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Chap_7_exp_SOTA.ipynb
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| { | |
| "nbformat": 4, | |
| "nbformat_minor": 0, | |
| "metadata": { | |
| "colab": { | |
| "name": "Chap_7_exp_SOTA.ipynb", | |
| "provenance": [], | |
| "machine_shape": "hm", | |
| "authorship_tag": "ABX9TyNejG1yQPFfqqoe0+vLDuXG", | |
| "include_colab_link": true | |
| }, | |
| "kernelspec": { | |
| "name": "python3", | |
| "display_name": "Python 3" | |
| }, | |
| "language_info": { | |
| "name": "python" | |
| }, | |
| "accelerator": "GPU" | |
| }, | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/RaviChandraVeeramachaneni/e6b62ec22dc464d569d3b1ccf9f28d5c/chap_7_exp_sota.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "-kYDbiQJ_fJL", | |
| "outputId": "d9f944fe-eace-46ac-88c4-9fe75d04f47c" | |
| }, | |
| "source": [ | |
| "!pip install -Uqq fastbook" | |
| ], | |
| "execution_count": 1, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "\u001b[K |████████████████████████████████| 720 kB 7.3 MB/s \n", | |
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| "\u001b[K |████████████████████████████████| 1.2 MB 16.2 MB/s \n", | |
| "\u001b[K |████████████████████████████████| 188 kB 37.3 MB/s \n", | |
| "\u001b[K |████████████████████████████████| 56 kB 2.8 MB/s \n", | |
| "\u001b[K |████████████████████████████████| 51 kB 273 kB/s \n", | |
| "\u001b[?25h" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 37 | |
| }, | |
| "id": "YRFju3Pd_wX7", | |
| "outputId": "33d8a526-d0e3-4176-bd82-cab8a3a76a1c" | |
| }, | |
| "source": [ | |
| "from fastai.vision.all import *\n", | |
| "path = untar_data(URLs.IMAGENETTE)" | |
| ], | |
| "execution_count": 2, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/html": [ | |
| "\n", | |
| " <div>\n", | |
| " <style>\n", | |
| " /* Turns off some styling */\n", | |
| " progress {\n", | |
| " /* gets rid of default border in Firefox and Opera. */\n", | |
| " border: none;\n", | |
| " /* Needs to be in here for Safari polyfill so background images work as expected. */\n", | |
| " background-size: auto;\n", | |
| " }\n", | |
| " .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n", | |
| " background: #F44336;\n", | |
| " }\n", | |
| " </style>\n", | |
| " <progress value='1557168128' class='' max='1557161267' style='width:300px; height:20px; vertical-align: middle;'></progress>\n", | |
| " 100.00% [1557168128/1557161267 00:37<00:00]\n", | |
| " </div>\n", | |
| " " | |
| ], | |
| "text/plain": [ | |
| "<IPython.core.display.HTML object>" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "oCwrcyjN__xg", | |
| "outputId": "ea56bd6a-a619-4505-ea3c-4ff8a8ea236e" | |
| }, | |
| "source": [ | |
| "dblock = DataBlock(blocks=(ImageBlock(), CategoryBlock()),\n", | |
| " get_items=get_image_files,\n", | |
| " get_y=parent_label,\n", | |
| " item_tfms=Resize(460),\n", | |
| " batch_tfms=aug_transforms(size=224, min_scale=0.75))\n", | |
| "dls = dblock.dataloaders(path, bs=64)" | |
| ], | |
| "execution_count": 3, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "/usr/local/lib/python3.7/dist-packages/torch/_tensor.py:1023: UserWarning: torch.solve is deprecated in favor of torch.linalg.solveand will be removed in a future PyTorch release.\n", | |
| "torch.linalg.solve has its arguments reversed and does not return the LU factorization.\n", | |
| "To get the LU factorization see torch.lu, which can be used with torch.lu_solve or torch.lu_unpack.\n", | |
| "X = torch.solve(B, A).solution\n", | |
| "should be replaced with\n", | |
| "X = torch.linalg.solve(A, B) (Triggered internally at /pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:760.)\n", | |
| " ret = func(*args, **kwargs)\n" | |
| ], | |
| "name": "stderr" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "kGQkcjvxYx1L", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 258 | |
| }, | |
| "outputId": "57a90b3c-3a11-4073-f011-6ebbcc85339c" | |
| }, | |
| "source": [ | |
| "model = xresnet50(n_out=dls.c)\n", | |
| "learn = Learner(dls, model, loss_func=CrossEntropyLossFlat(), metrics=accuracy)\n", | |
| "learn.fit_one_cycle(5, 3e-3)" | |
| ], | |
| "execution_count": 4, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/html": [ | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: left;\">\n", | |
| " <th>epoch</th>\n", | |
| " <th>train_loss</th>\n", | |
| " <th>valid_loss</th>\n", | |
| " <th>accuracy</th>\n", | |
| " <th>time</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <td>0</td>\n", | |
| " <td>1.641177</td>\n", | |
| " <td>1.878222</td>\n", | |
| " <td>0.429425</td>\n", | |
| " <td>01:25</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>1</td>\n", | |
| " <td>1.277725</td>\n", | |
| " <td>1.285429</td>\n", | |
| " <td>0.584765</td>\n", | |
| " <td>01:23</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>2</td>\n", | |
| " <td>0.964809</td>\n", | |
| " <td>1.028423</td>\n", | |
| " <td>0.672143</td>\n", | |
| " <td>01:24</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>3</td>\n", | |
| " <td>0.745534</td>\n", | |
| " <td>0.633708</td>\n", | |
| " <td>0.792009</td>\n", | |
| " <td>01:23</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>4</td>\n", | |
| " <td>0.596895</td>\n", | |
| " <td>0.546192</td>\n", | |
| " <td>0.833084</td>\n", | |
| " <td>01:24</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>" | |
| ], | |
| "text/plain": [ | |
| "<IPython.core.display.HTML object>" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /pytorch/c10/core/TensorImpl.h:1156.)\n", | |
| " return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)\n" | |
| ], | |
| "name": "stderr" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "SqODCIvEc0Mn" | |
| }, | |
| "source": [ | |
| "def get_dls(bs, size):\n", | |
| " dblock = DataBlock(blocks=(ImageBlock, CategoryBlock),\n", | |
| " get_items=get_image_files,\n", | |
| " get_y=parent_label,\n", | |
| " item_tfms=Resize(460),\n", | |
| " batch_tfms=[*aug_transforms(size=size, min_scale=0.75),\n", | |
| " Normalize.from_stats(*imagenet_stats)])\n", | |
| " return dblock.dataloaders(path, bs=bs)" | |
| ], | |
| "execution_count": 5, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "IF6M68uNc2Wx" | |
| }, | |
| "source": [ | |
| "dls = get_dls(64, 224)" | |
| ], | |
| "execution_count": 6, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "Qd6pJBcDfZBA", | |
| "outputId": "53247c97-0d5e-4d96-b4c6-6dbf079c61b3" | |
| }, | |
| "source": [ | |
| "x,y = dls.one_batch()\n", | |
| "x.mean(dim=[0,2,3]),x.std(dim=[0,2,3])" | |
| ], | |
| "execution_count": 7, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "(TensorImage([-0.1044, -0.0363, 0.0133], device='cuda:0'),\n", | |
| " TensorImage([1.1492, 1.1299, 1.2013], device='cuda:0'))" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 7 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 204 | |
| }, | |
| "id": "JOqAumulfgZq", | |
| "outputId": "56c49c56-d174-46ce-ce8a-c7b7412063b7" | |
| }, | |
| "source": [ | |
| "model = xresnet50(n_out=dls.c)\n", | |
| "learn = Learner(dls, model, loss_func=CrossEntropyLossFlat(), metrics=accuracy)\n", | |
| "learn.fit_one_cycle(5, 3e-3)" | |
| ], | |
| "execution_count": 10, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/html": [ | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: left;\">\n", | |
| " <th>epoch</th>\n", | |
| " <th>train_loss</th>\n", | |
| " <th>valid_loss</th>\n", | |
| " <th>accuracy</th>\n", | |
| " <th>time</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <td>0</td>\n", | |
| " <td>1.637188</td>\n", | |
| " <td>15.789495</td>\n", | |
| " <td>0.162435</td>\n", | |
| " <td>01:23</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>1</td>\n", | |
| " <td>1.282778</td>\n", | |
| " <td>1.238257</td>\n", | |
| " <td>0.608290</td>\n", | |
| " <td>01:24</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>2</td>\n", | |
| " <td>0.987376</td>\n", | |
| " <td>0.944911</td>\n", | |
| " <td>0.686333</td>\n", | |
| " <td>01:24</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>3</td>\n", | |
| " <td>0.768254</td>\n", | |
| " <td>0.752904</td>\n", | |
| " <td>0.755414</td>\n", | |
| " <td>01:24</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>4</td>\n", | |
| " <td>0.607765</td>\n", | |
| " <td>0.577785</td>\n", | |
| " <td>0.822629</td>\n", | |
| " <td>01:23</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>" | |
| ], | |
| "text/plain": [ | |
| "<IPython.core.display.HTML object>" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 173 | |
| }, | |
| "id": "4N8nb7aGoSA3", | |
| "outputId": "b5c05dcd-286a-44aa-e185-bacd1d589916" | |
| }, | |
| "source": [ | |
| "dls = get_dls(128, 128)\n", | |
| "learn = Learner(dls, xresnet50(n_out=dls.c), loss_func=CrossEntropyLossFlat(), \n", | |
| " metrics=accuracy)\n", | |
| "learn.fit_one_cycle(4, 3e-3)" | |
| ], | |
| "execution_count": 11, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/html": [ | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: left;\">\n", | |
| " <th>epoch</th>\n", | |
| " <th>train_loss</th>\n", | |
| " <th>valid_loss</th>\n", | |
| " <th>accuracy</th>\n", | |
| " <th>time</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <td>0</td>\n", | |
| " <td>1.627283</td>\n", | |
| " <td>2.359048</td>\n", | |
| " <td>0.382375</td>\n", | |
| " <td>01:02</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>1</td>\n", | |
| " <td>1.262686</td>\n", | |
| " <td>1.327771</td>\n", | |
| " <td>0.581404</td>\n", | |
| " <td>01:03</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>2</td>\n", | |
| " <td>0.958410</td>\n", | |
| " <td>0.836621</td>\n", | |
| " <td>0.721807</td>\n", | |
| " <td>01:01</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>3</td>\n", | |
| " <td>0.731075</td>\n", | |
| " <td>0.659469</td>\n", | |
| " <td>0.793876</td>\n", | |
| " <td>01:01</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>" | |
| ], | |
| "text/plain": [ | |
| "<IPython.core.display.HTML object>" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 267 | |
| }, | |
| "id": "xSImsqgdphVx", | |
| "outputId": "0c7986f1-6183-4ab9-990f-215d55101be6" | |
| }, | |
| "source": [ | |
| "learn.dls = get_dls(64, 224)\n", | |
| "learn.fine_tune(5, 1e-3)" | |
| ], | |
| "execution_count": 12, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/html": [ | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: left;\">\n", | |
| " <th>epoch</th>\n", | |
| " <th>train_loss</th>\n", | |
| " <th>valid_loss</th>\n", | |
| " <th>accuracy</th>\n", | |
| " <th>time</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <td>0</td>\n", | |
| " <td>0.823625</td>\n", | |
| " <td>0.971067</td>\n", | |
| " <td>0.705004</td>\n", | |
| " <td>01:24</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>" | |
| ], | |
| "text/plain": [ | |
| "<IPython.core.display.HTML object>" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| } | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/html": [ | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: left;\">\n", | |
| " <th>epoch</th>\n", | |
| " <th>train_loss</th>\n", | |
| " <th>valid_loss</th>\n", | |
| " <th>accuracy</th>\n", | |
| " <th>time</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <td>0</td>\n", | |
| " <td>0.666928</td>\n", | |
| " <td>0.635941</td>\n", | |
| " <td>0.803958</td>\n", | |
| " <td>01:23</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>1</td>\n", | |
| " <td>0.661911</td>\n", | |
| " <td>0.860903</td>\n", | |
| " <td>0.749440</td>\n", | |
| " <td>01:24</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>2</td>\n", | |
| " <td>0.574039</td>\n", | |
| " <td>0.595109</td>\n", | |
| " <td>0.817401</td>\n", | |
| " <td>01:25</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>3</td>\n", | |
| " <td>0.493426</td>\n", | |
| " <td>0.442514</td>\n", | |
| " <td>0.854369</td>\n", | |
| " <td>01:24</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>4</td>\n", | |
| " <td>0.437183</td>\n", | |
| " <td>0.426613</td>\n", | |
| " <td>0.863704</td>\n", | |
| " <td>01:24</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>" | |
| ], | |
| "text/plain": [ | |
| "<IPython.core.display.HTML object>" | |
| ] | |
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| "metadata": { | |
| "tags": [] | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 54 | |
| }, | |
| "id": "49Vla0gv3wD2", | |
| "outputId": "105b1110-8f5a-4d12-b712-edba9066ce83" | |
| }, | |
| "source": [ | |
| "preds,targs = learn.tta()\n", | |
| "accuracy(preds, targs).item()" | |
| ], | |
| "execution_count": 13, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/html": [ | |
| "\n", | |
| " <div>\n", | |
| " <style>\n", | |
| " /* Turns off some styling */\n", | |
| " progress {\n", | |
| " /* gets rid of default border in Firefox and Opera. */\n", | |
| " border: none;\n", | |
| " /* Needs to be in here for Safari polyfill so background images work as expected. */\n", | |
| " background-size: auto;\n", | |
| " }\n", | |
| " .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n", | |
| " background: #F44336;\n", | |
| " }\n", | |
| " </style>\n", | |
| " <progress value='0' class='' max='5' style='width:300px; height:20px; vertical-align: middle;'></progress>\n", | |
| " \n", | |
| " </div>\n", | |
| " \n" | |
| ], | |
| "text/plain": [ | |
| "<IPython.core.display.HTML object>" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| } | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/html": [ | |
| "" | |
| ], | |
| "text/plain": [ | |
| "<IPython.core.display.HTML object>" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| } | |
| }, | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "0.8752800822257996" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 13 | |
| } | |
| ] | |
| } | |
| ] | |
| } |
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