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March 24, 2023 06:20
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/ariG23498/40ddaf3975f245ede5d51fdbe18284bb/scratchpad.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "lIYdn1woOS1n" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "import tensorflow as tf\n", | |
| "import tensorflow_datasets as tfds\n", | |
| "import time\n", | |
| "import matplotlib.pyplot as plt" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "train_ds = tfds.load(\n", | |
| " name=\"cifar10\",\n", | |
| " split=\"train\"\n", | |
| ")" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 169, | |
| "referenced_widgets": [ | |
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| }, | |
| "id": "f2zGma93MLd0", | |
| "outputId": "9dcb4588-b433-422f-ae2a-192aa9afb69e" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Downloading and preparing dataset Unknown size (download: Unknown size, generated: Unknown size, total: Unknown size) to /root/tensorflow_datasets/cifar10/3.0.2...\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Dl Completed...: 0 url [00:00, ? url/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "96f0d8b2683f44ac982b81fda4b81228" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Dl Size...: 0 MiB [00:00, ? MiB/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "7152407ac7a840d39d92b0a902050986" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Extraction completed...: 0 file [00:00, ? file/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "442c994ac1124bcca35adcbcbeafbf3a" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Generating splits...: 0%| | 0/2 [00:00<?, ? splits/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "63bc6db1449f4b44a5f30bbbcad53360" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Generating train examples...: 0 examples [00:00, ? examples/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "cad6d46c18a544528feb7dc81def3993" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Shuffling /root/tensorflow_datasets/cifar10/3.0.2.incompleteVTEHKJ/cifar10-train.tfrecord*...: 0%| …" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "b6d9237ac1794d44a1e45a236d265140" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Generating test examples...: 0 examples [00:00, ? examples/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "e38dffc879cc4bd289a5684b88e31d43" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Shuffling /root/tensorflow_datasets/cifar10/3.0.2.incompleteVTEHKJ/cifar10-test.tfrecord*...: 0%| |…" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "9b4cbd4182ec4ca781b9ac04437a2af3" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Dataset cifar10 downloaded and prepared to /root/tensorflow_datasets/cifar10/3.0.2. Subsequent calls will reuse this data.\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "for element in train_ds.take(1):\n", | |
| " print(element[\"image\"].shape)\n", | |
| " print(element[\"label\"].shape)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "GXb_2FrKMWtl", | |
| "outputId": "62f8d1d4-8e62-4bf1-fe04-4bfa2e536a71" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "(32, 32, 3)\n", | |
| "()\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "@tf.function\n", | |
| "def map_fn_seed(element):\n", | |
| " images = element[\"image\"]\n", | |
| " low = tf.image.random_flip_left_right(images, seed=1337)\n", | |
| " enhanced = tf.image.random_flip_left_right(images, seed=1337)\n", | |
| " return low, enhanced" | |
| ], | |
| "metadata": { | |
| "id": "AMRxjFG4Mdja" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "@tf.function\n", | |
| "def map_fn_cond(element):\n", | |
| " images = element[\"image\"]\n", | |
| " return tf.cond(\n", | |
| " tf.random.uniform(shape=(), maxval=1) < 0.5,\n", | |
| " lambda: (images, images),\n", | |
| " lambda: (\n", | |
| " tf.image.flip_left_right(images),\n", | |
| " tf.image.flip_left_right(images),\n", | |
| " ),\n", | |
| " )" | |
| ], | |
| "metadata": { | |
| "id": "vEhsn7v8OEm6" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "BATCH_SIZE = 2048\n", | |
| "\n", | |
| "train_seed_ds = train_ds.batch(BATCH_SIZE).map(map_fn_seed)\n", | |
| "train_cond_ds = train_ds.batch(BATCH_SIZE).map(map_fn_cond)" | |
| ], | |
| "metadata": { | |
| "id": "we8hdudSOaWI" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# SEEDING" | |
| ], | |
| "metadata": { | |
| "id": "g83QnpvCP9cG" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "t0 = time.time()\n", | |
| "\n", | |
| "n = 100\n", | |
| "\n", | |
| "for l, e in train_seed_ds.take(n):\n", | |
| " pass\n", | |
| "\n", | |
| "batch_time = (time.time() - t0) / n\n", | |
| "sample_time = batch_time / BATCH_SIZE\n", | |
| "print(f\"Batch time -> {batch_time:0.4f}s\")\n", | |
| "print(f\"Sample time -> {sample_time:0.4f}s\")" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "yzsMzQ_NO6qg", | |
| "outputId": "71ac5169-828c-4f94-d637-1aa76c6b6467" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Batch time -> 0.0264s\n", | |
| "Sample time -> 0.0000s\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "fix, axs = plt.subplots(1, 2)\n", | |
| "for l, e in train_seed_ds.take(1):\n", | |
| " axs[0].imshow(l[0])\n", | |
| " axs[1].imshow(e[0])" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 202 | |
| }, | |
| "id": "mNBoPITrRJgm", | |
| "outputId": "2a14377a-ccce-4bba-9216-373c901ccfda" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 432x288 with 2 Axes>" | |
| ], | |
| "image/png": 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\n" 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| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# CONDITION" | |
| ], | |
| "metadata": { | |
| "id": "qHTFiagCP-0o" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "t0 = time.time()\n", | |
| "\n", | |
| "n = 100\n", | |
| "\n", | |
| "for l, e in train_cond_ds.take(n):\n", | |
| " pass\n", | |
| "\n", | |
| "batch_time = (time.time() - t0) / n\n", | |
| "sample_time = batch_time / BATCH_SIZE\n", | |
| "print(f\"Batch time -> {batch_time:0.4f}s\")\n", | |
| "print(f\"Sample time -> {sample_time:0.4f}s\")" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "UtL9_BPsPU8T", | |
| "outputId": "db377781-bd2f-42e6-b51b-b9112af16c29" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Batch time -> 0.0065s\n", | |
| "Sample time -> 0.0000s\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "fix, axs = plt.subplots(1, 2)\n", | |
| "for l, e in train_cond_ds.take(1):\n", | |
| " axs[0].imshow(l[0])\n", | |
| " axs[1].imshow(e[0])" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 202 | |
| }, | |
| "id": "x92C5fr9RcYL", | |
| "outputId": "f8f49b63-2a00-4832-a258-d97aba6162a2" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 432x288 with 2 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": { | |
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