From aecd7b795d8d6d6fa19ae7db95bcc73c2900dd49 Mon Sep 17 00:00:00 2001 From: Chris Ha <15088501+VRandme@users.noreply.github.com> Date: Mon, 2 Mar 2020 22:32:43 +0900 Subject: [PATCH] Revert "Create blseresnext-test.ipynb" This reverts commit 91b13658e02e6200140f93994754295c431d919c. --- blseresnext-test.ipynb | 138 ----------------------------------------- 1 file changed, 138 deletions(-) delete mode 100644 blseresnext-test.ipynb diff --git a/blseresnext-test.ipynb b/blseresnext-test.ipynb deleted file mode 100644 index 85a85444..00000000 --- a/blseresnext-test.ipynb +++ /dev/null @@ -1,138 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import torch\n", - "from fastai.vision import *" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import timm" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "#setup paths\n", - "path = Path(untar_data(URLs.CIFAR_100))" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "#hyperparameters\n", - "learning_rate = 0.0005133\n", - "dropout = 0.4068\n", - "bs = 32\n", - "epochs=10" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "ename": "TypeError", - "evalue": "new() received an invalid combination of arguments - got (bool, int), but expected one of:\n * (torch.device device)\n * (torch.Storage storage)\n * (Tensor other)\n * (tuple of ints size, torch.device device)\n didn't match because some of the arguments have invalid types: (\u001b[31;1mbool\u001b[0m, \u001b[31;1mint\u001b[0m)\n * (object data, torch.device device)\n didn't match because some of the arguments have invalid types: (\u001b[31;1mbool\u001b[0m, \u001b[31;1mint\u001b[0m)\n", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;31m#timm.models.blresnet_model(50,2,4)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;31m#timm.models.blresnext_model(50,4,32,2,4,pretrained = True)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mtimm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodels\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mblseresnext_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m50\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m32\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;32mG:\\Notebooks\\my-timm\\timm\\models\\blseresnext.py\u001b[0m in \u001b[0;36mblseresnext_model\u001b[1;34m(depth, basewidth, cardinality, alpha, beta, use_se, num_classes, pretrained)\u001b[0m\n\u001b[0;32m 299\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 300\u001b[0m model = bLSEResNeXt(Bottleneck, layers, basewidth, cardinality,\n\u001b[1;32m--> 301\u001b[1;33m alpha, beta, num_classes,use_se)\n\u001b[0m\u001b[0;32m 302\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mpretrained\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 303\u001b[0m url = model_urls['blseresnext-{}-{}x{}d-a{}-b{}'.format(depth, cardinality,\n", - "\u001b[1;32mG:\\Notebooks\\my-timm\\timm\\models\\blseresnext.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, block, layers, basewidth, cardinality, alpha, beta, use_se, num_classes)\u001b[0m\n\u001b[0;32m 199\u001b[0m num_channels[3] * block.expansion, layers[3], basewidth, cardinality, stride=2)\n\u001b[0;32m 200\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgappool\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mAdaptiveAvgPool2d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 201\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mLinear\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnum_channels\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mblock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexpansion\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnum_classes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 202\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 203\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mm\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodules\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\Anaconda3\\envs\\fastai_v1\\lib\\site-packages\\torch\\nn\\modules\\linear.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, in_features, out_features, bias)\u001b[0m\n\u001b[0;32m 70\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0min_features\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0min_features\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 71\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mout_features\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mout_features\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mweight\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mParameter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTensor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mout_features\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0min_features\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 73\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mbias\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 74\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbias\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mParameter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTensor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mout_features\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mTypeError\u001b[0m: new() received an invalid combination of arguments - got (bool, int), but expected one of:\n * (torch.device device)\n * (torch.Storage storage)\n * (Tensor other)\n * (tuple of ints size, torch.device device)\n didn't match because some of the arguments have invalid types: (\u001b[31;1mbool\u001b[0m, \u001b[31;1mint\u001b[0m)\n * (object data, torch.device device)\n didn't match because some of the arguments have invalid types: (\u001b[31;1mbool\u001b[0m, \u001b[31;1mint\u001b[0m)\n" - ] - } - ], - "source": [ - "#models\n", - "#model = models.resnet50\n", - "#timm.models.blresnet_model(50,2,4)\n", - "#timm.models.blresnext_model(50,4,32,2,4,pretrained = True)\n", - "timm.models.blseresnext_model(50,4,32,2,4)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tfms = get_transforms(do_flip=True, flip_vert=True, max_rotate=25, max_lighting=0.1, max_zoom=1.05, max_warp=0.9)\n", - "\n", - "data = (ImageList.from_folder(path)\n", - ".split_by_rand_pct()\n", - ".label_from_folder()\n", - ".transform(get_transforms())\n", - ".databunch()\n", - ".normalize(imagenet_stats))\n", - "\n", - "learn = cnn_learner(data,\n", - "model,\n", - "pretrained=False,\n", - "ps=dropout,\n", - "metrics=accuracy).to_fp16()\n", - "\n", - "learn.fit_one_cycle(epochs, max_lr=learning_rate)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "learn.save(‘inity1’)\n", - "learn.export(‘init1y.pkl’)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python [conda env:fastai_v1] *", - "language": "python", - "name": "conda-env-fastai_v1-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -}