From eb5b206f7e1b08435378d2a02395307be55ee6f1 Mon Sep 17 00:00:00 2001 From: Gustaf Rydholm Date: Tue, 6 Jul 2021 17:42:53 +0200 Subject: Refactoring data with attrs and refactor conf for hydra --- notebooks/00-scratch-pad.ipynb | 275 ++++++++++++++++++++++++++++++++++++++--- 1 file changed, 257 insertions(+), 18 deletions(-) (limited to 'notebooks') diff --git a/notebooks/00-scratch-pad.ipynb b/notebooks/00-scratch-pad.ipynb index 16c6533..1e30038 100644 --- a/notebooks/00-scratch-pad.ipynb +++ b/notebooks/00-scratch-pad.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -30,7 +30,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -39,7 +39,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -48,41 +48,280 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "@attr.s\n", - "class B:\n", - " batch_size = attr.ib()\n", - " num_workers = attr.ib()" + "class B(nn.Module):\n", + " input_dim = attr.ib()\n", + " hidden = attr.ib()\n", + " xx = attr.ib(init=False, default=\"hek\")\n", + " \n", + " def __attrs_post_init__(self):\n", + " super().__init__()\n", + " self.fc = nn.Linear(self.input_dim, self.hidden)\n", + " self.xx = \"da\"\n", + " \n", + " def forward(self, x):\n", + " return self.fc(x)" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 49, "metadata": {}, "outputs": [], "source": [ - "@attr.s\n", - "class T(B):\n", + "def f(x):\n", + " return 2\n", "\n", - " def __attrs_post_init__(self) -> None:\n", - " super().__init__(self.batch_size, self.num_workers)\n", - " self.hej = None\n", + "@attr.s(auto_attribs=True)\n", + "class T(B):\n", " \n", - " batch_size = attr.ib()\n", - " num_workers = attr.ib()\n", - " h: Path = attr.ib(converter=Path)" + " h: Path = attr.ib(converter=Path)\n", + " p: int = attr.ib(init=False, default=f(3))" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "__init__() missing 1 required positional argument: 'hidden'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mT\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_dim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m16\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"hej\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m: __init__() missing 1 required positional argument: 'hidden'" + ] + } + ], + "source": [ + "t = T(input_dim=16, h=\"hej\")" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'da'" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t.xx" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t.p" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "16" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t.input_dim" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "x = torch.rand(16, 16)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([16, 16])" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "T(input_dim=16, hidden=24, h=PosixPath('hej'))" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t.cuda()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ - "t = T(batch_size=16, num_workers=2, h=\"hej\")" + "x = x.cuda()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[ 3.6047e-01, 1.0200e+00, 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