{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "id": "NIgLaonXpNe-" }, "outputs": [], "source": [ "# !pip install -U sentence-transformers -q" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "QdBn7nrGZhA5", "outputId": "7817b1b2-440d-4aff-f1a8-1c9842966f4c" }, "outputs": [], "source": [ "# from google.colab import drive\n", "# drive.mount('/content/drive')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "id": "iUebtXJXCDp2" }, "outputs": [], "source": [ "# !unzip -q '/content/drive/MyDrive/flowers/flowers.zip' -d '/content/flowers'" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "id": "_rWYs7_s61qi" }, "outputs": [], "source": [ "import os\n", "import torchvision.transforms as tt\n", "from torch.utils.data import Dataset, DataLoader\n", "from torchvision.datasets import ImageFolder\n", "import torch\n", "import torch.nn as nn\n", "from tqdm.notebook import tqdm\n", "import torch.nn.functional as F\n", "import matplotlib.pyplot as plt\n", "import statistics\n", "import pandas as pd\n", "import numpy as np\n", "import random\n", "from PIL import Image\n", "from sentence_transformers import SentenceTransformer" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 876, "referenced_widgets": [ "25c6180f8dc44e30a90e41ab4444d568", "eabc53603ec2414d9dd723b7664282fb", "8f1e4514d00c4cd2bb0eb4983d8e8681", "81dae539bd60451f9fcecbada8b3caad", "250720543afa4c4d8f9370719dfa403f", "a5b146a212a64ee6bf3990ac35eb0da0", "c3b243c440f74e0ca605ebd2bff6697f", "fcf02750429b4870ad8477dc49919915", "2af223c430604402b6d5a83487da874d", "d742e9ad5bae4242b8261c4041f12021", "3e64d8147e83433d9083d0af2e080f9a", "6cf44825558a4773934ce077310d9ed2", "ec99a482c0e04ea3a772d061b4ca508a", "1683b59959674f1fbc79836aea1bac58", "e4e847d538e24c9f98f13f5e7cde1305", "07501c4e5279462bb24796eb6cda045f", "73c6019167ec48e1ba5cddf5a06c0af1", "61bf7f590a6942cb8f66e73e9210263f", "77578aea03b84d12a6ea42e2ac0e99f5", "84f64213852f4140ab08d6b23d34b170", "72240fb42b364fdc8c6ca53ec34d38d2", "7e19018c23024d6cad81865ce5ec8034", "5c39ba0ec5ab4d43b9ac79e73e4391ee", "be71de18392a4d55bae5d416c63bcd6d", "6672eba260464d9dbcc934811ffe3178", "f10260fb28544d1a9abe57e3d3e4736d", "ed80fae7192a41e48f1d074becde0a41", "132a9812bd56432ea91385d6a744eba0", "96faed2eb0654b77b2b67f7784e3e456", "25f6ee6b422b4bb190c045717f1bf706", "e6f942b491594ce68915ea3acfe0b88f", "5ec0af63112f4e5384c377ea611d6559", "c9f92b74471c49e5bf73a3e83414ffe3", "7786857a32814f60921c9872ba2fa14e", "12843ae913d347678c141fd8af5134d1", "1c13d5c147b44975a83173fd2048c662", "35004b96cc3044d59134d10d404ef83e", "cdb222db14154ef096453b9cfc35cde6", "dc3d1cd9b4454790a4da86cebd8549cb", "81a60c43999c4b178d8e6dd7c59cd9dc", "00c98b877348475998168bd2b63127bc", "00dd6eb29db64903aa8ef21062f2a928", "cc7957e179544ae9a637eddd868c2ce7", "fa926fc83d214083adf4ba5b8b5a59e2", "2f64dbf8a36f4c6dbfc32507a286d790", "06d9080b17c1463bbdda4e22bd37b11c", "d9db7ba1e1724110b64ca6ca38454cdd", "a9e051d9de6d4a00820eca179fbfb2fb", "9a05ee64ad774ffdb264cb5e2aafe67f", "27eb95a95cbe48ce8c7c3e50ff8030ff", "0769f82f53924848ad6b43494a6f1891", "b693c764f41a4d098c525bf3ef390d8e", "2615abb3e24b4caba00dbe2b05fea6c9", "db0b0b2072514e41b8eeb5057cc36d2a", "61f683c6bb9b492481461cff87ee32a4", "5fe921809e364140a9089b99a352d217", "ac9468c3b2a94efdbe2f582b075d52ae", "45095d810fb54ff6ba96e4e1e95538ee", "f264279c317b4acaa8066c9002fbc3d4", "e29722635f164791817a6778f4739fc1", "dafa004dea90413ca4fc01363e1bcfa9", "db5fef6560054d2c82c15769f3ea66d1", "86e463ca463b47abaae156eeadbeb10f", "4aa1677debee44fa97ea32b3a5e6808d", "a92bb42784f24843a6d5cf3709c7e779", "3db82b2b2e67431fb622e029d4c4f0c0", "87f085c4c1404f05badaac09761b83aa", "c9641bbf19e748e29c3de6628b565148", "26b3182505a64c5b9bb95f2f3ae747d6", "bda1923e2e59451db1842ac38b320da8", "1d109db062fe4b1d9831d14481d49e35", "cb0eec85aa6747abb04683b58eb9b864", "64a65d76766f4eeb94467f69cfe5f8fc", "eac641a7f1d5450c97c4c2be985b573b", "82ff48528042411fbba4b08886de2dc3", "77f3219ed10942ccb6b90932dd255836", "433dd4b858254fb7a2ba6c06f9668bcc", "602a5741b5ea4f3694d9843d8bf01613", "969f469d4f014738b61857319ef7c197", "8fe1183fd8f24ace89110b2ef9492b16", "23992a132eb64cdeb5979423d5b9a2d9", "15f701803de746c29c6b9c787b36c397", "a8893c31fc1c4c0e84be007524b66e9e", "ba72d3691bd046d2a4881c3b4034323a", "78a84d1619e542ccb9a10b649a497ddb", "e935c00045034186b35e271d7f3f757a", "0d0fc9a8b663450a9918ed74238c1602", "103bc72468744d4daa3b952d74e3d544", "2834e62c02d94e23b2d09ea8de9a9c7f", "96ed06ba098d4ae29485b104f2225676", "33f2425803414c58b9194c7db50d5d09", "d88b1b3d8f364b19b9ae79da6097541a", "f6523a1ae3c14433b3de892ac2f8eadb", "41a14d4b0be245bc9cebea9fd5a7d47e", "84e7a1867f484913b39a574d4ad5ac51", "78d1043ab3cc4a77afb54ded103e5784", "511a66a7c7da4eacad8d28accb29aa17", "cb7f35b8218c4a698014b60a1c1a1002", "00610bb053b34cafbe0be174129d40a4", "45552917ea2947928703b0f8eb6ff53b", "fdb5027996f0439292ecc0391270cdcf", "2e6ab9d647614cfeae8ff1cbe1e44620", "34a8a55386274401baf279176e1aa46b", "54e4b026bf494ed89689f502eb36e9a5", "a1817a25664742b6a5a119836f249589", "5ea3b73239a34de0baa33692b6b35985", "d2e2cae1e58e4771b272e167a6bbdb0a", "94fde80d08094283ab80a19bac0d054f", "48bc8acf3d654df5ac1d1377d512ebaf", "81e11f721bc7493f897dfb75e93e9196", "add028af7e9e4ee4a8e3033718961dbd", "253ca4448b4c4aab95141e09b85e26f3", "412362b2688c425080cc5ee45d75eb41", "4f5375ffb8c04fa3baeb66a651199640", "c643d5bb61ad4209bae9aa2eb9e9e650", "b8720bd3ce5b4817b8d2baaae535a271", "f87d2f2745df4fb1ae3f44883823226a", "34f077cd00c64a29a6bbf50a8487d46e", "962d42fbfe5345678d9f0cf9bba9e7d6", "e69190abaede4aa98b3ae67d32443731", "b8bb5646694d4e7bbdbf7892dbe5e3cd", "45fb228f529949c8b46e4081aca1af00", "ef8fb6bc382d4d6aac880d771cd9ff3a", "afb52c5cfc3549d1bf352b867d57beda", "4c8de558a27b4e3cb435c2cdc5cbf561", "efb38a4c124b4c178ca498e7587ccce8", "e4c73a16430740738bb78ea3d28c27fa", "98d0f114f5f84a188e1d5e979aee6781", "9676b37a5cc04322af4092a23c439175", "68524308df594cbd882da4e27204c3f0", "d440895efad644c5a3bdb4890344f6f9", "4a37554b648e4d10b937dff669663f8f", "ff01e75060ab426c85005d5f0f8a9bd1", "ef30dae380f6496b89fef53e548111ec", "651570ffea8c4998b0fa833cf9692b57", "b023634e623e410a9a05ccd18940bda4", "8e25e34caf9648aa82bc599cf37182c3", "674aab817cf648b3b3ee89d9ccf95d93", "a20c7e7a1bb74bcd86bdf723de710133", "c7320f872f8f4d4ba32dbb737d4dc19b", "bedb67dd327f4154b27498f72427f769", "3604273ef5474e7b8c7bdd8beef1fbcb", "4b901a36473b4779a2adda6efe5047aa", "c51fed90f05647c6aca30ed2862eed0f", "daed59cbb71f4f02b8a8c1783314097c", "48c76e9cff9442b9a1dd1b881bbe1293", "a9478a827e734619b9d853ca16397c1c", "ca38fb09c8b54901a2d39ca01fe7e649", "3c09e72f378546c2b2c1c0fc849d6de5", "6ba4525c5d8347978cc628ba0c15e45d", "ceaa951f3c2a4ccfb1cb92a9d0f160eb", "123c4914445843aab41063593455aef7", "6c8ffcd81db14cdc89e1518a9399457d", "997d24a5944f41629bb11c3a51fa57d8" ] }, "id": "DoypUEVoBh6d", "outputId": "4479b642-4edc-4718-dea2-27cf234327bc" }, "outputs": [ { "data": { "text/plain": [ "'cuda'" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "image_size = 64\n", "batch_size = 64\n", "stats = (0.5, 0.5, 0.5), (0.5, 0.5, 0.5)\n", "df = pd.read_csv('image_desc.csv')\n", "df2 = df.groupby('Image Name')\n", "\n", "sentence_model = SentenceTransformer('all-MiniLM-L6-v2')\n", "\n", "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n", "device" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "id": "CXZhZ3gDaOxL" }, "outputs": [], "source": [ "image_transform = tt.Compose([ tt.Resize(image_size), tt.CenterCrop(image_size), tt.ToTensor(), tt.Normalize(*stats)])" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "id": "Fybmd_U_ZSYS" }, "outputs": [], "source": [ "class FlowerDescDataset(Dataset):\n", " def __init__(self, df2, transform = image_transform):\n", " self.desc_df = df2\n", " self.transform=transform\n", " \n", " def __len__(self):\n", " return self.desc_df.ngroups\n", " \n", " def __getitem__(self,index):\n", " img_name = list(self.desc_df.groups.keys())[index]\n", " img_path = 'jpg/flowers/' + img_name\n", " img = Image.open(img_path)\n", " # img = np.asarray(img)\n", " # img = torch.from_numpy(img)\n", " image=self.transform(img)\n", "\n", " descriptions = self.desc_df.get_group(img_name)\n", " matching_text = descriptions.iloc[random.randint(0, len(descriptions)-1), 2]\n", "\n", " random_row = df.sample(n=1)\n", " mismatching_text = random_row['Text Description'].values[0]\n", " while random_row['Image Name'].values[0] == img_name:\n", " random_row = df.sample(n=1)\n", " mismatching_text = random_row['Text Description'].values[0]\n", " \n", " return (image, matching_text, mismatching_text)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "t2905UrUdfg0", "outputId": "09a72f70-b8a7-44f5-f1d2-32d7a2256253" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "8189\n" ] } ], "source": [ "dataset = FlowerDescDataset(df2)\n", "print(len(dataset))\n", "dl = DataLoader(dataset, batch_size=batch_size, shuffle=True)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 243, "referenced_widgets": [ "013336c872c1429d8084688bbb54299f", "599e3739adfc49b49f5f35aa857d9c72", "63fc0fb6d84e46ebbcc055604039d2be", "81e9bff1364241b1836aed1899628e7e", "748c733f9cde40b7b00b4d6f56727d9e", "a2a9f8a7901c4826a325470d0b166260", "5e8eeef7f611482ab2d90f1183d88b31", "ab18eb95291d4ed9b9f3c171f29cc781", "e48e2a40214849f4967c19ba492e64bf", "6c965b442e444c75b78cc12f90ea9153", "504864b823ee4080ae0f32d8f5a714a3" ] }, "id": "VnO0ofstZs8K", "outputId": "9aac78f3-67ea-41b6-e0b2-49073ef13786" }, "outputs": [ { "data": { "application/json": { "ascii": false, "bar_format": null, "colour": null, "elapsed": 0.020517587661743164, "initial": 0, "n": 0, "ncols": null, "nrows": 29, "postfix": null, "prefix": "", "rate": null, "total": 128, "unit": "it", "unit_divisor": 1000, "unit_scale": false }, "application/vnd.jupyter.widget-view+json": { "model_id": "b3274770cf2646f79c501a76231bbdb2", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/128 [00:00" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch [1/128], loss_g: 2.29974, loss_d: 0.25074, real_score: 0.94099, temp_score: 0.11608, fake_score: 0.15257\n" ] }, { "data": { "application/json": { "ascii": false, "bar_format": null, "colour": null, "elapsed": 0.025122404098510742, "initial": 0, "n": 0, "ncols": null, "nrows": 29, "postfix": null, "prefix": "", "rate": null, "total": 128, "unit": "it", "unit_divisor": 1000, "unit_scale": false }, "application/vnd.jupyter.widget-view+json": { "model_id": "a6e4c6a3b9c0408a8c988c1ebc228dbf", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/128 [00:00" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch [2/128], loss_g: 2.33313, loss_d: 0.17789, real_score: 0.96553, temp_score: 0.10702, fake_score: 0.13458\n" ] }, { "data": { "application/json": { "ascii": false, "bar_format": null, "colour": null, "elapsed": 0.009660720825195312, "initial": 0, "n": 0, "ncols": null, "nrows": 29, "postfix": null, "prefix": "", "rate": null, "total": 128, "unit": "it", "unit_divisor": 1000, "unit_scale": false }, "application/vnd.jupyter.widget-view+json": { "model_id": "3e4245cab85b491b8c02ddc5077eac21", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/128 [00:00" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch [3/128], loss_g: 2.45690, loss_d: 0.24742, real_score: 0.94072, temp_score: 0.11671, fake_score: 0.14454\n" ] }, { "data": { "application/json": { "ascii": false, "bar_format": null, "colour": null, "elapsed": 0.00815725326538086, "initial": 0, "n": 0, "ncols": null, "nrows": 29, "postfix": null, "prefix": "", "rate": null, "total": 128, "unit": "it", "unit_divisor": 1000, "unit_scale": false }, "application/vnd.jupyter.widget-view+json": { "model_id": "f3eb3bca01544ece9a9575c00591f511", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/128 [00:00" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch [4/128], loss_g: 2.40740, loss_d: 0.17881, real_score: 0.96345, temp_score: 0.10355, fake_score: 0.12998\n" ] }, { "data": { "application/json": { "ascii": false, "bar_format": null, "colour": null, "elapsed": 0.025543928146362305, "initial": 0, "n": 0, "ncols": null, "nrows": 29, "postfix": null, "prefix": "", "rate": null, "total": 128, "unit": "it", "unit_divisor": 1000, "unit_scale": false }, "application/vnd.jupyter.widget-view+json": { "model_id": "2b8d560738294b9d8666ebc29384ea69", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/128 [00:00" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch [5/128], loss_g: 2.40752, loss_d: 0.19957, real_score: 0.95568, temp_score: 0.10603, fake_score: 0.13314\n" ] }, { "data": { "application/json": { "ascii": false, "bar_format": null, "colour": null, "elapsed": 0.023084163665771484, "initial": 0, "n": 0, "ncols": null, "nrows": 29, "postfix": null, "prefix": "", "rate": null, "total": 128, "unit": "it", "unit_divisor": 1000, "unit_scale": false }, "application/vnd.jupyter.widget-view+json": { "model_id": "ab56e4c988294e458c47db1bac7c1e32", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/128 [00:00" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch [6/128], loss_g: 2.39900, loss_d: 0.17435, real_score: 0.96435, temp_score: 0.10115, fake_score: 0.12919\n" ] }, { "data": { "application/json": { "ascii": false, "bar_format": null, "colour": null, "elapsed": 0.004142284393310547, "initial": 0, "n": 0, "ncols": null, "nrows": 29, "postfix": null, "prefix": "", "rate": null, "total": 128, "unit": "it", "unit_divisor": 1000, "unit_scale": false }, "application/vnd.jupyter.widget-view+json": { "model_id": "2b4e40d64afd44619c405c7a3ae60f7a", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/128 [00:00" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch [7/128], loss_g: 2.43938, loss_d: 0.34805, real_score: 0.91385, temp_score: 0.11013, fake_score: 0.14877\n" ] }, { "data": { "application/json": { "ascii": false, "bar_format": null, "colour": null, "elapsed": 0.016794204711914062, "initial": 0, "n": 0, "ncols": null, "nrows": 29, "postfix": null, "prefix": "", "rate": null, "total": 128, "unit": "it", "unit_divisor": 1000, "unit_scale": false }, "application/vnd.jupyter.widget-view+json": { "model_id": "a102866f58d6439ab3315887bfef0984", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/128 [00:00" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch [8/128], loss_g: 2.32397, loss_d: 0.19708, real_score: 0.95834, temp_score: 0.10776, fake_score: 0.14467\n" ] }, { "data": { "application/json": { "ascii": false, "bar_format": null, "colour": null, "elapsed": 0.018951892852783203, "initial": 0, "n": 0, "ncols": null, "nrows": 29, "postfix": null, "prefix": "", "rate": null, "total": 128, "unit": "it", "unit_divisor": 1000, "unit_scale": false }, "application/vnd.jupyter.widget-view+json": { "model_id": "690622a8b2ef4c1ebb8f84f9056943fe", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 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"version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/128 [00:00" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch [127/128], loss_g: 2.83844, loss_d: 0.19300, real_score: 0.94843, temp_score: 0.06033, fake_score: 0.11532\n" ] }, { "data": { "application/json": { "ascii": false, "bar_format": null, "colour": null, "elapsed": 0.015442609786987305, "initial": 0, "n": 0, "ncols": null, "nrows": 29, "postfix": null, "prefix": "", "rate": null, "total": 128, "unit": "it", "unit_divisor": 1000, "unit_scale": false }, "application/vnd.jupyter.widget-view+json": { "model_id": "9df39f4754874322934933f776ddf6da", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/128 [00:00" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch [128/128], loss_g: 2.87318, loss_d: 0.11182, real_score: 0.97526, temp_score: 0.04845, fake_score: 0.09390\n" ] } ], "source": [ "epochs = 128\n", "lr = 0.0002\n", "\n", "history = fit(epochs, lr)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 449 }, "id": "tX11oaaGG7Ad", "outputId": "ea063064-58fd-4922-fdb2-cd272ba5fd56" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ENTER DESCRIPTION: purple-blue petals\n" ] }, { "data": { "image/png": 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