source director(src, dest) is empty or does not contain a valid extension.\n", + "#InvalidFileExtension: If the most frequent file extension is found to be 'None' during auto-compression.\n", + "if images_compression == \"auto\":\n", + " images_compression = get_most_common_extension(src)\n", + " if images_compression is None:\n", + " raise InvalidFileExtension(src)\n", + "#Invalid Path Exception: If the source directory(dest) does not exist.\n", + "\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kLFROMLa4su8" + }, + "source": [ + "#Download and ingest a kaggle dataset and store it as a structured dataset to destination\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "56IJSZUq46UI" + }, + "source": [ + "\n", + "def ingest_kaggle(tag, src, dest, exist_ok=False, \n", + " images_compression='auto', dest_creds=None, \n", + " kaggle_credentials=None, progress_bar=True, summary=True, **dataset_kwargs)\n", + "\n", + "\n", + "#SamePathException Error:If the source and destination path are same.\n", + "\n", + "# if os.path.isdir(src) and os.path.isdir(dest):\n", + "# if os.path.samefile(src, dest):\n", + "# raise SamePathException(src)\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ycBkn-nI6o--" + }, + "source": [ + "#Loads an existing dataset\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "sL4zLo-j84y3" + }, + "source": [ + "def load(path, read_only=False, memory_cache_size=256, \n", + " local_cache_size=0, creds=None, token=None, verbose=True)\n", + "\n", + "#DatasetHandlerError: If a Dataset does not exist at the given path.\n", + "# if not dataset_exists(storage):\n", + "# raise DatasetHandlerError(\n", + "# f\"A Hub dataset does not exist at the given path ({path}). Check the path provided or in case you want to create a new dataset, use hub.empty().\"\n", + "# )" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9MRkdgLE_BAS" + }, + "source": [ + "#Initializes a new or existing dataset in Classes\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "I1xb1-sO_VXz" + }, + "source": [ + "class dataset_cl \n", + "(\n", + "storage, index=None, group_index='', read_only=False, public=True, token=None, verbose=True, version_state=None, **kwargs)\n", + "\n", + "#Errors Raised:\n", + "#ValueError: If an existing local path is given, it must be a directory.\n", + "#ImproperDatasetInitialization: Exactly one argument out of 'path' and 'storage' needs to be specified. This is raised if none of them are specified or more than one are specifed.\n", + "#InvalidHubPathException: If a Hub cloud path (path starting with hub://) is specified and it isn't of the form hub://username/datasetname.\n", + "#AuthorizationException: If a Hub cloud path (path starting with hub://) is specified and the user doesn't have access to the dataset.\n", + "#PathNotEmptyException: If the path to the dataset doesn't contain a Hub dataset and is also not empty." + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KJulXrrWA6Zl" + }, + "source": [ + "#Creates a tensor group. Intermediate groups in the path are also created.\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "cellView": "code", + "id": "COEWRYpoBeAn" + }, + "source": [ + "#@title Default title text\n", + "def create_tensor(self, name, htype='generic', \n", + " dtype='unspecified', sample_compression='unspecified', \n", + " chunk_compression='unspecified', **kwargs)\n", + "\n", + "\n", + "##Errors Raised:\n", + "\n", + "#TensorAlreadyExistsError: Duplicate tensors are not allowed.\n", + "#TensorGroupAlreadyExistsError: Duplicate tensor groups are not allowed.\n", + "#InvalidTensorNameError: If name is in dataset attributes.\n", + "#NotImplementedError: If trying to override 'chunk_compression'.\n", + " \n", + " \"\" # if not the head node, checkout to an auto branch that is newly created\n", + " auto_checkout(self.version_state, self.storage)\n", + " name = name.strip(\"/\")\n", + "\n", + " while \"//\" in name:\n", + " name = name.replace(\"//\", \"/\")\n", + "\n", + " full_path = posixpath.join(self.group_index, name)\n", + "\n", + " if tensor_exists(full_path, self.storage, self.version_state[\"commit_id\"]):\n", + " raise TensorAlreadyExistsError(name)\n", + "\n", + " if full_path in self._groups:\n", + " raise TensorGroupAlreadyExistsError(name)\n", + "\n", + " if not name or name in dir(self):\n", + " raise InvalidTensorNameError(name) \"\"" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7VkuKk10Gisq" + }, + "source": [ + "#Initializes a new tensor.\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "-7pMXVxNGJS_" + }, + "source": [ + "class tensor \n", + "(key, storage, version_state, index=None)\n", + "\n", + "#TensorDoesNotExistError: If no tensor with key exists and a tensor_meta was not provided.\n", + ">> key (str): The internal identifier for this tensor." + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lVmtiwB__FaP" + }, + "source": [ + "# Extends the end of the tensor by appending multiple elements from a sequence. Accepts a sequence, a single batched numpy array, or a sequence of `read()` outputs, which can be used to load files" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "OLYeMDAx_MHh" + }, + "source": [ + "def extend(self, samples: Union[np.ndarray, Sequence[SampleValue]]):\n", + " \"\"\"Extends the end of the tensor by appending multiple elements from a sequence. Accepts a sequence, a single batched numpy array,\n", + " or a sequence of `hub.read` outputs, which can be used to load files. See examples down below.\n", + "\n", + " Example:\n", + " numpy input:\n", + " >>> len(tensor)\n", + " 0\n", + " >>> tensor.extend(np.zeros((100, 28, 28, 1)))\n", + " >>> len(tensor)\n", + " 100\n", + "\n", + " file input:\n", + " >>> len(tensor)\n", + " 0\n", + " >>> tensor.extend([\n", + " hub.read(\"path/to/image1\"),\n", + " hub.read(\"path/to/image2\"),\n", + " ])\n", + " >>> len(tensor)\n", + " 2 \"\"\"\n", + "\n", + "#Error Raises:\n", + "#TensorDtypeMismatchError: TensorDtypeMismatchError: Dtype for array must be equal to or castable to this tensor's dtype\n", + " " + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jWK_nmM9E2kF" + }, + "source": [ + "# Computes the contents of the tensor in numpy format." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Mc4MonToE1xL" + }, + "source": [ + " def numpy(self, aslist=False) \n", + "\n", + " \"\"\"Computes the contents of the tensor in numpy format.\n", + "\n", + " Args:\n", + " aslist (bool): If True, a list of np.ndarrays will be returned. Helpful for dynamic tensors.\n", + " If False, a single np.ndarray will be returned unless the samples are dynamically shaped, in which case\n", + " an error is raised.\n", + "\n", + "\n", + " Returns:\n", + " A numpy array containing the data represented by this tensor.\n", + " \"\"\"\n", + "\n", + " return self.chunk_engine.numpy(self.index, aslist=aslist)\n", + "\n", + "#Error Raises: \n", + "#DynamicTensorNumpyError: If reading a dynamically-shaped array slice without `aslist=True`.\n", + " " + ], + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/colabs/hub_tensorflow.ipynb b/colabs/hub_tensorflow.ipynb new file mode 100644 index 0000000..bc601ad --- /dev/null +++ b/colabs/hub_tensorflow.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"hub .ipynb","provenance":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"markdown","source":["# Install and Load hub"],"metadata":{"id":"vn5vo5tCIo7V"}},{"cell_type":"code","source":["!pip install black\n","!pip install blackcellmagic\n","%load_ext blackcellmagic"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5zcH0zXXVl9E","executionInfo":{"status":"ok","timestamp":1647882106808,"user_tz":-330,"elapsed":13435,"user":{"displayName":"JAIVANTI DHOKEY","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"17702932951656635647"}},"outputId":"877459d7-0663-4b3d-ab59-c9f34c79f7ef"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting black\n"," Downloading black-22.1.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.4 MB)\n","\u001b[K |████████████████████████████████| 1.4 MB 5.3 MB/s \n","\u001b[?25hCollecting typed-ast>=1.4.2\n"," Downloading typed_ast-1.5.2-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (843 kB)\n","\u001b[K |████████████████████████████████| 843 kB 38.2 MB/s \n","\u001b[?25hCollecting click>=8.0.0\n"," Downloading click-8.0.4-py3-none-any.whl (97 kB)\n","\u001b[K |████████████████████████████████| 97 kB 6.7 MB/s \n","\u001b[?25hCollecting mypy-extensions>=0.4.3\n"," Downloading mypy_extensions-0.4.3-py2.py3-none-any.whl (4.5 kB)\n","Collecting pathspec>=0.9.0\n"," Downloading pathspec-0.9.0-py2.py3-none-any.whl (31 kB)\n","Requirement already satisfied: tomli>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from black) (2.0.1)\n","Collecting platformdirs>=2\n"," Downloading platformdirs-2.5.1-py3-none-any.whl (14 kB)\n","Requirement already satisfied: typing-extensions>=3.10.0.0 in /usr/local/lib/python3.7/dist-packages (from black) (3.10.0.2)\n","Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from click>=8.0.0->black) (4.11.2)\n","Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->click>=8.0.0->black) (3.7.0)\n","Installing collected packages: typed-ast, platformdirs, pathspec, mypy-extensions, click, black\n"," Attempting uninstall: click\n"," Found existing installation: click 7.1.2\n"," Uninstalling click-7.1.2:\n"," Successfully uninstalled click-7.1.2\n","\u001b[31mERROR: 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.\n","flask 1.1.4 requires click<8.0,>=5.1, but you have click 8.0.4 which is incompatible.\u001b[0m\n","Successfully installed black-22.1.0 click-8.0.4 mypy-extensions-0.4.3 pathspec-0.9.0 platformdirs-2.5.1 typed-ast-1.5.2\n","Collecting blackcellmagic\n"," Downloading blackcellmagic-0.0.3-py3-none-any.whl (4.2 kB)\n","Collecting black<22.0,>=21.9b0\n"," Downloading black-21.12b0-py3-none-any.whl (156 kB)\n","\u001b[K |████████████████████████████████| 156 kB 4.9 MB/s \n","\u001b[?25hRequirement already satisfied: jupyter<2.0.0,>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from blackcellmagic) (1.0.0)\n","Requirement already satisfied: click>=7.1.2 in /usr/local/lib/python3.7/dist-packages (from black<22.0,>=21.9b0->blackcellmagic) (8.0.4)\n","Requirement already satisfied: typed-ast>=1.4.2 in /usr/local/lib/python3.7/dist-packages (from black<22.0,>=21.9b0->blackcellmagic) (1.5.2)\n","Collecting 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bleach->nbconvert->jupyter<2.0.0,>=1.0.0->blackcellmagic) (0.5.1)\n","Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from bleach->nbconvert->jupyter<2.0.0,>=1.0.0->blackcellmagic) (21.3)\n","Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->bleach->nbconvert->jupyter<2.0.0,>=1.0.0->blackcellmagic) (3.0.7)\n","Requirement already satisfied: qtpy in /usr/local/lib/python3.7/dist-packages (from qtconsole->jupyter<2.0.0,>=1.0.0->blackcellmagic) (2.0.1)\n","Installing collected packages: tomli, black, blackcellmagic\n"," Attempting uninstall: tomli\n"," Found existing installation: tomli 2.0.1\n"," Uninstalling tomli-2.0.1:\n"," Successfully uninstalled tomli-2.0.1\n"," Attempting uninstall: black\n"," Found existing installation: black 22.1.0\n"," Uninstalling black-22.1.0:\n"," Successfully uninstalled black-22.1.0\n","Successfully installed black-21.12b0 blackcellmagic-0.0.3 tomli-1.2.3\n"]}]},{"cell_type":"code","source":["!pip3 install hub"],"metadata":{"id":"9QHbnKDPhRC6","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1647882288187,"user_tz":-330,"elapsed":3209,"user":{"displayName":"JAIVANTI DHOKEY","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"17702932951656635647"}},"outputId":"58ff20e7-1489-4afa-85ec-e8e9ed9fb0c2"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: hub in /usr/local/lib/python3.7/dist-packages (2.3.1)\n","Requirement already satisfied: boto3 in /usr/local/lib/python3.7/dist-packages (from hub) (1.21.22)\n","Requirement already satisfied: humbug>=0.2.6 in /usr/local/lib/python3.7/dist-packages (from hub) (0.2.7)\n","Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from hub) (8.0.4)\n","Requirement already satisfied: pathos in /usr/local/lib/python3.7/dist-packages (from hub) (0.2.8)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from hub) (1.21.5)\n","Requirement already satisfied: pillow in /usr/local/lib/python3.7/dist-packages (from hub) (7.1.2)\n","Requirement already satisfied: numcodecs in /usr/local/lib/python3.7/dist-packages (from hub) (0.9.1)\n","Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from hub) (4.63.0)\n","Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from humbug>=0.2.6->hub) (2.23.0)\n","Requirement already satisfied: botocore<1.25.0,>=1.24.22 in /usr/local/lib/python3.7/dist-packages (from boto3->hub) (1.24.22)\n","Requirement already satisfied: s3transfer<0.6.0,>=0.5.0 in /usr/local/lib/python3.7/dist-packages (from boto3->hub) (0.5.2)\n","Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in /usr/local/lib/python3.7/dist-packages (from boto3->hub) (1.0.0)\n","Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /usr/local/lib/python3.7/dist-packages (from botocore<1.25.0,>=1.24.22->boto3->hub) (2.8.2)\n","Requirement already satisfied: urllib3<1.27,>=1.25.4 in /usr/local/lib/python3.7/dist-packages (from botocore<1.25.0,>=1.24.22->boto3->hub) (1.25.11)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil<3.0.0,>=2.1->botocore<1.25.0,>=1.24.22->boto3->hub) (1.15.0)\n","Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from click->hub) (4.11.2)\n","Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->click->hub) (3.7.0)\n","Requirement already satisfied: typing-extensions>=3.6.4 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->click->hub) (3.10.0.2)\n","Requirement already satisfied: pox>=0.3.0 in /usr/local/lib/python3.7/dist-packages (from pathos->hub) (0.3.0)\n","Requirement already satisfied: multiprocess>=0.70.12 in /usr/local/lib/python3.7/dist-packages (from pathos->hub) (0.70.12.2)\n","Requirement already satisfied: dill>=0.3.4 in /usr/local/lib/python3.7/dist-packages (from pathos->hub) (0.3.4)\n","Requirement already satisfied: ppft>=1.6.6.4 in /usr/local/lib/python3.7/dist-packages (from pathos->hub) (1.6.6.4)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->humbug>=0.2.6->hub) (2.10)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->humbug>=0.2.6->hub) (2021.10.8)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->humbug>=0.2.6->hub) (3.0.4)\n"]}]},{"cell_type":"code","source":["%%black\n","import hub"],"metadata":{"id":"BOZDs1cKgqDl"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["%%black\n","import tensorflow as tf\n","import pathlib\n","import os\n","import matplotlib.pyplot as plt\n","# import pandas as pd\n","import numpy as np\n","from PIL import Image\n","from tqdm import tqdm\n","\n","np.set_printoptions(precision=4)\n"],"metadata":{"id":"jaEIv15DMzVc"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Load Hub datasets\n","\n"],"metadata":{"id":"XKAON2t7I9Eh"}},{"cell_type":"code","source":["%%black\n","#Look for different datasets in Hub\n","print(hub.list('activeloop')) "],"metadata":{"id":"ddzgz_uqimBc"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Visualise the data through the link given in the output\n","![image.png](data:image/png;base64,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)"],"metadata":{"id":"eIoPIYZHT4Mb"}},{"cell_type":"markdown","source":["Load CIFAR-10 data from Hub"],"metadata":{"id":"CoNGJylRoIqS"}},{"cell_type":"code","source":["%%black\n","#load CIFAR-10 dataset from HUB\n","ds_cifar10_hub = hub.load('hub://activeloop/cifar10-train')"],"metadata":{"id":"-ieOoRUaDexu"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Details about CIFAR-10 data"],"metadata":{"id":"7I4DaBSQn-Tq"}},{"cell_type":"code","source":["%%black\n","ds_cifar10_hub # details about cifar10 data"],"metadata":{"id":"i3z-qnzgDfjC"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Normalise the data"],"metadata":{"id":"IvRVA2JlY9xT"}},{"cell_type":"code","source":["%%black\n","# Normalise the data\n","\n","def to_model_fit(item):\n"," x = item['images']/255 # normalize\n"," y = item['labels']\n"," return (x, y)\n","\n","\n","ds_cifar10_hub_tf = ds_cifar10_hub.tensorflow()\n","ds_cifar10_hub_tf = ds_cifar10_hub_tf.map(lambda x: to_model_fit(x))\n","ds_cifar10_hub_tf\n"],"metadata":{"id":"L--KwHBIDiRh"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Batching and Shuffling Data"],"metadata":{"id":"CqWZz4l4J5IM"}},{"cell_type":"markdown","source":["Shuffle entire dataset"],"metadata":{"id":"evQv8SnZoQbd"}},{"cell_type":"code","source":["%%black\n","batch_size = 10\n","\n","# shuffling on the entire dataset\n","shuffle_buffer = len(ds_cifar10_hub)\n","\n","def visualize_img_label_in_first_batch_TF_ds(ds, batch_size):\n"," for image, label in ds:\n"," for b in range(batch_size):\n"," print(f'Image size: {image.numpy()[b].shape}')\n"," print(label.numpy()[b])\n"," plt.imshow(image.numpy()[b])\n"," plt.show()\n"," break\n","\n"],"metadata":{"id":"8iGSqtC9Dldf"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Shuffled batched data "],"metadata":{"id":"fRK-ckTZoOof"}},{"cell_type":"code","source":["%%black\n","ds_cifar10_hub_tf_shuffled_batched = ds_cifar10_hub_tf.shuffle(shuffle_buffer).batch(batch_size)"],"metadata":{"id":"PRqOrAujDpVT"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Visualize first batch in ds_cifar10_hub_tf_shuffled_batched"],"metadata":{"id":"aik0fvO2of06"}},{"cell_type":"code","source":["%%black\n","visualize_img_label_in_first_batch_TF_ds(ds_cifar10_hub_tf_shuffled_batched, batch_size)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"d1RSas_uDufk","executionInfo":{"status":"ok","timestamp":1647159912732,"user_tz":-330,"elapsed":82452,"user":{"displayName":"JAIVANTI DHOKEY","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"17702932951656635647"}},"outputId":"0a5e71bc-67ef-404e-e530-8830fe27b735"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Image size: (32, 32, 3)\n","[9]\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{"needs_background":"light"}},{"output_type":"stream","name":"stdout","text":["Image 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"stream","name":"stdout","text":["Image 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1m7fc8kf0H2OneTupCKQNCYnuCRz2WWX1W5vX36I7hOYtdApuRzWOX+eji0EPeLKTn0vsmZQcHJigstrY1O76FiW8/oEWaP+0sozLmstOp8Py/hEMnkNiKUyRtQHLnq8Vjs4VvBcM3dbt+C9DLvknD2QsPXOLkQiKNmFSAQluxCJoGQXIhGU7EIkwkhX4x1An/xQPzJj7N27J3jEehbPnKVjTfTpGDp8v4bXr+KvlMGqadCqKWrJ1I/qjwUru21i4shzrjJE7p/eOW5OKXJuhLFW/Qp/VDotz/l8WNDjKQt6K7HaddGq9VRQL24lWPlH0FLKg3ZeJVGHondiJ/PBWqWt9XhCiNcQSnYhEkHJLkQiKNmFSAQluxCJoGQXIhHW0/7pEICvANiPgdZ1xN1vN7M5AF8HcCkGLaCud/eX1n489vrCNZnTp+sf1pv1pg8AaJTcLNLvcTmpG0hN5vWSXU5qiAFAg54vgIKfc/TEhDIUMXGw2AGg7HNZqFgJzCkNPlft2fozsCaXtfI2N8k0AlkrUJsAEIkt0ADHAkNLI+OSXVRH0YLjlaRFWLM9XE07xnre2fsAPuXuVwJ4K4BPmNmVAG4GcL+7XwHg/upvIcQFyprJ7u7H3f3H1e1FAE8AOAjgOgB3VXe7C8AHtitIIcTm2dB3djO7FMCbATwIYL+7H6+GnsfgY74Q4gJl3cluZlMAvgXgk+7+ii/EPihWXfs1wswOm9lRMzva6fDveEKI7WVdyW5mOQaJ/lV3/3a1+QUzO1CNHwBwom5fdz/i7vPuPj82xiubCCG2lzWT3cwMg37sT7j7bauG7gFwQ3X7BgDf3frwhBBbxXpcb28D8BEAj5rZw9W2WwB8BsA3zOxGAL8EcP2aj2RAgzi9ioJLPCdO1H5oQNbi0kRZ8DpzRZe71BDEkTeJoyyQ1zywlBV9LodFjrjIXVX06s8tOGMYqRcHAHkgaxV9Ln06GWuNc/ddUfCDlR7NMZe8WH03BHXm8nbQTiqYEA+ug6LDn2smzzabPI6S1mwcTs4d7Or+A3Al891r7S+EuDDQL+iESAQluxCJoGQXIhGU7EIkgpJdiEQYacFJg8GI5OGBK6hPJCprBDJZIBmxGADAwWUt1sqpEcgdwaFCt5aFviYuNbHdIrkxy4NjBcUcLSiK2Vk8U7u9Pc6LVLba03Ss1+eT1etFzrz6+KP59eBJa+aRw5GPLS5yKTgnElt4nQ5he9M7uxCJoGQXIhGU7EIkgpJdiERQsguRCEp2IRJhpNJbRCS9dXv1soWX/LUqD1xjHjmoAkmjTwpENjMu/TCXHxBLK42of1lQcJKFX0byoPP4ox58Uc+5Xqe+4OfCi/y8JnYFkmKDu+V6He6+G5+or6GQ5/zS7wQWQeqiA9Dt8+Is58/xHoLMZBc5/UJJl6B3diESQckuRCIo2YVIBCW7EImgZBciEUa+Gh+tTjN6ZDXeysCUENTvcuZoQdzChy3VN/qBKtCMaqcFBIM2RIxF0Gqq3+NLu0WrzeOIzCTEvLR89hTfJ1h9LhuTdKzX5yaT8RZ5zKDuXpbx9k9RrcSlc4t8v6BeH8jxij6f34xcV5GapHd2IRJByS5EIijZhUgEJbsQiaBkFyIRlOxCJMKa0puZHQLwFQxaMjuAI+5+u5ndCuCjAE5Wd73F3e+NH4ybOCIjDDNceGAWKaOWQMGxyqBdE4gJoih4HM1AemuENcY2XpMv2q/XD+aDjgAe1FVrZnyMFdjLmnyf6Uludjl55nxwLH4GRVE/V2Vn43XrAMAD2XN5qd78AwBecnmwZIauYO4bHs19PevR2fsAPuXuPzazaQAPmdl91djn3f0fN3xUIcTIWU+vt+MAjle3F83sCQAHtzswIcTWsqHv7GZ2KYA3A3iw2nSTmT1iZnea2UVbHJsQYgtZd7Kb2RSAbwH4pLsvAPgigDcAuBqDd/7Pkf0Om9lRMzvaWebmfiHE9rKuZDezHINE/6q7fxsA3P0Fdy98sGLxJQDX1O3r7kfcfd7d58fG66uGCCG2nzWT3Qa1k+4A8IS737Zq+4FVd/sggMe2PjwhxFaxntX4twH4CIBHzezhatstAD5sZldjoHs8C+Bjaz6Sc+nCAicak1aiOlxh86SgjljYVoeoLhbUhGsENdwaxl9rmWQEAP1IpiTzG5xy2LaoEfSoimroFVZ/ae2a4Us7s3N87PTCcTrWavNPjMzFWAYuwCyQFIse/yra63B5sJ2Fvb5qiZ4zVg8xunzXsxr/AxJOrKkLIS4o9As6IRJByS5EIijZhUgEJbsQiaBkFyIRRl5wkstekcTDiutFrrFAFgocYGUga1FBJtTrOKH7ruQxRkUPQWTALAsKLDZy/nBR+6rgOWNDMzO7eByB5DW7ixecHJ+comNnF+qLQPb6fA7LoJDp0mLkbOOP2SZtqADQufLIzcfiD64pvbMLkQhKdiESQckuRCIo2YVIBCW7EImgZBciEUYqvbk7+lTy2Lj0Fh5rWGdbNEZcXmGxzGAsco1F59wYotBjoxEUjgz64rECoQAQqJvIiGSX51zmywJn2Otet4+OLXe6dOyM17sHm81ImuWPF0lv40G9hsiZ1ye96rq9oLAoub6j603v7EIkgpJdiERQsguRCEp2IRJByS5EIijZhUiEkbvemDQwjHEsluS4tJIFMlQkaxkJMpKMhnW2NYI+drm16BhzQwUtyhCEERb1jExv7Xa7dvvEJHevTc/M0LFuN5DDlnihR6ocBrEvLwePF/QQnBzn7rsskDB7RO8tgifGIt2ToHd2IRJByS5EIijZhUgEJbsQiaBkFyIR1lyNN7MxAA8AaFf3/6a7f9rMLgNwN4DdAB4C8BF350umGKxM98iP+/v94Ef/ZEU7ap+UN7nhotniq9lRzTW2bB0snIdEK/WRYhDVJuuV9fNYBvXRyrLeiAHEhpGgaxQu3n1J7fa5oMWTBefcWV6hY/3AMMLUiSIwSp1bOEvHsqguXDjHfL9Op/7cioKvxueBeYmxnst0BcC73P1NGLRnvtbM3grgswA+7+6/D+AlADdu+OhCiJGxZrL7gHPVn3n1zwG8C8A3q+13AfjAtkQohNgS1tufPas6uJ4AcB+ApwGccf+NWfg5AAe3J0QhxFawrmR398LdrwZwCYBrAPzheg9gZofN7KiZHWXfTYQQ28+Glpbc/QyA7wP4EwCzZr9pwn0JgGNknyPuPu/u82Nj9T+hFEJsP2smu5ntNbPZ6vY4gPcAeAKDpP/z6m43APjudgUphNg861m/PwDgLjPLMHhx+Ia7/7uZPQ7gbjP7ewD/DeCOtR7IzNDK69/dQ1MLUS3ynIc/FtT8imquhXXhSCB5IMdEjxeZMaJabZFBoiQGicjsEkmYgRqG9hiXMPfu3Vu7nT3/ALB0fpmOlUUwx7wxF8bbE7Xbu10uNwa+JjSDNlpR/cLI6MVk1izj18AwebRmsrv7IwDeXLP9GQy+vwshfgfQL+iESAQluxCJoGQXIhGU7EIkgpJdiESwyHm15QczOwngl9WfewCcGtnBOYrjlSiOV/K7Fsfr3b1W9xxpsr/iwGZH3X1+Rw6uOBRHgnHoY7wQiaBkFyIRdjLZj+zgsVejOF6J4nglr5k4duw7uxBitOhjvBCJsCPJbmbXmtnPzOwpM7t5J2Ko4njWzB41s4fN7OgIj3unmZ0ws8dWbZszs/vM7Mnqf16ZcXvjuNXMjlVz8rCZvW8EcRwys++b2eNm9lMz+6tq+0jnJIhjpHNiZmNm9kMz+0kVx99V2y8zswervPm6WdAHrA53H+k/ABkGZa0uB9AC8BMAV446jiqWZwHs2YHjvgPAWwA8tmrbPwC4ubp9M4DP7lActwL46xHPxwEAb6luTwP4OYArRz0nQRwjnRMMzM9T1e0cwIMA3grgGwA+VG3/JwB/uZHH3Yl39msAPOXuz/ig9PTdAK7bgTh2DHd/AMDpV22+DoPCncCICniSOEaOux939x9XtxcxKI5yECOekyCOkeIDtrzI604k+0EAv1r1904Wq3QA3zOzh8zs8A7F8DL73f14dft5APt3MJabzOyR6mP+tn+dWI2ZXYpB/YQHsYNz8qo4gBHPyXYUeU19ge7t7v4WAH8G4BNm9o6dDggYvLIjrHuyrXwRwBsw6BFwHMDnRnVgM5sC8C0An3T3hdVjo5yTmjhGPie+iSKvjJ1I9mMADq36mxar3G7c/Vj1/wkA38HOVt55wcwOAED1/4mdCMLdX6gutBLAlzCiOTGzHIME+6q7f7vaPPI5qYtjp+akOvaGi7wydiLZfwTgimplsQXgQwDuGXUQZjZpZtMv3wbwXgCPxXttK/dgULgT2MECni8nV8UHMYI5sUGhvjsAPOHut60aGumcsDhGPSfbVuR1VCuMr1ptfB8GK51PA/ibHYrhcgyUgJ8A+Oko4wDwNQw+DvYw+O51IwY98+4H8CSA/wIwt0Nx/AuARwE8gkGyHRhBHG/H4CP6IwAerv69b9RzEsQx0jkB8McYFHF9BIMXlr9ddc3+EMBTAP4NQHsjj6tf0AmRCKkv0AmRDEp2IRJByS5EIijZhUgEJbsQiaBkFyIRlOxCJIKSXYhE+D+JA6mMmnOXdQAAAABJRU5ErkJggg==\n"},"metadata":{"needs_background":"light"}},{"output_type":"stream","name":"stdout","text":["Image 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F2c8VVWzXRMbE3AmqSOWZb1lrZy29ILpWkpqliVRLg2STK7EHsxaZUUtqiyrj5bMx3IS4/YkO2x7kHU4v3VbuM+5M/E1YMuJpZRYb1GtuXPJ9ZZlmyXOFqdfiK+DYWKXRkSZbQBVYNsuJe3GJord3b9LWPKRd07aXwhxfqBv0AlRCBK7EIUgsQtRCBK7EIUgsQtRCJ0WnFxcGvDkocbv3oAnRlpQ5M889oyM2LqqNsWZbf1eHMcwGFtOLJJR8nHas6TlVVa70OODLgWf371kHwuyCgGWEwvw3FJs85wcNWeV9QaxBTUcJL5WkgHWi5P2wtuZ9WJ7bTFxyUZZiBYf05JsSrPmIIdBIUqAoTUH4klBTN3ZhSgEiV2IQpDYhSgEiV2IQpDYhSgEiV2IQujUesMM+s1FG5PEqzDrrYqy0MitpvnEektqWMaZY734eEOLfRxPPmstmZHlxdhrGgbHnEvmapRk0S0l2VpVkGEHsBhkqY2SLLo0MyxxZnuJ9RkdM7qmIJ+PDEvezyxTMTpfVjwyctiGw+kKTgohXgFI7EIUgsQuRCFI7EIUgsQuRCF0uhpv1mO+37xy7UkiTLRy2gsSCABGSZLMuaQ+XbYCGtUES8IgycdhkGRVpCvkSassDwrKZXOVWRBRkgZAP6uTFxwyydNgkKzUZzu2WT2vevFr9mQ+qmQ+zOP428RoyWuukpqCEbqzC1EIErsQhSCxC1EIErsQhSCxC1EIErsQhTDRejOzvcCXGbdkdmC/u3/ezG4DPgw8Wz/1Vne/Lz+aY4GtMRwmtkUw1ktaNWUl7bK0G+vFYz5sPmhqqySJCdl+nthyWeKHWdAqaxQnmWQWTz9oMwRgWS+kYGwUzCHkd57Masre6izhpQ3ZXGVJPlVyXUXXcfae5a+6mdX47APgE+7+AzPbDjxsZvfXY59z979Z81mFEJ2zml5vR4Aj9eNTZvYEsGejAxNCrC9r+pvdzC4D3gQ8VG+62cwOmtkdZqbm60Kcx6xa7Ga2DfgG8HF3Pwl8AbgCuJrxnf8zwX77zOyAmR04e+rkOoQshGjDqsRuZnOMhf4Vd78HwN2PufvQx19q/yJwTdO+7r7f3RfcfeGC7ReuV9xCiDUyUew2Xn68HXjC3T+7YvvuFU97P/DY+ocnhFgvVrMa/xbgQ8CjZvZIve1W4INmdjVjO+5p4CMTj+TOKGhp40mrm8iisn5cAy39HEssoyppC2QW2B2ZrZK0VholdfLSDLDEOuxXzW/pKLPyMrsxq8mXZLBFltco6YeVZRzOzcWXajZXUQZbv9/ueMNkLLMie9lkBdlykVbS4yUxrGY1/rs0X10TPHUhxPmEvkEnRCFI7EIUgsQuRCFI7EIUgsQuRCF0XHDSmOs3f75YYkNZUNiwl2RCeVJwMmOun2Q1DYPYg9cEeRZdRpJslmb7RbZc1lops4US523Cfs17Vpa0akqyvAZL8dgwyR6MbMXRIMkay46XZJul7lpifUZOauLaMhdYm1kMurMLUQgSuxCFILELUQgSuxCFILELUQgSuxCF0Kn1hjse2CvDYZzhU0VWU+IzZNlJGVlhwygrywexLTTKKl9m1lXWB86SzKvg8zuz3rKyhlEW3Xi/tRfTzHrOZf3+svjT/nxhhmCW+Rhbm5ndm/bMSzMLM4Mz2mftBSd1ZxeiECR2IQpBYheiECR2IQpBYheiECR2IQqhU+vN8dBiGybF9Qa+1Lg9K+aYZb1lVsdw2HwugEFgsQ1HsRXWn4ttHEtsqKzPV5b1FvUiy3qsJfUrGSWFGSObLz1oy9ec9cXLrLIquEairDxIpwNLst6yY2aW3TDsB5hl8639Pq07uxCFILELUQgSuxCFILELUQgSuxCFMHE13sw2Aw8Cm+rnf93dP2lmlwN3Aa8CHgY+5B4sm9e4O8uD5qd4ko4RrlUmK7QtcgsASHJa4rpqWcG4dOU/diDa4kGNt2ylO2MQtCYC6Fl2+QT3kaB22iTyUn5rT6DJ2mFFrasmkV2PbRJosuQfC/bJnKbV3NkXgXe4+xsZt2e+zsyuBT4NfM7dfwM4Ady0imMJIWbERLH7mNP1j3P1PwfeAXy93n4n8L4NiVAIsS6stj97VXdwPQ7cD/wEeN79l7/jHQL2bEyIQoj1YFVid/ehu18NvAa4Bvit1Z7AzPaZ2QEzO/DC6VMtwxRCTMuaVuPd/XngO8DvATvMfrlC8xrgcLDPfndfcPeFLdu2TxWsEKI9E8VuZq82sx314y3Au4AnGIv+j+qn3Qh8a6OCFEJMz2oSYXYDd5pZxfjD4W53/xcz+xFwl5n9JfAfwO2TDuTuDJYjdy62DCI7IUpMgTzJpJ8kd2QWSZRkkpHtk52rrf0TnS+bj2wsap8EeSJMm/ZbbecqI7Kv2s5v2zjy1xa1ckresxYxTBS7ux8E3tSw/SnGf78LIX4F0DfohCgEiV2IQpDYhSgEiV2IQpDYhSgEa9N6pvXJzJ4Fnql/vAT4WWcnj1EcL0VxvJRftThe6+6vbhroVOwvObHZAXdfmMnJFYfiKDAO/RovRCFI7EIUwizFvn+G516J4ngpiuOlvGLimNnf7EKIbtGv8UIUwkzEbmbXmdl/mdmTZnbLLGKo43jazB41s0fM7ECH573DzI6b2WMrtu00s/vN7Mf1/xfPKI7bzOxwPSePmNl7Oohjr5l9x8x+ZGaPm9kf19s7nZMkjk7nxMw2m9n3zOyHdRx/UW+/3MweqnXzNTObX9OB3b3Tf0DFuKzV64B54IfAVV3HUcfyNHDJDM77NuDNwGMrtv01cEv9+Bbg0zOK4zbgTzuej93Am+vH24H/Bq7qek6SODqdE8YZrNvqx3PAQ8C1wN3AB+rtfw98dC3HncWd/RrgSXd/yselp+8Crp9BHDPD3R8EnnvZ5usZF+6Ejgp4BnF0jrsfcfcf1I9PMS6OsoeO5ySJo1N8zLoXeZ2F2PcAP13x8yyLVTrwbTN72Mz2zSiGF9nl7kfqx0eBXTOM5WYzO1j/mr/hf06sxMwuY1w/4SFmOCcviwM6npONKPJa+gLdW939zcAfAh8zs7fNOiAYf7KTle7ZWL4AXMG4R8AR4DNdndjMtgHfAD7u7idXjnU5Jw1xdD4nPkWR14hZiP0wsHfFz2Gxyo3G3Q/X/x8HvslsK+8cM7PdAPX/x2cRhLsfqy+0EfBFOpoTM5tjLLCvuPs99ebO56QpjlnNSX3uNRd5jZiF2L8PXFmvLM4DHwDu7ToIM9tqZttffAy8G3gs32tDuZdx4U6YYQHPF8VV8346mBMbF2i7HXjC3T+7YqjTOYni6HpONqzIa1crjC9bbXwP45XOnwB/NqMYXsfYCfgh8HiXcQBfZfzr4DLjv71uYtwz7wHgx8C/AztnFMc/Ao8CBxmLbXcHcbyV8a/oB4FH6n/v6XpOkjg6nRPgtxkXcT3I+IPlz1dcs98DngT+Gdi0luPqG3RCFELpC3RCFIPELkQhSOxCFILELkQhSOxCFILELkQhSOxCFILELkQh/D+W+2e+iWiOrwAAAABJRU5ErkJggg==\n"},"metadata":{"needs_background":"light"}},{"output_type":"stream","name":"stdout","text":["Image 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"stream","name":"stdout","text":["Image 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nuc2rIyEIxwrpCwUAGQk310n8qi8qrO7+6cCzd9crZ8Q4tZCv6ATIhHk7EIkgpxdiESQswuRCHJ2IRJhoAkn8/k8xkfDv6zNVbgkA1IKqWI8UeLoKI82K5W5vDY+wmWog7cfCLafPc3lqazDx3j+wiy1NSOllUZHwtF3ADBeD/9wKTM+xnKFJ8zMnCcwbCzPUdtKK5xg8cQFXlrpr//uOLX91dPHqO3+e7iMtncyPFcWib6rRiQv5Pn18coil952T4ZLmAHApaVwhGC7w31ix0i4vFk+Ei2pK7sQiSBnFyIR5OxCJIKcXYhEkLMLkQhydiESYaDSm3cdjQapUxapNzY8FJaGhogkBwCTk1x6u3SZS16R3JFYXJ4PthfLXO7Iurwu2/Iyt1XKXA7L5flcVYeHg+3NPJeTChVuazR5hF17ntdLO/nmhWD7T196g/Z54n8+T20H9k9T2/vfx6W3iR3hiMkFPnRUq1zaHK5xCa07xGXKfESymzgYTqiaIZwsEwDKpfAxK5S4dKwruxCJIGcXIhHk7EIkgpxdiESQswuRCINdjYejk4UDJDyyOupZeIncuuFtAUCnw1e6J3aGVz8B4PZ9PC/c0FA4MCFvPKii2+Ur56XIyn8sEKYQUS4WWuGNju7l+d0s8pXvK3w1fnGBj/HnL54Mtj/7Ei+D9K533kttD977bmrLF/hELrTDqsZUJP9fscLVlXwhvLoPALVyWAkBgHKRu1qGcJDS0gqfX1h45d9yPOBJV3YhEkHOLkQiyNmFSAQ5uxCJIGcXIhHk7EIkwlrKP+0H8GcAptAr93TU3b9mZuMAvgvgIHoloD7h7rwOEoBOlmFuPpy3bGwsXAIHAOavhMv7NImMBwDdLg9KGBrigQ4rGZcu5q+GpZCZGV7aZ+Eqz9NWLPKgheEar4LtOf4d3bawbNRaCZeFAqIxSFhc4NLbT46/TG1X2+FxfPgjH6d97rj9DmqrR4J1MufnQa4QHsdwjQcaFUgfAGiR3HoA0GpzWyki5+WI9GYRPZqe35FjuZYrewfAH7v73QAeAPCHZnY3gEcBPOXudwJ4qv+3EOIWZVVnd/dz7v6z/usFACcATAN4GMDj/bc9DuBjWzVIIcTGualndjM7COBeAM8AmHL3a3mBz6N3my+EuEVZs7ObWQ3A9wF8zt1vyOLg7g7ytGBmR8zsmJkda7V4OVkhxNayJmc3syJ6jv4td/9Bv3nGzPb07XsABNO/uPtRdz/s7odLJb5IIYTYWlZ1djMz9Oqxn3D3r1xnegLAI/3XjwD40eYPTwixWawl6u39AD4N4Hkzu1af5/MAvgTge2b2GQCnAHxitQ15t4tGoxG0NZtnab9OO3z7X8y4NHHnIb6E0FjiZXqOvcFzpDFJxsFlvlIk99jUBI+giiXDy7rcduViWAZcXFqgfa42+OPViVf4cRmZ2E9tv/3A+4Lt9Z3hskUAUIjMo0VKfcXkpmI5LG9mEWm2E9EiY/2qVV6uKReRS42U5ipFJEA2DrYtYA3O7u5/DxAhEPjQav2FELcG+gWdEIkgZxciEeTsQiSCnF2IRJCzC5EIA004mcvlMERKOeXzPGljuxOOJjr56rlgOwC8duo0tR2Y5BFU5SqPRGt3w9+NXedSzanzF/n2eJAU9u7aQW1TUzx5ZIMkKXzlTDhyEADOXgnLoQAwfds7qO09991PbaVyeI4jU4Wcc9moVOKlkGLnTo7YIrtCm0i9AFCtRs4dIvMBQIecwzGqxFcALrFF5+KmRyCE+JVEzi5EIsjZhUgEObsQiSBnFyIR5OxCJMJApbd8IY+xsXAiRY9EE4HICZXye2iX06+/SG0Hdx+ktj27eCTaUD1cI85zfBrHJ3iU18IVLsuNjvKkmENDvKYYymHJbrJ6gHZZGuZJJQ+94+18X5Gzxy0cmVcp80iubofrcjFJKZZcNPOwjUlyqxGT1wqF9blTqxWWSxcWeKRipRKOsIvNha7sQiSCnF2IRJCzC5EIcnYhEkHOLkQiDHQ1vpAvYOfOcJknM/69UyqHgyCGI2WcXo6sSrbaPHddLeNBIZfOnQy218d4YMr0vj3U9lpjkdrykZXdq1f5Ku3cwkp4HO98gPbZd3tYZQBWCayI5FUrl1jAU2Q13nkAikciaCplnvut2QrPR4zYivvKCt9es8nPq1ggT5aFlQu2Sg/wwBqtxgsh5OxCpIKcXYhEkLMLkQhydiESQc4uRCKsKr2Z2X4Af4ZeSWYHcNTdv2ZmXwTw+wAu9N/6eXd/MrqtXA5lIpOM7ODyT4fICdUhHrTyvvf/FrX94vjT1Da8wqW3K/NhaWh2npdI2pvxKV5e4lLNpatclpvYySXHciUslbWvhstCAcB4vU5ty61lauu0ucwzlA8f58oQl8myQkR+jUiR7YhEVSySfrFyTBFbPlLOq7XCj2erFQnyIZ9tej8vr7VMyqjl83ye1qKzdwD8sbv/zMzqAJ41sx/3bV919/+whm0IIbaZtdR6OwfgXP/1gpmdADC91QMTQmwuN/XMbmYHAdwL4Jl+02fN7Dkze8zMwoHqQohbgjU7u5nVAHwfwOfcfR7A1wG8DcA96F35v0z6HTGzY2Z2bHmZPw8LIbaWNTm7mRXRc/RvufsPAMDdZ9w9c/cugG8ACFYMcPej7n7Y3Q8PDfEE+0KIrWVVZ7de6YlvAjjh7l+5rv36CI+PA3hh84cnhNgs1rIa/34AnwbwvJkd77d9HsCnzOwe9OS4kwD+YLUN5XM5DNfCMs9QjZc7KpKIoeVFnjutNsqXEN77zz5Ebb98+Rlq2zkVzv223ApHLQHAuSsz1LZjit/pzC3PUdvB26aorVwNz1WzwaW3+QuRiLIRHtHXdv65S6Xw/HfBpbcOuJRXI+cNAHSWeb/luavB9jdeP0n7TE/vo7bJyXDUJgBYZPxd8HpT5Ur4PMgikX4sEtRyfD9rWY3/eyA40qimLoS4tdAv6IRIBDm7EIkgZxciEeTsQiSCnF2IRBhw+acCxsbDUk5thEtlnVY42iwr8Ggni0T/VOtc5hua5pF0eaL+FJtcTlrocnmt0YlEa0USPS41eNLD2kh4/KwdAIYrPAnkyTd/SW0ref7ZduwKl70qRRKL7tk9SW1j4/z8mL3Ay2gtLoaj9nbt2kX7jI7y86P3s5MwVVKSCQCswOfY2TWXJKKMwUenK7sQySBnFyIR5OxCJIKcXYhEkLMLkQhydiESYbDSW76A+sho0LbU5IktykS2iMknHR4whFKVyyB3/cYHqe3i3Llg++QIl2oO7ArXPAOAxYXL1Hb+dR4x/EIkYmuERKmNjPAklSOVcN0wANi3705qq+7k2cnaubB0WC7y+ehGohgvLXFbN+OC0xiRWevDXIosRJJbxuqvFUu8H0uaCgBtUretUOTyK9j5HdHedGUXIhHk7EIkgpxdiESQswuRCHJ2IRJBzi5EIgxUejMzFEmtt1w7HNkGADkiQWSRBH8rbR4xVGhHJI0cl4aGKweC7SwhJgDU6lzyOnjgILXdftvt1HbixeeobWIinIxy5ySPKLtw7iS1XWlyyStHohEBoEISfnad10N7802eFLO5wve158AhamuR86rd4duL5HlEJ3KeNpcXqK0SqUtYIdFyZZJUsjeO8DzmIlF5urILkQhydiESQc4uRCLI2YVIBDm7EImw6mq8mVUAPA2g3H//X7j7F8zsEIDvANgJ4FkAn3Z3HiUAoOuOlZVw/rR2JMCgRFbjrcBX1S2yGu9dvtza8sgqLSutk+fbazbD5YcAYOY8t+3ZG175B4A7f/O91FaploPtQ0NcZajWwmWtAKDTmKe2diTaqEAUiizjAU/FSLAOIiW2ltr83Mk8rNjkIudON6LylMj8AkCtznPy5SI56Frt8P5iigG64fnwiJSwliv7CoDfcff3oFee+SEzewDAnwD4qrvfAeAKgM+sYVtCiG1iVWf3Hov9P4v9fw7gdwD8Rb/9cQAf25IRCiE2hbXWZ8/3K7jOAvgxgNcAzLn7tUDcMwB4cLMQYttZk7O7e+bu9wDYB+B+AG9f6w7M7IiZHTOzYwsL/BdGQoit5aZW4919DsDfAvgnAEbN7NoC3z4AZ0mfo+5+2N0P1+u8xrYQYmtZ1dnNbNLMRvuvqwB+F8AJ9Jz+X/Tf9giAH23VIIUQG2ctgTB7ADxuZnn0vhy+5+5/aWYvAfiOmf07AP8bwDdX21CWZZifD8tNLBgAAMqlsNxRrfI+9UgASrPBgzFyef79lyPSWyEiq+RzfHsTO7lUU4zkQYsF3iyRXG3diIxjkcCPao3n+cMKl9FYrrZOpKJRocrv/MqVWHI1bssXw8cmFjDSbK7v/BiucXlzaZnPVUZktG4rIm3G6jyxPqu9wd2fA3BvoP119J7fhRC/AugXdEIkgpxdiESQswuRCHJ2IRJBzi5EIlgsSmbTd2Z2AcCp/p8TAC4ObOccjeNGNI4b+VUbx23uHkw4OFBnv2HHZsfc/fC27Fzj0DgSHIdu44VIBDm7EImwnc5+dBv3fT0ax41oHDfyazOObXtmF0IMFt3GC5EI2+LsZvaQmf3CzF41s0e3Ywz9cZw0s+fN7LiZHRvgfh8zs1kze+G6tnEz+7GZvdL/P1w/aevH8UUzO9ufk+Nm9pEBjGO/mf2tmb1kZi+a2R/12wc6J5FxDHROzKxiZj8xs5/3x/Fv++2HzOyZvt9818x4+GMIdx/oPwB59NJa3Q6gBODnAO4e9Dj6YzkJYGIb9vsBAPcBeOG6tn8P4NH+60cB/Mk2jeOLAP7VgOdjD4D7+q/rAP4PgLsHPSeRcQx0TgAYgFr/dRHAMwAeAPA9AJ/st/8nAP/yZra7HVf2+wG86u6vey/19HcAPLwN49g23P1pAJff0vwweok7gQEl8CTjGDjufs7df9Z/vYBecpRpDHhOIuMYKN5j05O8boezTwM4fd3f25ms0gH8jZk9a2ZHtmkM15hy93P91+cBhMuxDobPmtlz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\n"},"metadata":{"needs_background":"light"}},{"output_type":"stream","name":"stdout","text":["Image 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"stream","name":"stdout","text":["Image 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["# Resizing CIFAR-10 to (28,28,3) using Hub transformation feature"],"metadata":{"id":"TR-wGO-RKKg-"}},{"cell_type":"markdown","source":["Append the label and image to the output sample"],"metadata":{"id":"XQ8tdj5jomN8"}},{"cell_type":"code","source":["%%black\n","@hub.compute\n","def resize(sample_in, sample_out, new_size): \n"," # Append the label and image to the output sample\n"," sample_out.labels.append(sample_in.labels.numpy())\n"," sample_out.images.append(np.array(Image.fromarray(sample_in.images.numpy()).resize(new_size)))\n"," \n"," return sample_out\n","\n","\n"],"metadata":{"id":"4KZKqZVpDweF"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["visualize some of the images from the dataset"],"metadata":{"id":"dMmwbaN7seB-"}},{"cell_type":"code","source":["%%black\n","def visualize_first_N_img_label_in_hub_ds(ds, number_images):\n"," N = 0\n"," for item in ds:\n"," if N <= number_images:\n"," print(f'Image size: {item.images.numpy().shape}')\n"," print(item.labels.numpy())\n"," plt.imshow(item.images.numpy())\n"," plt.show()\n"," N += 1\n"," else:\n"," break\n","\n"],"metadata":{"id":"iKEAxW7FENld"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Visualize first 4 images in ds_cifar10"],"metadata":{"id":"BX3iFBqBorpv"}},{"cell_type":"code","source":["%%black\n","# Visualize first 4 images in ds_cifar10\n","visualize_first_N_img_label_in_hub_ds(ds_cifar10_hub, 4)"],"metadata":{"id":"wIM7cO0g79sF","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1647159913715,"user_tz":-330,"elapsed":1007,"user":{"displayName":"JAIVANTI DHOKEY","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"17702932951656635647"}},"outputId":"a1b4dd2a-7a3c-4311-b84c-5b075145c5c5"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Image size: (32, 32, 3)\n","[6]\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{"needs_background":"light"}},{"output_type":"stream","name":"stdout","text":["Image 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"stream","name":"stdout","text":["Image 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"stream","name":"stdout","text":["Image 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"stream","name":"stdout","text":["Image 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["list the resize measurement and seed"],"metadata":{"id":"Pkf0aF9hqr55"}},{"cell_type":"code","source":["%%black\n","resize_size = (28,28)\n","\n","# We use the same seed to shuffle both the tf.data and Hub datasets so \n","# that we can efficiently compare them\n","shuffle_common_seed = 21\n"],"metadata":{"id":"4qqCx8crHO6A"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Evaluate resize dataset"],"metadata":{"id":"uXvI1ah5rFzo"}},{"cell_type":"code","source":["%%black\n","# Hub path of new resized dataset -- this could be any path format (hub, s3, etc)\n","path_dataset_resized = './cifar10-dataset-resized-256x256'\n","\n","# We use the overwrite=True to make this code re-runnable\n","ds_cifar10_hub_resized = hub.like(path_dataset_resized, ds_cifar10_hub, overwrite = True)\n","\n","resize(new_size=resize_size).eval(ds_cifar10_hub, ds_cifar10_hub_resized, num_workers = 2)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"tVz0bEBwE2cT","outputId":"9420ab99-3646-493e-c407-41a2e8fb06fd","executionInfo":{"status":"ok","timestamp":1647163803594,"user_tz":-330,"elapsed":840815,"user":{"displayName":"JAIVANTI DHOKEY","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"17702932951656635647"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Evaluating resize: 100%|██████████| 50000/50000 [14:00<00:00, 59.51it/s]\n"]}]},{"cell_type":"markdown","source":["Normalize the data"],"metadata":{"id":"OSja_vmnrScC"}},{"cell_type":"code","source":["%%black\n","def to_model_fit(item):\n"," x = item['images']/255 # normalize\n"," y = item['labels']\n"," return (x, y)"],"metadata":{"id":"hU5x3gq0oL_c"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Fit the model"],"metadata":{"id":"9jS5Hl1ZrWmC"}},{"cell_type":"code","source":["%%black\n","ds_cifar10_hub_tf = ds_cifar10_hub_resized.tensorflow()\n","\n","ds_cifar10_hub_tf = (ds_cifar10_hub_tf\n"," # calling to_model_fit\n"," .map(lambda x: to_model_fit(x))\n"," .batch(batch_size)\n"," .shuffle(len(ds_cifar10_hub_resized), seed=shuffle_common_seed)\n"," .prefetch(tf.data.AUTOTUNE))\n"],"metadata":{"id":"Bxw6RP0NF0MG"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["visualise the labeled data from batch"],"metadata":{"id":"IJSb6WWwrbp8"}},{"cell_type":"code","source":["%%black\n","visualize_img_label_in_first_batch_TF_ds(ds_cifar10_hub_tf, batch_size)"],"metadata":{"id":"A0JXebfyn29r"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Train the Model"],"metadata":{"id":"hm8CiCXpQExW"}},{"cell_type":"code","source":["%%black\n","num_classes = 10\n","cifar10_class = ['airplane', 'automobile','bird','cat','deer', 'dog', 'frog', 'horse', 'ship', 'truck']"],"metadata":{"id":"wOerqMwBiEa5"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["compile the model"],"metadata":{"id":"PggNk5CYrjUr"}},{"cell_type":"code","source":["%black\n","def train_with_simple_CNN_function(ds):\n"," model = tf.keras.Sequential([\n"," tf.keras.layers.InputLayer(input_shape=(resize_size[0], resize_size[1], 3)),\n"," tf.keras.layers.Conv2D(16,3,padding='same',activation='relu'),\n"," tf.keras.layers.MaxPooling2D(),\n"," tf.keras.layers.Conv2D(32,3,padding='same',activation='relu'),\n"," tf.keras.layers.MaxPooling2D(),\n"," tf.keras.layers.Conv2D(64,3,padding='same',activation='relu'),\n"," tf.keras.layers.MaxPooling2D(),\n"," tf.keras.layers.Dropout(0.2),\n"," tf.keras.layers.Flatten(),\n"," tf.keras.layers.Dense(128,activation='relu'),\n"," tf.keras. layers.Dense(len(cifar10_class), activation='softmax')\n"," ])\n","\n"," # Compile the model, we are using the Adam optimizer, the SparseCategoricalCrossentropy loss\n"," # and SparseCategoricalAccuracy because our labels are not categorical \n"," model.compile(\n"," optimizer='adam',\n"," loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n"," metrics=[tf.keras.metrics.SparseCategoricalAccuracy()]\n"," )\n","\n"," # Start training over 2 epoch\n"," history = model.fit(ds, epochs = 2)\n"," model.summary()"],"metadata":{"id":"jPA70A6UP-Z7"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["call the training function and run iteration"],"metadata":{"id":"vtveQrlDrtg9"}},{"cell_type":"code","source":["%%black\n","train_with_simple_CNN_function(ds_cifar10_hub_tf)"],"metadata":{"id":"FTOFhjauH_wY"},"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/colabs/hub_torch.ipynb b/colabs/hub_torch.ipynb new file mode 100644 index 0000000..eb6cf02 --- /dev/null +++ b/colabs/hub_torch.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"hub_torch.ipynb","provenance":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["# Install and load Hub"],"metadata":{"id":"5s4MWEw5fAMp"}},{"cell_type":"code","source":["!pip install black\n","!pip install blackcellmagic\n","%load_ext blackcellmagic"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5zcH0zXXVl9E","executionInfo":{"status":"ok","timestamp":1647882106808,"user_tz":-330,"elapsed":13435,"user":{"displayName":"JAIVANTI DHOKEY","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"17702932951656635647"}},"outputId":"877459d7-0663-4b3d-ab59-c9f34c79f7ef"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting black\n"," Downloading black-22.1.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.4 MB)\n","\u001b[K 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This behaviour is the source of the following dependency conflicts.\n","flask 1.1.4 requires click<8.0,>=5.1, but you have click 8.0.4 which is incompatible.\u001b[0m\n","Successfully installed black-22.1.0 click-8.0.4 mypy-extensions-0.4.3 pathspec-0.9.0 platformdirs-2.5.1 typed-ast-1.5.2\n","Collecting blackcellmagic\n"," Downloading blackcellmagic-0.0.3-py3-none-any.whl (4.2 kB)\n","Collecting black<22.0,>=21.9b0\n"," Downloading black-21.12b0-py3-none-any.whl (156 kB)\n","\u001b[K |████████████████████████████████| 156 kB 4.9 MB/s \n","\u001b[?25hRequirement already satisfied: jupyter<2.0.0,>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from blackcellmagic) (1.0.0)\n","Requirement already satisfied: click>=7.1.2 in /usr/local/lib/python3.7/dist-packages (from black<22.0,>=21.9b0->blackcellmagic) (8.0.4)\n","Requirement already satisfied: typed-ast>=1.4.2 in /usr/local/lib/python3.7/dist-packages (from black<22.0,>=21.9b0->blackcellmagic) (1.5.2)\n","Collecting 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tomli-1.2.3\n"]}]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"aumgJyBDUary","executionInfo":{"status":"ok","timestamp":1647410981456,"user_tz":-330,"elapsed":17507,"user":{"displayName":"JAIVANTI DHOKEY","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"17702932951656635647"}},"outputId":"28d47353-bbb3-4eec-975a-63df780d5f58"},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting hub\n"," Downloading hub-2.3.1-py3-none-any.whl (287 kB)\n","\u001b[?25l\r\u001b[K |█▏ | 10 kB 18.4 MB/s eta 0:00:01\r\u001b[K |██▎ | 20 kB 20.1 MB/s eta 0:00:01\r\u001b[K |███▍ | 30 kB 24.5 MB/s eta 0:00:01\r\u001b[K |████▌ | 40 kB 16.1 MB/s eta 0:00:01\r\u001b[K |█████▊ | 51 kB 6.9 MB/s eta 0:00:01\r\u001b[K |██████▉ | 61 kB 8.0 MB/s eta 0:00:01\r\u001b[K |████████ | 71 kB 8.7 MB/s eta 0:00:01\r\u001b[K |█████████ | 81 kB 7.5 MB/s eta 0:00:01\r\u001b[K |██████████▎ | 92 kB 8.2 MB/s eta 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This behaviour is the source of the following dependency conflicts.\n","datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\u001b[0m\n","Successfully installed boto3-1.21.20 botocore-1.24.20 hub-2.3.1 humbug-0.2.7 jmespath-0.10.0 numcodecs-0.9.1 pathos-0.2.8 pox-0.3.0 ppft-1.6.6.4 s3transfer-0.5.2 urllib3-1.25.11\n"]}],"source":["!pip3 install hub"]},{"cell_type":"code","source":["%%black\n","import matplotlib.pyplot as plt"],"metadata":{"id":"JmJ66lOfheCP"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["%%black\n","import hub\n","from PIL import Image\n","import numpy as np\n","import numpy as np\n","import pandas as pd\n","import random\n","import os, time\n","import torch\n","from torchvision import datasets, transforms, models\n","\n","ds_train = hub.load('hub://activeloop/fashion-mnist-train')\n","ds_test = hub.load('hub://activeloop/fashion-mnist-test')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JBJbpknSVOvH","executionInfo":{"status":"ok","timestamp":1647410992818,"user_tz":-330,"elapsed":11374,"user":{"displayName":"JAIVANTI DHOKEY","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"17702932951656635647"}},"outputId":"bcbdb8ae-5bdc-42f7-ff6e-2e14cebfa9d8"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["NumExpr defaulting to 2 threads.\n","Opening dataset in read-only mode as you don't have write permissions.\n","hub://activeloop/fashion-mnist-train loaded successfully.\n","This dataset can be visualized at https://app.activeloop.ai/activeloop/fashion-mnist-train.\n","Opening dataset in read-only mode as you don't have write permissions.\n","hub://activeloop/fashion-mnist-test loaded successfully.\n","This dataset can be visualized at https://app.activeloop.ai/activeloop/fashion-mnist-test.\n"]}]},{"cell_type":"markdown","source":["# Load and visualise data using the link in the output\n","Train data\n","\n","![image.png](data:image/png;base64,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)\n"],"metadata":{"id":"38jKrzUiWGSv"}},{"cell_type":"code","source":["%%black\n","ds_train = hub.load('hub://activeloop/fashion-mnist-train')\n","ds_test = hub.load('hub://activeloop/fashion-mnist-test')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"m8rymZ-zWAFy","executionInfo":{"status":"ok","timestamp":1647411006635,"user_tz":-330,"elapsed":3667,"user":{"displayName":"JAIVANTI DHOKEY","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"17702932951656635647"}},"outputId":"f986474d-3481-448d-fd11-22c1869401e1"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Opening dataset in read-only mode as you don't have write permissions.\n","hub://activeloop/fashion-mnist-train loaded successfully.\n","This dataset can be visualized at https://app.activeloop.ai/activeloop/fashion-mnist-train.\n","Opening dataset in read-only mode as you don't have write permissions.\n","hub://activeloop/fashion-mnist-test loaded successfully.\n","This dataset can be visualized at https://app.activeloop.ai/activeloop/fashion-mnist-test.\n"]}]},{"cell_type":"code","source":["%%black\n","Image.fromarray(ds_train.images[0].numpy()).resize((100,100))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":117},"id":"08rebET1ZQWf","executionInfo":{"status":"ok","timestamp":1647256935605,"user_tz":-330,"elapsed":31,"user":{"displayName":"JAIVANTI DHOKEY","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"17702932951656635647"}},"outputId":"3f9cde76-3a08-45ef-e798-7fea8a3e9fb0"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAGQAAABkCAAAAABVicqIAAAOgklEQVR4nO1ayXIkR3J97h6RS22oAtDobm5D2YiiaUwXmUn//xW6SCaTaTQcTg97QQO1ZGaE+9MhC71whsMGTQcdGIcqoDIrXoa7h/t7HgX8On4dv47/F0N+6RdNjeHxwUT8yXvTLwXJy46n/QOIQPDTKI8BEbyfyPrLjb8Zy/mKCCDBH999/sIvWomo9durbY398YM5/y/NJWpNv1jtLlfTcDcFELWQIsIHtIdBQAB5JIggAE3N5up6u1ro6XRkFZZDAch5KYIHB5EAKPI4kPlhRZvl7rMvr1qZ8jhKEQ5SJrz3lswgDx88EuQ8Ure+fPrFdSpHK1WrYMh6536OZs7W+tg/j1qJBiDNand9c3OtQ1aPHEnL5dXt8e5umO8iP4hmmf//dBARFQDarq+vn1zt5KRK6dFkHw9vX//Rp3cb890yVAgSfLS5pFlfXl3tLmAUSo/cMKb9KoZ6rB8aSVISIaMGH+UTggDT8mK33awpIZS2BiPlzBLLwzC5e1QPWEpt14pPUxmLPwKEBAmE9ZvterWMKISm/f2J3bK/ypu7+/1hGIfxeJq0Wy7XFyuc3t4dQD7GXDHvhLzYbFZ9G6WjiIzlzldNv1nfDPe3t/vD/nAnquvd7vLqAnc/NAmEP85cEOmW682mzyY5hyiGNotZajJr3zZd17bJBttcXu52a2RYAqU+zvGa8ma3261aOGFJVCL6KXUaU9QS1oY0/bam5XrZ8ii66xfZ2ulTQebYt3Z1fX21XeWY4JpULS+HaZrKOE3TNFZpbAnR1LQ67Qe9uLjZGJrxcStJi6vnz643ixQBipk1C4nx/u3bYTyNlZozLHetKcrberDtbrcc74s9DqTZff71F9erNgWhEkJoQiteAcuEBENSm5XhufP2+ulO2pjKz4J8VJ/759/+9qubZaNOnPeNwtq1LddjhaCMYwn38AhcXK6++nJ5rLcvfx7k3SCA/vk33z5ZdSZCCMEgHcyrvpbqIhz2d4fTMJUattjutp8/kTy+ehGPM1fePH22S1AAEMEMo5rBcIrEKYsg3EXT4vrZ7sl65LC/f2SqF1VVIemEikBEdL4SAUggkLvlqVTk7dMnq6bu96fyCan+o8oQ0+nUZ6V7iImKQFUQEeeSmBap30zFA2m1u7A4/PDqEI8tWj6djj0zvYSEqqiKKAiSISSkyYtwkqLdqquH+1e3Ax9dGRlBkmSIkBIiKsIIkCQhYgJAKNIusijd4/EgIqoqIiIICSJAipoAxBlEVYQERDW3XaN4NIiamapSNBgRUb24NF1WciYrVTWpRIgWQ+pXi+bRIGo5m+JcvKPWMo1Fe1UwziwrTCgRUosSue/b/Ikg79ihpqZJwoggIaDXMk6WYRoMmWNaGAAkJhLI7WK5/IR6Ih+QYM1tm+l0DyhUGF4rYFkLAFEVBYOiIhxHJtFutb3/pCwswpmDatv2XS4RHqEiFAGD0JQ1BKJqqvDqIqY+FrYN8uLi7fHTfDJTR11uVn2bnDMFnam8mqmqyPw0IhAQmrQc975YBVPbf1LuEhEHtF9dfXa9btMEQqmCmXxrzgJCKQjBjNt2ON69mDZXuTDl/PMglDkX6vLm8y+erFpVDSVEQFHLEilLAAqCDhGIpaYP3v/puMPyVKmPKFrarq8v122GymwcilrWTMvioChJkAIRS8k57e+lL/enyX+eEslDANNLKQGBiEJAEKIeDDUJUAChqipJenU0y4rD8PLN/eFnicTsSRKI4+vF9d0UpjbTCqoIGUHCARElNJs6PUYGLr5YjsP++z+9vP15SiSCOZo43r9+sx9dRQwCEkhJ6FFrCcBEVDSZIKqPhXLZbV5898P3L17efxqDnPl67pd9hmvMygYQNYW5sBICyJw9AwgX2HqV7+vdm7f74cO0Ig9iDJw5AgAwHv7y7Tf/8M3zpkZxigoJRFWJIFTnjCACgCQDmlYSXRyPY/0gd8l5d80gwQed+b4yPv3nf/1q2xeWSp0vR52NKUaCoTyXFQKSevOGpTg+SpDn/SrvVeZHY/XFP/3LjR+nqAFSAEpwjgsxRszrFkQQEE2tdW1WUSszyLspH97Id7JJLaWUzOzq68+vF+O+eLwLB4+HJ4MoVAR0+JxzVCW1y/Wy0Xkl8iAmKRQK8b5jAuhiuV6vln13+bsdynEYeZ4WCI+5/sr5FQjSPQAVuLC7fHZ3e/sehPOG+8uuwmJ7+eTmenexvni6Op1OQ4GqgbODPaChoioqCpAR9ABUlQXeXX1+9+JHReuDlsIcohC5uLx+8uz5k6vLi1XbTHWqATnvm3M7YFbV548AUaGIKlyj3T3989JmEH68ANVkyVSs6bp2e3l5eXm1vbhYdUIPyeYsrkkBQgUE1UxjKkFRy42BBEUkHM1mt108gHxkH81N23ZNtna1u7zYbtfLvuu6LiEikHKMY4GGCURMleGSk4zDfqLmbtP2Sg8PCoN5vV139pc13tqm7RaLZd+kfvv8+dV23WcE1VgjoGbhU4QiRNQ0G9wlZ5RyONE6Re5TVC/VwUCWzap9iC7VnLMKJOWmadquXy67bN326bOrzbJRnyrPseQK1yYiQgxKUdUkOWEyFZjZOdNxTpoprZZnELG02F5fLkwlNzmnlNuuyyLWb9etgeHhJOjVI6htt/LjsUjK4m7SmlkChpKRuzbXk3iNCIpBky0XeQbRtNx9+dvf7BrV3GRTkWQZrNSc6uBZvDqFDC/TabLL9UXgVAALr5pyZwos1rFA05iWGtUJFSPEUt/muUtki93zr7/95kmrmpskCIpo1FI8ECWqRMRZmJa7vdqlWTaFCPiQHcHUJW0ya/FaA5oMCIqmpDNI2n71d7/9zWeXWSQlQ3gAEmZWq8+yg5xJ8HQ6vKi1y52uW4iKmcZEFI86VaRw1qm68+yYiPA4E+588+0/fvn0ojMSwQj3WWISZAgoBFQ1dU3Zn77f7xHXm6vEUkIEcYjpeKq0nLMn1uJByEzJWWUYy6xPuptvfnezyBIRAMPd5xpEzi8EBGK5X5Qu7l4OSaZ8ucRwKKBPp9Pxfj/ZYtW1YeJ1XrSogFFlnCqB1Mr2yWefb1HJIM8CBHPNFouYi4tARDXlfnmIN99F0xHHQyXrdDoN+1PJTTCcQlJFVWc641pDTCM9T188u9o048i5aqnQCIGoiIDu1UsEjCxjtJ+XF7eH379682JntcwGBXWhuV+0yQQwqJiqziQAoqlJJX3ZfX6zzhwPrpZM7Zz3ISKWEuo4jl6cFj5WNF/fvPyPf/vv6Q//uW6SGWBNt+j7vmvmMJpT30zMGEqoNV1b05P+yaYVn4ZqmUyiomcFranJMkUVhiPCg9rdNDfT76fXt68W3WKRRfOSrS03qwYkI85S7JzSCWhq+m5Mlk3C5+5xeKiZ2dx2FTBQy1TqHBB0TciLy+dfLYrlbrlqzfJis1kvF11ihJ/7z7NUPVMLa7p+TKMNx8PRpDEIC6mWk4ioQcDqw/44OUUY8CqSal19s7o9jGg36y6n3PRd0zZKvlcxIhDOIlLE2n5R05H7/f7YIhvDvQZSTSZiluGIctoPFckEQXcyTtb//dfHt3elWa+7JidVIcMJMs7UYG4nzMIb2vQrphNO4zgZCEZEOAA6xBID4eU0jmHnAk36FO1y1Zbb2zEvF12TjKyllggGI4IBBEPO3WF3l2axljRocVHWKQjSDCIRpFpJiKiTU+RBWENURUTzEkXbJgnCaynu1d09GAxATUVmdjlhirzetemkE1UxHVzMLJlEuHtAzQDSaVBVUdVEiKj5qQq7FhKlgKVMDtCnyedtfE4qlppM+FTzatelk42TR52G0CRmWWpErU5Rg4jELP9VdU68Cj8NlrJGFHpEmSZqgo9jnXsVCBJq1gRJKZ76ZUon2d/d3bfMhEa4oE6lelABUYgqVG3OE+c9FjCKuU9Rw+tUISHh8S7xekBTIuhNCoiapWNdvHyxuew7rVMtriy1xszQ7ewCsVmdKwgwPKQW8SisEeExE1aFkgHSS4VFMGoryQxRSzqN7Z//uLJu1Qz7WiOiOmGm5waH6JzEIhiqACJqcBSSQSdBAZ2QBDICIQgnROA1GqjU4XRKBfvXf1zkvjcSdPcaPLfKSPLcraEH5lOHCJ9NI8RcOsC5iRMhmJd+Jn0RZDnt704JGN58x2ncbxvJObyWUklnuKqqmIrOhRaiACQYcW7WQAUqiiDVFFEDYoQ41FLKKfdajm/+/GJMQH2Lu9vXL57dXG1aiTqehhrhFSJzaZhz/vmo6pyeZpL9XqOKCauTYppaQjU1XWsW09s//+H7moDYD69u37x8ddKuTZKUmGqZuz4iOncB3zF5YO4KyoO7HgoDQublJlEA2vat1OH21Yvvv2cC6D7WWorKdNFnEzRWk+MMMjvlASQIURXO/SABiHe6Y+7dQUxZa5SS/fT2xf/8cDsizQJovJM6vf6vi/Vqs7tYZToftI2K4rwSIauHqLB6nDfNh6eKmuYejNTjfgjhcP/6h+/+NADpfMo1vR1uf99uttfPvs67lUHM5iMsfdB4EIFPpUJRx7HMgRYPQhhkOBhBJYbbV7enadi/efVmfze3bAUESjkAWO1u7nS91kZMTFQeXCwIAqCXsUBQTqeJEPfqeGgMEwy6e2ji8eWLV/fHw93r13cEfszq9x6Sdb/JkpquSSYAqWbK4iFgOZ4mCOowFEI8agDvQEBGeKhxePPq9nA87d/u52uJH+mT6Q7T63/vTKxfLJrGJEpo05kPQ4XEuD9OEEYtDiAYD+Y6TxIMirIeD6exTNP56PHHh/5iKeesEF2sV12XxYdq3TKXw3EiYtgfJsh7JXc+Q37/nPM7w32uYn8V5AO41arrstahWL/I5XiYiBgP5afu/1vjp3++IF2Tk3ips7nGSkSJn7z9l4HMwokeYklYPQj4L8L4WyB/5dJP/3jg1/Hr+Mvxv7G/O+UCBivnAAAAAElFTkSuQmCC\n"},"metadata":{},"execution_count":73}]},{"cell_type":"markdown","source":["# Data Transformation"],"metadata":{"id":"ViWoY9DIX2Yw"}},{"cell_type":"code","source":["%%black\n","def transform(sample_in):\n"," return {'images': tform(sample_in['images']), 'labels': sample_in['labels']}\n","\n","tform = transforms.Compose([\n"," transforms.ToPILImage(), # Must convert to PIL image for subsequent operations to run\n"," transforms.RandomRotation(20), # Image augmentation\n"," transforms.ToTensor(), # Must convert to pytorch tensor for subsequent operations to run\n"," transforms.Normalize([0.5], [0.5]),\n","])\n"],"metadata":{"id":"-1_fbaF6XFrw"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# List the Batch size w.r.t data"],"metadata":{"id":"mWeA1LQKOakp"}},{"cell_type":"code","source":["%%black\n","batch_size = 32\n","\n","train_loader = ds_train.pytorch(num_workers = 2, shuffle = True, transform = transform, batch_size = batch_size)\n","test_loader = ds_test.pytorch(num_workers = 2, transform = transform, batch_size = batch_size)\n"],"metadata":{"id":"zIEderPPaIJH"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["%%black\n","# device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n","#device = torch.device(\"cpu\")\n"],"metadata":{"id":"o_i_AyVQaPAD"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Model define"],"metadata":{"id":"up2W8LGRX2lJ"}},{"cell_type":"code","source":["%%black\n","# Simple model can be trained on a CPU\n","device = torch.device(\"cpu\")\n","\n","net = models.resnet18(pretrained=True)\n","# Convert model to grayscale\n","net.conv1 = torch.nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False)\n","\n","# Update the fully connected layer based on the number of classes in the dataset\n","net.fc = torch.nn.Linear(net.fc.in_features, len(ds_train.labels.info.class_names))\n","\n","classifier=net.to(device)\n","\n","# Specity the loss function and optimizer\n","criterion = torch.nn.CrossEntropyLoss()\n","optimizer = torch.optim.SGD(net.parameters(), lr=0.001, momentum=0.1)"],"metadata":{"id":"lECz786EbSvZ"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Model Training"],"metadata":{"id":"GirEhgBIbcG7"}},{"cell_type":"code","source":["%%black\n","def train_model(loader, test_loader, model, epochs = 1):\n"," for epoch in range(epochs): # loop over the dataset multiple times\n","\n"," # Zero the performance stats for each epoch\n"," running_loss = 0.0\n"," start_time = time.time()\n"," total = 0\n"," correct = 0\n"," \n"," for i, data in enumerate(loader):\n"," # get the inputs; data is a list of [inputs, labels]\n"," inputs = data['images']\n"," labels = torch.squeeze(data['labels'])\n","\n"," inputs = inputs.to(device)\n"," labels = labels.to(device)\n","\n"," # zero the parameter gradients\n"," optimizer.zero_grad()\n","\n"," # forward + backward + optimize\n"," outputs = model(inputs.float())\n"," loss = criterion(outputs, labels)\n"," loss.backward()\n"," optimizer.step()\n"," \n"," _, predicted = torch.max(outputs.data, 1)\n"," total += labels.size(0)\n"," correct += (predicted == labels).sum().item()\n"," accuracy = 100 * correct / total\n"," \n"," # Print performance statistics\n"," running_loss += loss.item()\n"," if i % 10 == 0: # print every 10 batches\n"," batch_time = time.time()\n"," speed = (i+1)/(batch_time-start_time)\n"," print('[%d, %5d] loss: %.3f, speed: %.2f, accuracy: %.2f %%' %\n"," (epoch + 1, i, running_loss, speed, accuracy))\n","\n"," running_loss = 0.0\n"," plt.plot(running_loss, speed)\n"," plt.plot(running_loss, accuracy)\n"," print('Testing Model Performance')\n"," test_model(test_loader, model)\n","\n"," print('Finished Training')\n"," return accuracy, running_loss, speed"],"metadata":{"id":"qbqI-1ZgbbMW"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["%%black\n","def test_model(loader, model):\n"," start_time = time.time()\n"," total = 0\n"," correct = 0\n"," with torch.no_grad():\n"," for i, data in enumerate(loader):\n"," # get the inputs; data is a list of [inputs, labels]\n"," inputs = data['images']\n"," labels = torch.squeeze(data['labels'])\n","\n"," inputs = inputs.to(device)\n"," labels = labels.to(device)\n","\n"," # zero the parameter gradients\n"," optimizer.zero_grad()\n","\n"," # forward + backward + optimize\n"," outputs = model(inputs.float())\n","\n"," _, predicted = torch.max(outputs.data, 1)\n"," total += labels.size(0)\n"," correct += (predicted == labels).sum().item()\n"," accuracy = 100 * correct / total\n"," \n"," print('Finished Testing')\n"," print('Testing accuracy: %.1f %%' %(accuracy))\n"," plt.plot(accuracy)"],"metadata":{"id":"UuB2pTLVbS77"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Calculate Loss, Speed and Accuracy for epoch"],"metadata":{"id":"TcV9BbNPOPWk"}},{"cell_type":"code","source":["%%black\n","train_model(train_loader, test_loader, net, epochs = 1)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"d816E3WRbrxi","executionInfo":{"status":"ok","timestamp":1647257709154,"user_tz":-330,"elapsed":771879,"user":{"displayName":"JAIVANTI DHOKEY","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"17702932951656635647"}},"outputId":"94ca7ade-a278-4949-f39b-e57107da0121"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[1, 0] loss: 2.653, speed: 0.02, accuracy: 12.50 %\n"]},{"output_type":"display_data","data":{"text/plain":[""],"application/javascript":["/* Put everything inside the global mpl namespace */\n","window.mpl = {};\n","\n","\n","mpl.get_websocket_type = function() {\n"," if (typeof(WebSocket) !== 'undefined') {\n"," return WebSocket;\n"," } else if (typeof(MozWebSocket) !== 'undefined') {\n"," return MozWebSocket;\n"," } else {\n"," alert('Your browser does not have WebSocket support. ' +\n"," 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n"," 'Firefox 4 and 5 are also supported but you ' +\n"," 'have to enable WebSockets in about:config.');\n"," };\n","}\n","\n","mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n"," this.id = figure_id;\n","\n"," this.ws = websocket;\n","\n"," this.supports_binary = (this.ws.binaryType != undefined);\n","\n"," if (!this.supports_binary) {\n"," var warnings = document.getElementById(\"mpl-warnings\");\n"," if (warnings) {\n"," warnings.style.display = 'block';\n"," warnings.textContent = (\n"," \"This browser does not support binary websocket messages. \" +\n"," \"Performance may be slow.\");\n"," }\n"," }\n","\n"," this.imageObj = new Image();\n","\n"," this.context = undefined;\n"," this.message = undefined;\n"," this.canvas = undefined;\n"," this.rubberband_canvas = undefined;\n"," this.rubberband_context = undefined;\n"," this.format_dropdown = undefined;\n","\n"," this.image_mode = 'full';\n","\n"," this.root = $('
');\n"," this._root_extra_style(this.root)\n"," this.root.attr('style', 'display: inline-block');\n","\n"," $(parent_element).append(this.root);\n","\n"," this._init_header(this);\n"," this._init_canvas(this);\n"," this._init_toolbar(this);\n","\n"," var fig = this;\n","\n"," this.waiting = false;\n","\n"," this.ws.onopen = function () {\n"," fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n"," fig.send_message(\"send_image_mode\", {});\n"," if (mpl.ratio != 1) {\n"," fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n"," }\n"," fig.send_message(\"refresh\", {});\n"," }\n","\n"," this.imageObj.onload = function() {\n"," if (fig.image_mode == 'full') {\n"," // Full images could contain transparency (where diff images\n"," // almost always do), so we need to clear the canvas so that\n"," // there is no ghosting.\n"," fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n"," }\n"," fig.context.drawImage(fig.imageObj, 0, 0);\n"," };\n","\n"," this.imageObj.onunload = function() {\n"," fig.ws.close();\n"," }\n","\n"," this.ws.onmessage = this._make_on_message_function(this);\n","\n"," this.ondownload = ondownload;\n","}\n","\n","mpl.figure.prototype._init_header = function() {\n"," var titlebar = $(\n"," '
');\n"," var titletext = $(\n"," '
');\n"," titlebar.append(titletext)\n"," this.root.append(titlebar);\n"," this.header = titletext[0];\n","}\n","\n","\n","\n","mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n","\n","}\n","\n","\n","mpl.figure.prototype._root_extra_style = function(canvas_div) {\n","\n","}\n","\n","mpl.figure.prototype._init_canvas = function() {\n"," var fig = this;\n","\n"," var canvas_div = $('
');\n","\n"," canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n","\n"," function canvas_keyboard_event(event) {\n"," return fig.key_event(event, event['data']);\n"," }\n","\n"," canvas_div.keydown('key_press', canvas_keyboard_event);\n"," canvas_div.keyup('key_release', canvas_keyboard_event);\n"," this.canvas_div = canvas_div\n"," this._canvas_extra_style(canvas_div)\n"," this.root.append(canvas_div);\n","\n"," var canvas = $('');\n"," canvas.addClass('mpl-canvas');\n"," canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n","\n"," this.canvas = canvas[0];\n"," this.context = canvas[0].getContext(\"2d\");\n","\n"," var backingStore = this.context.backingStorePixelRatio ||\n","\tthis.context.webkitBackingStorePixelRatio ||\n","\tthis.context.mozBackingStorePixelRatio ||\n","\tthis.context.msBackingStorePixelRatio ||\n","\tthis.context.oBackingStorePixelRatio ||\n","\tthis.context.backingStorePixelRatio || 1;\n","\n"," mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n","\n"," var rubberband = $('');\n"," rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n","\n"," var pass_mouse_events = true;\n","\n"," canvas_div.resizable({\n"," start: function(event, ui) {\n"," pass_mouse_events = false;\n"," },\n"," resize: function(event, ui) {\n"," fig.request_resize(ui.size.width, ui.size.height);\n"," },\n"," stop: function(event, ui) {\n"," pass_mouse_events = true;\n"," fig.request_resize(ui.size.width, ui.size.height);\n"," },\n"," });\n","\n"," function mouse_event_fn(event) {\n"," if (pass_mouse_events)\n"," return fig.mouse_event(event, event['data']);\n"," }\n","\n"," rubberband.mousedown('button_press', mouse_event_fn);\n"," rubberband.mouseup('button_release', mouse_event_fn);\n"," // Throttle sequential mouse events to 1 every 20ms.\n"," rubberband.mousemove('motion_notify', mouse_event_fn);\n","\n"," rubberband.mouseenter('figure_enter', mouse_event_fn);\n"," rubberband.mouseleave('figure_leave', mouse_event_fn);\n","\n"," canvas_div.on(\"wheel\", function (event) {\n"," event = event.originalEvent;\n"," event['data'] = 'scroll'\n"," if (event.deltaY < 0) {\n"," event.step = 1;\n"," } else {\n"," event.step = -1;\n"," }\n"," mouse_event_fn(event);\n"," });\n","\n"," canvas_div.append(canvas);\n"," canvas_div.append(rubberband);\n","\n"," this.rubberband = rubberband;\n"," this.rubberband_canvas = rubberband[0];\n"," this.rubberband_context = rubberband[0].getContext(\"2d\");\n"," this.rubberband_context.strokeStyle = \"#000000\";\n","\n"," this._resize_canvas = function(width, height) {\n"," // Keep the size of the canvas, canvas container, and rubber band\n"," // canvas in synch.\n"," canvas_div.css('width', width)\n"," canvas_div.css('height', height)\n","\n"," canvas.attr('width', width * mpl.ratio);\n"," canvas.attr('height', height * mpl.ratio);\n"," canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n","\n"," rubberband.attr('width', width);\n"," rubberband.attr('height', height);\n"," }\n","\n"," // Set the figure to an initial 600x600px, this will subsequently be updated\n"," // upon first draw.\n"," this._resize_canvas(600, 600);\n","\n"," // Disable right mouse context menu.\n"," $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n"," return false;\n"," });\n","\n"," function set_focus () {\n"," canvas.focus();\n"," canvas_div.focus();\n"," }\n","\n"," window.setTimeout(set_focus, 100);\n","}\n","\n","mpl.figure.prototype._init_toolbar = function() {\n"," var fig = this;\n","\n"," var nav_element = $('
');\n"," nav_element.attr('style', 'width: 100%');\n"," this.root.append(nav_element);\n","\n"," // Define a callback function for later on.\n"," function toolbar_event(event) {\n"," return fig.toolbar_button_onclick(event['data']);\n"," }\n"," function toolbar_mouse_event(event) {\n"," return fig.toolbar_button_onmouseover(event['data']);\n"," }\n","\n"," for(var toolbar_ind in mpl.toolbar_items) {\n"," var name = mpl.toolbar_items[toolbar_ind][0];\n"," var tooltip = mpl.toolbar_items[toolbar_ind][1];\n"," var image = mpl.toolbar_items[toolbar_ind][2];\n"," var method_name = mpl.toolbar_items[toolbar_ind][3];\n","\n"," if (!name) {\n"," // put a spacer in here.\n"," continue;\n"," }\n"," var button = $('');\n"," button.click(method_name, toolbar_event);\n"," button.mouseover(tooltip, toolbar_mouse_event);\n"," nav_element.append(button);\n"," }\n","\n"," // Add the status bar.\n"," var status_bar = $('');\n"," nav_element.append(status_bar);\n"," this.message = status_bar[0];\n","\n"," // Add the close button to the window.\n"," var buttongrp = $('
');\n"," var button = $('');\n"," button.click(function (evt) { fig.handle_close(fig, {}); } );\n"," button.mouseover('Stop Interaction', toolbar_mouse_event);\n"," buttongrp.append(button);\n"," var titlebar = this.root.find($('.ui-dialog-titlebar'));\n"," titlebar.prepend(buttongrp);\n","}\n","\n","mpl.figure.prototype._root_extra_style = function(el){\n"," var fig = this\n"," el.on(\"remove\", function(){\n","\tfig.close_ws(fig, {});\n"," });\n","}\n","\n","mpl.figure.prototype._canvas_extra_style = function(el){\n"," // this is important to make the div 'focusable\n"," el.attr('tabindex', 0)\n"," // reach out to IPython and tell the keyboard manager to turn it's self\n"," // off when our div gets focus\n","\n"," // location in version 3\n"," if (IPython.notebook.keyboard_manager) {\n"," IPython.notebook.keyboard_manager.register_events(el);\n"," }\n"," else {\n"," // location in version 2\n"," IPython.keyboard_manager.register_events(el);\n"," }\n","\n","}\n","\n","mpl.figure.prototype._key_event_extra = function(event, name) {\n"," var manager = IPython.notebook.keyboard_manager;\n"," if (!manager)\n"," manager = IPython.keyboard_manager;\n","\n"," // Check for shift+enter\n"," if (event.shiftKey && event.which == 13) {\n"," this.canvas_div.blur();\n"," // select the cell after this one\n"," var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n"," IPython.notebook.select(index + 1);\n"," }\n","}\n","\n","mpl.figure.prototype.handle_save = function(fig, msg) {\n"," fig.ondownload(fig, null);\n","}\n","\n","\n","mpl.find_output_cell = function(html_output) {\n"," // Return the cell and output element which can be found *uniquely* in the notebook.\n"," // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n"," // IPython event is triggered only after the cells have been serialised, which for\n"," // our purposes (turning an active figure into a static one), is too late.\n"," var cells = IPython.notebook.get_cells();\n"," var ncells = cells.length;\n"," for (var i=0; i= 3 moved mimebundle to data attribute of output\n"," data = data.data;\n"," }\n"," if (data['text/html'] == html_output) {\n"," return [cell, data, j];\n"," }\n"," }\n"," }\n"," }\n","}\n","\n","// Register the function which deals with the matplotlib target/channel.\n","// The kernel may be null if the page has been refreshed.\n","if (IPython.notebook.kernel != null) {\n"," IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n","}\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["
"]},"metadata":{}}]},{"cell_type":"code","source":[""],"metadata":{"id":"a0hBXlH8syAm"},"execution_count":null,"outputs":[]}]} \ No newline at end of file