{ "cells": [ { "cell_type": "markdown", "id": "d0b72877", "metadata": {}, "source": [ "# vqgan-jax-encoding-yfcc100m" ] }, { "cell_type": "markdown", "id": "ba7b31e6", "metadata": {}, "source": [ "Same as `vqgan-jax-encoding-with-captions`, but for YFCC100M.\n", "\n", "This dataset was prepared by @borisdayma in Json lines format." ] }, { "cell_type": "code", "execution_count": 92, "id": "3b59489e", "metadata": {}, "outputs": [], "source": [ "import io\n", "\n", "import requests\n", "from PIL import Image\n", "import numpy as np\n", "from tqdm import tqdm\n", "\n", "import torch\n", "import torchvision.transforms as T\n", "import torchvision.transforms.functional as TF\n", "from torchvision.transforms import InterpolationMode\n", "from torch.utils.data import Dataset, DataLoader\n", "from torchvision.datasets.folder import default_loader\n", "import os\n", "\n", "import jax\n", "from jax import pmap" ] }, { "cell_type": "markdown", "id": "511c3b9e", "metadata": {}, "source": [ "## VQGAN-JAX model" ] }, { "cell_type": "code", "execution_count": 93, "id": "2ca50dc7", "metadata": {}, "outputs": [], "source": [ "from vqgan_jax.modeling_flax_vqgan import VQModel" ] }, { "cell_type": "markdown", "id": "7b60da9a", "metadata": {}, "source": [ "We'll use a VQGAN trained by using Taming Transformers and converted to a JAX model." ] }, { "cell_type": "code", "execution_count": 167, "id": "29ce8b15", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Working with z of shape (1, 256, 16, 16) = 65536 dimensions.\n" ] } ], "source": [ "model = VQModel.from_pretrained(\"flax-community/vqgan_f16_16384\")" ] }, { "cell_type": "markdown", "id": "c7c4c1e6", "metadata": {}, "source": [ "## Dataset" ] }, { "cell_type": "code", "execution_count": 94, "id": "33861477", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "from pathlib import Path" ] }, { "cell_type": "code", "execution_count": 134, "id": "81b19eca", "metadata": {}, "outputs": [], "source": [ "yfcc100m = Path('/home/khali/TPU-Test/YFCC100M_OpenAI_subset')\n", "# Images are 'sharded' from the following directory\n", "yfcc100m_images = yfcc100m/'data'/'data'/'images'\n", "yfcc100m_metadata = yfcc100m/'metadata_YFCC100M.jsonl'\n", "yfcc100m_output = yfcc100m/'metadata_encoded.tsv'" ] }, { "cell_type": "markdown", "id": "1c58bb4a", "metadata": {}, "source": [ "### Cleanup" ] }, { "cell_type": "markdown", "id": "1a14ae3d", "metadata": {}, "source": [ "We need to select entries with images that exist. Otherwise we can't build batches because `Dataloader` does not support `None` in batches. We use Huggingface Datasets, I understand they support threaded reading of jsonl files, and I was running out of memory when using pandas." ] }, { "cell_type": "code", "execution_count": 96, "id": "7811648c", "metadata": {}, "outputs": [], "source": [ "import datasets\n", "from datasets import Dataset, load_dataset" ] }, { "cell_type": "code", "execution_count": 10, "id": "4811a230", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "tcmalloc: large alloc 1254047744 bytes == 0xb2b08000 @ 0x7f9e78632680 0x7f9e78653824 0x585b92 0x504d56 0x56acb6 0x5f5956 0x56acb6 0x5f5956 0x5a8cb3 0x56ae94 0x568d9a 0x68cdc7 0x5ff5d4 0x5c3cb0 0x56aadf 0x501148 0x56c422 0x501148 0x56c422 0x501148 0x504d56 0x56acb6 0x5f5956 0x56aadf 0x5f5956 0x56acb6 0x568d9a 0x5f5b33 0x50b7f8 0x5f2702 0x56c332\n", "tcmalloc: large alloc 1254047744 bytes == 0xfd74e000 @ 0x7f9e78632680 0x7f9e78653824 0x590214 0x586f90 0x56e1f3 0x5f5956 0x56acb6 0x5f5956 0x5a8cb3 0x56ae94 0x568d9a 0x68cdc7 0x5ff5d4 0x5c3cb0 0x56aadf 0x501148 0x56c422 0x501148 0x56c422 0x501148 0x504d56 0x56acb6 0x5f5956 0x56aadf 0x5f5956 0x56acb6 0x568d9a 0x5f5b33 0x50b7f8 0x5f2702 0x56c332\n", "tcmalloc: large alloc 5016190976 bytes == 0x148b42000 @ 0x7f9e78632680 0x7f9e78653824 0x5b9144 0x7f9b2929127e 0x7f9b29291a19 0x7f9b29291886 0x7f9b29291cef 0x7f9b2928f204 0x5f2cc9 0x5f30ff 0x5705f6 0x5f5956 0x56acb6 0x5f5956 0x56acb6 0x5f5956 0x56acb6 0x5f5956 0x5a8cb3 0x56ae94 0x568d9a 0x68cdc7 0x5ff5d4 0x5c3cb0 0x56aadf 0x501148 0x56c422 0x501148 0x56c422 0x501148 0x504d56\n", "tcmalloc: large alloc 5019099136 bytes == 0x273f12000 @ 0x7f9e78632680 0x7f9e78653824 0x5b9144 0x7f9b2929127e 0x7f9b29291a19 0x7f9b29291886 0x7f9b29291cef 0x7f9b2928f204 0x5f2cc9 0x5f30ff 0x5705f6 0x5f5956 0x56acb6 0x5f5956 0x56acb6 0x5f5956 0x56acb6 0x5f5956 0x5a8cb3 0x56ae94 0x568d9a 0x68cdc7 0x5ff5d4 0x5c3cb0 0x56aadf 0x501148 0x56c422 0x501148 0x56c422 0x501148 0x504d56\n", "tcmalloc: large alloc 5019811840 bytes == 0x39f9a8000 @ 0x7f9e78632680 0x7f9e78653824 0x5b9144 0x7f9b2929127e 0x7f9b29291a19 0x7f9b29291886 0x7f9b29291cef 0x7f9b2928f204 0x5f2cc9 0x5f30ff 0x5705f6 0x5f5956 0x56acb6 0x5f5956 0x56acb6 0x5f5956 0x56acb6 0x5f5956 0x5a8cb3 0x56ae94 0x568d9a 0x68cdc7 0x5ff5d4 0x5c3cb0 0x56aadf 0x501148 0x56c422 0x501148 0x56c422 0x501148 0x504d56\n", "tcmalloc: large alloc 5024571392 bytes == 0x4cb4ec000 @ 0x7f9e78632680 0x7f9e78653824 0x5b9144 0x7f9b2929127e 0x7f9b29291a19 0x7f9b29291886 0x7f9b29291cef 0x7f9b2928f204 0x5f2cc9 0x5f30ff 0x5705f6 0x5f5956 0x56acb6 0x5f5956 0x56acb6 0x5f5956 0x56acb6 0x5f5956 0x5a8cb3 0x56ae94 0x568d9a 0x68cdc7 0x5ff5d4 0x5c3cb0 0x56aadf 0x501148 0x56c422 0x501148 0x56c422 0x501148 0x504d56\n", "tcmalloc: large alloc 5021097984 bytes == 0x4cb4ec000 @ 0x7f9e78632680 0x7f9e78653824 0x5b9144 0x7f9b2929127e 0x7f9b29291a19 0x7f9b29291886 0x7f9b29291cef 0x7f9b2928f204 0x5f2cc9 0x5f30ff 0x5705f6 0x5f5956 0x56acb6 0x5f5956 0x56acb6 0x5f5956 0x56acb6 0x5f5956 0x5a8cb3 0x56ae94 0x568d9a 0x68cdc7 0x5ff5d4 0x5c3cb0 0x56aadf 0x501148 0x56c422 0x501148 0x56c422 0x501148 0x504d56\n", "tcmalloc: large alloc 5022818304 bytes == 0x4cb4ec000 @ 0x7f9e78632680 0x7f9e78653824 0x5b9144 0x7f9b2929127e 0x7f9b29291a19 0x7f9b29291886 0x7f9b29291cef 0x7f9b2928f204 0x5f2cc9 0x5f30ff 0x5705f6 0x5f5956 0x56acb6 0x5f5956 0x56acb6 0x5f5956 0x56acb6 0x5f5956 0x5a8cb3 0x56ae94 0x568d9a 0x68cdc7 0x5ff5d4 0x5c3cb0 0x56aadf 0x501148 0x56c422 0x501148 0x56c422 0x501148 0x504d56\n", "tcmalloc: large alloc 5020794880 bytes == 0x4cb4ec000 @ 0x7f9e78632680 0x7f9e78653824 0x5b9144 0x7f9b2929127e 0x7f9b29291a19 0x7f9b29291886 0x7f9b29291cef 0x7f9b2928f204 0x5f2cc9 0x5f30ff 0x5705f6 0x5f5956 0x56acb6 0x5f5956 0x56acb6 0x5f5956 0x56acb6 0x5f5956 0x5a8cb3 0x56ae94 0x568d9a 0x68cdc7 0x5ff5d4 0x5c3cb0 0x56aadf 0x501148 0x56c422 0x501148 0x56c422 0x501148 0x504d56\n", "tcmalloc: large alloc 5019451392 bytes == 0x39f9a8000 @ 0x7f9e78632680 0x7f9e78653824 0x5b9144 0x7f9b2929127e 0x7f9b29291a19 0x7f9b29291886 0x7f9b29291cef 0x7f9b2928f204 0x5f2cc9 0x5f30ff 0x5705f6 0x5f5956 0x56acb6 0x5f5956 0x56acb6 0x5f5956 0x56acb6 0x5f5956 0x5a8cb3 0x56ae94 0x568d9a 0x68cdc7 0x5ff5d4 0x5c3cb0 0x56aadf 0x501148 0x56c422 0x501148 0x56c422 0x501148 0x504d56\n", "tcmalloc: large alloc 5020565504 bytes == 0x4cb4ec000 @ 0x7f9e78632680 0x7f9e78653824 0x5b9144 0x7f9b2929127e 0x7f9b29291a19 0x7f9b29291886 0x7f9b29291cef 0x7f9b2928f204 0x5f2cc9 0x5f30ff 0x5705f6 0x5f5956 0x56acb6 0x5f5956 0x56acb6 0x5f5956 0x56acb6 0x5f5956 0x5a8cb3 0x56ae94 0x568d9a 0x68cdc7 0x5ff5d4 0x5c3cb0 0x56aadf 0x501148 0x56c422 0x501148 0x56c422 0x501148 0x504d56\n", "tcmalloc: large alloc 5012561920 bytes == 0x273f12000 @ 0x7f9e78632680 0x7f9e78653824 0x5b9144 0x7f9b2929127e 0x7f9b29291a19 0x7f9b29291886 0x7f9b29291cef 0x7f9b2928f204 0x5f2cc9 0x5f30ff 0x5705f6 0x5f5956 0x56acb6 0x5f5956 0x56acb6 0x5f5956 0x56acb6 0x5f5956 0x5a8cb3 0x56ae94 0x568d9a 0x68cdc7 0x5ff5d4 0x5c3cb0 0x56aadf 0x501148 0x56c422 0x501148 0x56c422 0x501148 0x504d56\n", "tcmalloc: large alloc 5021835264 bytes == 0x5f6cba000 @ 0x7f9e78632680 0x7f9e78653824 0x5b9144 0x7f9b2929127e 0x7f9b29291a19 0x7f9b29291886 0x7f9b29291cef 0x7f9b2928f204 0x5f2cc9 0x5f30ff 0x5705f6 0x5f5956 0x56acb6 0x5f5956 0x56acb6 0x5f5956 0x56acb6 0x5f5956 0x5a8cb3 0x56ae94 0x568d9a 0x68cdc7 0x5ff5d4 0x5c3cb0 0x56aadf 0x501148 0x56c422 0x501148 0x56c422 0x501148 0x504d56\n", "tcmalloc: large alloc 5017436160 bytes == 0x273f12000 @ 0x7f9e78632680 0x7f9e78653824 0x5b9144 0x7f9b2929127e 0x7f9b29291a19 0x7f9b29291886 0x7f9b29291cef 0x7f9b2928f204 0x5f2cc9 0x5f30ff 0x5705f6 0x5f5956 0x56acb6 0x5f5956 0x56acb6 0x5f5956 0x56acb6 0x5f5956 0x5a8cb3 0x56ae94 0x568d9a 0x68cdc7 0x5ff5d4 0x5c3cb0 0x56aadf 0x501148 0x56c422 0x501148 0x56c422 0x501148 0x504d56\n" ] } ], "source": [ "# The metadata is too bog to load into memory at once, so chopping it into chunks\n", "chunk_size=1000000\n", "batch_no=1\n", "for chunk in pd.read_json(yfcc100m_metadata, orient=\"records\", lines=True,chunksize=chunk_size):\n", " chunk.to_csv('./chunks/chunk'+str(batch_no)+'.tsv', sep=\"\\t\", index=False)\n", " batch_no+=1" ] }, { "cell_type": "code", "execution_count": 25, "id": "46b2f083", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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1124636144124324682@N01mharrsch2004-11-03 23:04:02.01099523042NaNAn+ornate+Roman+urnPhotographed+at+the+%3Ca+href%3D%22http%3A%2F%...ancient,baltimore,burial,death,empire,funeral,...NaN...http://creativecommons.org/licenses/by-nc-sa/2.0/11cf37054610cf37054610jpg0d29f01b149167d683f9ddde464bb3dbAn ornate Roman urnPhotographed at the Walters Art Museum, Baltim...
2125159951035803024@N01bmitd672004-10-30 17:09:32.01099538888Canon+PowerShot+S30Jai+%26+Tara+on+the+CumberlandAnother+trip+for+the+happy+couple.blue+heron,cumberland+river,jai,tara,tennesseeNaN...http://creativecommons.org/licenses/by-nc-sa/2.0/114a4234e32c4a4234e32cjpg0d296e9e34bdae41edb6c679ff824ab2aJai & Tara on the CumberlandAnother trip for the happy couple.
3234858773621375@N00Thom+Watson2004-12-18 21:08:09.01103497228SONY+DSC-W1Castle+gate+-+%22lite-brited%22Taken+at+the+Miracle+of+Lights+display+in+Cent...bullrunpark,castle,centreville,christmas,decor...NaN...http://creativecommons.org/licenses/by-nc-sa/2.0/217162c974c37162c974c3jpg0d29ce96395848478b1e8396e44899Castle gate - \"lite-brited\"Taken at the Miracle of Lights display in Cent...
4351604748600072071@N01doctor+paradox2005-01-18 16:44:18.01106084658NaNA+Picture+Share%21Tabularcameraphone,moblog,unfoundNaN...http://creativecommons.org/licenses/by-nc-sa/2.0/31663e0d8b3d663e0d8b3djpg0d29abf32c4e12ff881f975b70e0cec0A Picture Share!Tabular
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999995464865105424511045@N04mtfrazier2010-05-02 15:47:45.01275083371Canon+EOS+50DU.S.+Navy+Blue+Angels%3A+20102+May+2010%0ASunday%0ASt.+Joseph%2C+MissouriNaNNaN...http://creativecommons.org/licenses/by-nc-nd/2.0/407252d12d73fb0dd5856ea42jpg060fa2911cb81eb25b356e9fee978aefU.S. Navy Blue Angels: 20102 May 2010 Sunday St. Joseph, Missouri
999996465213099621963865@N04GRAB1.02010-05-29 19:23:10.01275200833SONY+DSLR-A230Attempts+on+Her+LifeBAPA+1+production+of+Martin+Crimp%27s+Attempts...NaNNaN...http://creativecommons.org/licenses/by-nc-nd/2.0/4003588891215792f46599456jpg060f5ef5ce4c2d24566226abebd67d4Attempts on Her LifeBAPA 1 production of Martin Crimp's Attempts o...
999997465256833964025277@N001Sock2010-05-13 15:38:37.01275234267Canon+EOS+DIGITAL+REBEL+XTCarlsbad+Caverns+3%E2%99%A5%E2%99%A5%E2%99%A5%E2%99%A5%E2%99%A5%...carlsbad,carlsbad+caverns,cave,faa,new+mexico,...NaN...http://creativecommons.org/licenses/by-nc-nd/2.0/401050a1808a69ecf6d348e3djpg060f029482d1d1028fda5281daf498fCarlsbad Caverns 3♥♥♥♥♥♥♥ Interested in purchasing this photogra...
999998465311089520483509@N00subberculture2010-05-30 15:37:05.01275245596Canon+DIGITAL+IXUS+40WantIsn%27t+that+gorgeous%3F2010,edinburgh+museum,may,phonebox,woodNaN...http://creativecommons.org/licenses/by-sa/2.0/4066577c3b3a254c4697e1511jpg060f72775f433cf8de3efaeb431866153WantIsn't that gorgeous?
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1000000 rows × 26 columns

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" ], "text/plain": [ " photoid uid unickname datetaken \\\n", "0 137943 48600072071@N01 doctor+paradox 2004-08-01 18:13:06.0 \n", "1 1246361 44124324682@N01 mharrsch 2004-11-03 23:04:02.0 \n", "2 1251599 51035803024@N01 bmitd67 2004-10-30 17:09:32.0 \n", "3 2348587 73621375@N00 Thom+Watson 2004-12-18 21:08:09.0 \n", "4 3516047 48600072071@N01 doctor+paradox 2005-01-18 16:44:18.0 \n", "... ... ... ... ... \n", "999995 4648651054 24511045@N04 mtfrazier 2010-05-02 15:47:45.0 \n", "999996 4652130996 21963865@N04 GRAB1.0 2010-05-29 19:23:10.0 \n", "999997 4652568339 64025277@N00 1Sock 2010-05-13 15:38:37.0 \n", "999998 4653110895 20483509@N00 subberculture 2010-05-30 15:37:05.0 \n", "999999 4655503987 8457193@N07 zackojones 2010-05-30 15:34:58.0 \n", "\n", " dateuploaded capturedevice \\\n", "0 1091409186 NaN \n", "1 1099523042 NaN \n", "2 1099538888 Canon+PowerShot+S30 \n", "3 1103497228 SONY+DSC-W1 \n", "4 1106084658 NaN \n", "... ... ... \n", "999995 1275083371 Canon+EOS+50D \n", "999996 1275200833 SONY+DSLR-A230 \n", "999997 1275234267 Canon+EOS+DIGITAL+REBEL+XT \n", "999998 1275245596 Canon+DIGITAL+IXUS+40 \n", "999999 1275310230 Canon+EOS+7D \n", "\n", " title \\\n", "0 A+Picture+Share%21 \n", "1 An+ornate+Roman+urn \n", "2 Jai+%26+Tara+on+the+Cumberland \n", "3 Castle+gate+-+%22lite-brited%22 \n", "4 A+Picture+Share%21 \n", "... ... \n", "999995 U.S.+Navy+Blue+Angels%3A+2010 \n", "999996 Attempts+on+Her+Life \n", "999997 Carlsbad+Caverns+3 \n", "999998 Want \n", "999999 Summertime \n", "\n", " description \\\n", "0 Antenna \n", "1 Photographed+at+the+%3Ca+href%3D%22http%3A%2F%... \n", "2 Another+trip+for+the+happy+couple. \n", "3 Taken+at+the+Miracle+of+Lights+display+in+Cent... \n", "4 Tabular \n", "... ... \n", "999995 2+May+2010%0ASunday%0ASt.+Joseph%2C+Missouri \n", "999996 BAPA+1+production+of+Martin+Crimp%27s+Attempts... \n", "999997 %E2%99%A5%E2%99%A5%E2%99%A5%E2%99%A5%E2%99%A5%... \n", "999998 Isn%27t+that+gorgeous%3F \n", "999999 You+gotta+love+it%21 \n", "\n", " usertags machinetags ... \\\n", "0 cameraphone,cayugaheights,green,hydrant,ithaca... NaN ... \n", "1 ancient,baltimore,burial,death,empire,funeral,... NaN ... \n", "2 blue+heron,cumberland+river,jai,tara,tennessee NaN ... \n", "3 bullrunpark,castle,centreville,christmas,decor... NaN ... \n", "4 cameraphone,moblog,unfound NaN ... \n", "... ... ... ... \n", "999995 NaN NaN ... \n", "999996 NaN NaN ... \n", "999997 carlsbad,carlsbad+caverns,cave,faa,new+mexico,... NaN ... \n", "999998 2010,edinburgh+museum,may,phonebox,wood NaN ... \n", "999999 georgia,savannah,united+states,us NaN ... \n", "\n", " licenseurl serverid farmid \\\n", "0 http://creativecommons.org/licenses/by-nc-sa/2.0/ 1 1 \n", "1 http://creativecommons.org/licenses/by-nc-sa/2.0/ 1 1 \n", "2 http://creativecommons.org/licenses/by-nc-sa/2.0/ 1 1 \n", "3 http://creativecommons.org/licenses/by-nc-sa/2.0/ 2 1 \n", "4 http://creativecommons.org/licenses/by-nc-sa/2.0/ 3 1 \n", "... ... ... ... \n", "999995 http://creativecommons.org/licenses/by-nc-nd/2.0/ 4072 5 \n", "999996 http://creativecommons.org/licenses/by-nc-nd/2.0/ 4003 5 \n", "999997 http://creativecommons.org/licenses/by-nc-nd/2.0/ 4010 5 \n", "999998 http://creativecommons.org/licenses/by-sa/2.0/ 4066 5 \n", "999999 http://creativecommons.org/licenses/by-nc-sa/2.0/ 4043 5 \n", "\n", " secret secretoriginal ext marker \\\n", "0 1650c7cdc6 1650c7cdc6 jpg 0 \n", "1 cf37054610 cf37054610 jpg 0 \n", "2 4a4234e32c 4a4234e32c jpg 0 \n", "3 7162c974c3 7162c974c3 jpg 0 \n", "4 663e0d8b3d 663e0d8b3d jpg 0 \n", "... ... ... ... ... \n", "999995 2d12d73fb0 dd5856ea42 jpg 0 \n", "999996 8889121579 2f46599456 jpg 0 \n", "999997 0a1808a69e cf6d348e3d jpg 0 \n", "999998 77c3b3a254 c4697e1511 jpg 0 \n", "999999 caff543bfe f60952ac4d jpg 0 \n", "\n", " key title_clean \\\n", "0 d29e7c6a3028418c64eb15e3cf577c2 A Picture Share! \n", "1 d29f01b149167d683f9ddde464bb3db An ornate Roman urn \n", "2 d296e9e34bdae41edb6c679ff824ab2a Jai & Tara on the Cumberland \n", "3 d29ce96395848478b1e8396e44899 Castle gate - \"lite-brited\" \n", "4 d29abf32c4e12ff881f975b70e0cec0 A Picture Share! \n", "... ... ... \n", "999995 60fa2911cb81eb25b356e9fee978aef U.S. Navy Blue Angels: 2010 \n", "999996 60f5ef5ce4c2d24566226abebd67d4 Attempts on Her Life \n", "999997 60f029482d1d1028fda5281daf498f Carlsbad Caverns 3 \n", "999998 60f72775f433cf8de3efaeb431866153 Want \n", "999999 60f687e11b913bce461e9525d8047e0 Summertime \n", "\n", " description_clean \n", "0 Antenna \n", "1 Photographed at the Walters Art Museum, Baltim... \n", "2 Another trip for the happy couple. \n", "3 Taken at the Miracle of Lights display in Cent... \n", "4 Tabular \n", "... ... \n", "999995 2 May 2010 Sunday St. Joseph, Missouri \n", "999996 BAPA 1 production of Martin Crimp's Attempts o... \n", "999997 ♥♥♥♥♥♥♥ Interested in purchasing this photogra... \n", "999998 Isn't that gorgeous? \n", "999999 You gotta love it! \n", "\n", "[1000000 rows x 26 columns]" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# looking up at a chunk\n", "pd.read_csv(\"./chunks/chunk1.tsv\", sep=\"\\t\")" ] }, { "cell_type": "code", "execution_count": 98, "id": "c51c5597", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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keytitle_cleandescription_cleanext
0d29e7c6a3028418c64eb15e3cf577c2A Picture Share!Antennajpg
1d29f01b149167d683f9ddde464bb3dbAn ornate Roman urnPhotographed at the Walters Art Museum, Baltim...jpg
2d296e9e34bdae41edb6c679ff824ab2aJai & Tara on the CumberlandAnother trip for the happy couple.jpg
3d29ce96395848478b1e8396e44899Castle gate - \"lite-brited\"Taken at the Miracle of Lights display in Cent...jpg
4d29abf32c4e12ff881f975b70e0cec0A Picture Share!Tabularjpg
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" ], "text/plain": [ " key title_clean \\\n", "0 d29e7c6a3028418c64eb15e3cf577c2 A Picture Share! \n", "1 d29f01b149167d683f9ddde464bb3db An ornate Roman urn \n", "2 d296e9e34bdae41edb6c679ff824ab2a Jai & Tara on the Cumberland \n", "3 d29ce96395848478b1e8396e44899 Castle gate - \"lite-brited\" \n", "4 d29abf32c4e12ff881f975b70e0cec0 A Picture Share! \n", "\n", " description_clean ext \n", "0 Antenna jpg \n", "1 Photographed at the Walters Art Museum, Baltim... jpg \n", "2 Another trip for the happy couple. jpg \n", "3 Taken at the Miracle of Lights display in Cent... jpg \n", "4 Tabular jpg " ] }, "execution_count": 98, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Looking at a chunk with only the relevant columns that we need\n", "df = pd.read_csv(\"./chunks/chunk1.tsv\", sep=\"\\t\")[[\"key\", \"title_clean\", \"description_clean\", \"ext\"]]\n", "df.head()" ] }, { "cell_type": "markdown", "id": "cc1668f8", "metadata": {}, "source": [ "### Grabbing each chunks from the folder, cleaning it up, only taking the entries which image exist and appending it to the global df" ] }, { "cell_type": "code", "execution_count": null, "id": "abbcccf3", "metadata": {}, "outputs": [], "source": [ "# the function that helps us to decide whether an image with certain id exists in storage, we only take the ones that we have the images for\n", "def image_exists(item):\n", " name, _, _, ext, _ = item\n", " root=str(yfcc100m_images)\n", " image_path = (Path(root)/name[0:3]/name[3:6]/name).with_suffix(\".\"+ext)\n", " if image_path.exists():\n", " return True\n", " else:\n", " return None" ] }, { "cell_type": "code", "execution_count": 86, "id": "44fa86ab", "metadata": {}, "outputs": [], "source": [ "# This cell does it all, grabs each chunk, cleans it up based on image existing condition, etc.\n", "global_df = pd.DataFrame()\n", "chunks_dir = \"./chunks\"\n", "for filename in os.listdir(chunks_dir):\n", " df = pd.read_csv(f\"./chunks/{str(filename)}\", sep=\"\\t\")[[\"key\", \"title_clean\", \"description_clean\", \"ext\"]]\n", " df['caption'] = df[\"title_clean\"]+\". \"+df['description_clean']\n", " df['is_exist'] = df.apply(image_exists, axis=1)\n", " df = df.dropna()[[\"key\", \"caption\"]]\n", " df.columns = ['image_file', 'caption']\n", " global_df = global_df.append(df, ignore_index=True)" ] }, { "cell_type": "code", "execution_count": 89, "id": "45024fdc", "metadata": {}, "outputs": [], "source": [ "# saving the tsv to disk\n", "global_df.to_csv('./chunks/YFCC_subset_clean.tsv', sep=\"\\t\", index=False)" ] }, { "cell_type": "code", "execution_count": 101, "id": "dca4eb73", "metadata": {}, "outputs": [], "source": [ "# loading the tsv from disk (for explicitness, also my electricity was gone, glad it happened after I saved to the disk :( )\n", "\n", "dataset = pd.read_csv(f\"./chunks/YFCC_subset_clean.tsv\", sep=\"\\t\")" ] }, { "cell_type": "code", "execution_count": 153, "id": "a511264a", "metadata": {}, "outputs": [], "source": [ "\"\"\"\n", "Luke Melas-Kyriazi's dataset.py's modified version for YFCC\n", "\"\"\"\n", "import warnings\n", "from typing import Optional, Callable\n", "from pathlib import Path\n", "import numpy as np\n", "import torch\n", "import pandas as pd\n", "from torch.utils.data import Dataset\n", "from torchvision.datasets.folder import default_loader\n", "from PIL import ImageFile\n", "from PIL.Image import DecompressionBombWarning\n", "ImageFile.LOAD_TRUNCATED_IMAGES = True\n", "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", "warnings.filterwarnings(\"ignore\", category=DecompressionBombWarning)\n", "\n", "\n", "class CaptionDataset(Dataset):\n", " \"\"\"\n", " A PyTorch Dataset class for (image, texts) tasks. Note that this dataset \n", " returns the raw text rather than tokens. This is done on purpose, because\n", " it's easy to tokenize a batch of text after loading it from this dataset.\n", " \"\"\"\n", "\n", " def __init__(self, *, images_root: str, captions_path: str, text_transform: Optional[Callable] = None, \n", " image_transform: Optional[Callable] = None, image_transform_type: str = 'torchvision',\n", " include_captions: bool = True):\n", " \"\"\"\n", " :param images_root: folder where images are stored\n", " :param captions_path: path to csv that maps image filenames to captions\n", " :param image_transform: image transform pipeline\n", " :param text_transform: image transform pipeline\n", " :param image_transform_type: image transform type, either `torchvision` or `albumentations`\n", " :param include_captions: Returns a dictionary with `image`, `text` if `true`; otherwise returns just the images.\n", " \"\"\"\n", "\n", " # Base path for images\n", " self.images_root = Path(images_root)\n", "\n", " # Load captions as DataFrame\n", " self.captions = pd.read_csv(f\"./chunks/YFCC_subset_clean.tsv\", sep=\"\\t\")\n", " self.captions['image_file'] = self.captions['image_file'].astype(str)\n", "\n", " # PyTorch transformation pipeline for the image (normalizing, etc.)\n", " self.text_transform = text_transform\n", " self.image_transform = image_transform\n", " self.image_transform_type = image_transform_type.lower()\n", " assert self.image_transform_type in ['torchvision', 'albumentations']\n", "\n", " # Total number of datapoints\n", " self.size = len(self.captions)\n", "\n", " # Return image+captions or just images\n", " self.include_captions = include_captions\n", " \n", " def image_exists(item):\n", " name, caption = item\n", " root=str(self.images_root)\n", " image_path = (Path(root)/name[0:3]/name[3:6]/name).with_suffix(\".jpg\")\n", "\n", " return image_path.exists()\n", "\n", " def verify_that_all_images_exist(self):\n", " for image_file in self.captions['image_file']:\n", " if not image_exists:\n", " print(f'file does not exist: {p}')\n", "\n", " def _get_raw_image(self, i):\n", " name = self.captions.iloc[i]['image_file']\n", " image_path = (Path(self.images_root)/name[0:3]/name[3:6]/name).with_suffix(\".jpg\")\n", " image = default_loader(image_path)\n", " return image\n", "\n", " def _get_raw_text(self, i):\n", " return self.captions.iloc[i]['caption']\n", "\n", " def __getitem__(self, i):\n", " image = self._get_raw_image(i)\n", " caption = self._get_raw_text(i)\n", " if self.image_transform is not None:\n", " if self.image_transform_type == 'torchvision':\n", " image = self.image_transform(image)\n", " elif self.image_transform_type == 'albumentations':\n", " image = self.image_transform(image=np.array(image))['image']\n", " else:\n", " raise NotImplementedError(f\"{self.image_transform_type=}\")\n", " return {'image': image, 'text': caption} if self.include_captions else image\n", "\n", " def __len__(self):\n", " return self.size\n", "\n", "\n", "if __name__ == \"__main__\":\n", " import albumentations as A\n", " from albumentations.pytorch import ToTensorV2\n", " from transformers import AutoTokenizer\n", " \n", "\n", " images_root = \"/home/khali/TPU-Test/YFCC100M_OpenAI_subset/data/data/images\"\n", " captions_path = './YFCC_subset_clean.tsv'\n", " image_size = 256\n", " \n", " # Create transforms\n", " def image_transform(image):\n", " s = min(image.size)\n", " r = image_size / s\n", " s = (round(r * image.size[1]), round(r * image.size[0]))\n", " image = TF.resize(image, s, interpolation=InterpolationMode.LANCZOS)\n", " image = TF.center_crop(image, output_size = 2 * [image_size])\n", " image = torch.unsqueeze(T.ToTensor()(image), 0)\n", " image = image.permute(0, 2, 3, 1).numpy()\n", " return image\n", " \n", " # Create dataset\n", " dataset = CaptionDataset(\n", " images_root=images_root,\n", " captions_path=captions_path,\n", " image_transform=image_transform,\n", " image_transform_type='torchvision',\n", " include_captions=False\n", " )" ] }, { "cell_type": "code", "execution_count": 155, "id": "cc922704", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2483316" ] }, "execution_count": 155, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(dataset)" ] }, { "cell_type": "code", "execution_count": 156, "id": "6e47ba46", "metadata": {}, "outputs": [], "source": [ "dataloader = DataLoader(dataset, batch_size=32, num_workers=4)" ] }, { "cell_type": "code", "execution_count": 1, "id": "c8a130eb", "metadata": {}, "outputs": [], "source": [ "# looking at a batch\n", "next(iter(dataloader))" ] }, { "cell_type": "code", "execution_count": null, "id": "c192fd44", "metadata": {}, "outputs": [], "source": [ "# import matplotlib.pyplot as plt\n", "# for tensor_image, _ in dataloader:\n", "# print(tensor_image)\n", "# plt.imshow(tensor_image.permute(1, 2, 0))\n", "# break" ] }, { "cell_type": "markdown", "id": "62ad01c3", "metadata": {}, "source": [ "## Encoding" ] }, { "cell_type": "code", "execution_count": 158, "id": "88f36d0b", "metadata": {}, "outputs": [], "source": [ "def encode(model, batch):\n", "# print(\"jitting encode function\")\n", " _, indices = model.encode(batch)\n", " return indices" ] }, { "cell_type": "code", "execution_count": 160, "id": "1f35f0cb", "metadata": {}, "outputs": [], "source": [ "def superbatch_generator(dataloader, num_tpus):\n", " iter_loader = iter(dataloader)\n", " for batch in iter_loader:\n", " superbatch = [batch.squeeze(1)]\n", " try:\n", " for b in range(num_tpus-1):\n", " batch = next(iter_loader)\n", " if batch is None:\n", " break\n", " # Skip incomplete last batch\n", " if batch.shape[0] == dataloader.batch_size:\n", " superbatch.append(batch.squeeze(1))\n", " except StopIteration:\n", " pass\n", " superbatch = torch.stack(superbatch, axis=0)\n", " yield superbatch" ] }, { "cell_type": "code", "execution_count": 170, "id": "2210705b", "metadata": {}, "outputs": [], "source": [ "import os\n", "\n", "def encode_captioned_dataset(dataset, output_tsv, batch_size=32, num_workers=16):\n", " if os.path.isfile(output_tsv):\n", " print(f\"Destination file {output_tsv} already exists, please move away.\")\n", " return\n", " \n", " num_tpus = 8 \n", " dataloader = DataLoader(dataset, batch_size=batch_size, num_workers=num_workers)\n", " superbatches = superbatch_generator(dataloader, num_tpus=num_tpus)\n", " \n", " p_encoder = pmap(lambda batch: encode(model, batch))\n", "\n", " # We save each superbatch to avoid reallocation of buffers as we process them.\n", " # We keep the file open to prevent excessive file seeks.\n", " with open(output_tsv, \"w\") as file:\n", " iterations = len(dataset) // (batch_size * num_tpus)\n", " for n in tqdm(range(iterations)):\n", " superbatch = next(superbatches)\n", " encoded = p_encoder(superbatch.numpy())\n", " encoded = encoded.reshape(-1, encoded.shape[-1])\n", "\n", " # Extract fields from the dataset internal `captions` property, and save to disk\n", " start_index = n * batch_size * num_tpus\n", " end_index = (n+1) * batch_size * num_tpus\n", " paths = dataset.captions[\"image_file\"][start_index:end_index].values\n", " captions = dataset.captions[\"caption\"][start_index:end_index].values\n", " encoded_as_string = list(map(lambda item: np.array2string(item, separator=',', max_line_width=50000, formatter={'int':lambda x: str(x)}), encoded))\n", " batch_df = pd.DataFrame.from_dict({\"image_file\": paths, \"caption\": captions, \"encoding\": encoded_as_string})\n", " batch_df.to_csv(file, sep='\\t', header=(n==0), index=None)" ] }, { "cell_type": "code", "execution_count": 171, "id": "7704863d", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4850/4850 [2:27:51<00:00, 1.83s/it]\n" ] } ], "source": [ "encode_captioned_dataset(dataset, yfcc100m_output, batch_size=64, num_workers=16)" ] }, { "cell_type": "markdown", "id": "8953dd84", "metadata": {}, "source": [ "----" ] } ], "metadata": { "interpreter": { "hash": "db471c52d602b4f5f40ecaf278e88ccfef85c29d0a1a07185b0d51fc7acf4e26" }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 5 }