wehub-resource-sync
157 lines
3.3 KiB
Plaintext
157 lines
3.3 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b08a2f4e",
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"metadata": {},
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"outputs": [],
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"source": [
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"#| hide\n",
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"#| eval: false\n",
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"! [ -e /content ] && pip install -Uqq fastai # upgrade fastai on colab"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "be424fac",
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"metadata": {},
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"outputs": [],
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"source": [
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"#| default_exp callback.preds"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "baa1e5e1",
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"metadata": {},
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"outputs": [],
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"source": [
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"#| export\n",
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"from fastai.basics import *"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6b9166e2",
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"metadata": {},
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"outputs": [],
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"source": [
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"#| hide\n",
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"from nbdev.showdoc import *\n",
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"from fastai.test_utils import *"
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]
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},
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{
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"cell_type": "markdown",
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"id": "cf763b1f",
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"metadata": {},
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"source": [
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"# Predictions callbacks\n",
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"\n",
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"> Various callbacks to customize get_preds behaviors"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a6fc01ba",
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"metadata": {},
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"source": [
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"## MCDropoutCallback\n",
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"\n",
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"> Turns on dropout during inference, allowing you to call Learner.get_preds multiple times to approximate your model uncertainty using [Monte Carlo Dropout](https://arxiv.org/pdf/1506.02142.pdf)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8f0bcd2d",
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"metadata": {},
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"outputs": [],
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"source": [
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"#| export\n",
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"class MCDropoutCallback(Callback):\n",
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" def before_validate(self):\n",
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" for m in [m for m in flatten_model(self.model) if 'dropout' in m.__class__.__name__.lower()]:\n",
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" m.train()\n",
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" \n",
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" def after_validate(self):\n",
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" for m in [m for m in flatten_model(self.model) if 'dropout' in m.__class__.__name__.lower()]:\n",
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" m.eval()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9a7daff0",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"torch.Size([10, 32, 1])"
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]
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},
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"execution_count": null,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"learn = synth_learner()\n",
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"\n",
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"# Call get_preds 10 times, then stack the predictions, yielding a tensor with shape [# of samples, batch_size, ...]\n",
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"dist_preds = []\n",
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"for i in range(10):\n",
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" preds, targs = learn.get_preds(cbs=[MCDropoutCallback()])\n",
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" dist_preds += [preds]\n",
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"\n",
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"torch.stack(dist_preds).shape"
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]
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},
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{
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"cell_type": "markdown",
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"id": "cc6a1c75",
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"metadata": {},
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"source": [
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"## Export -"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "617f80e3",
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"metadata": {},
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"outputs": [],
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"source": [
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"#| hide\n",
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"from nbdev import nbdev_export\n",
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"nbdev_export()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d62545b2",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"jupytext": {
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"split_at_heading": true
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},
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"kernelspec": {
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"display_name": "python3",
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"language": "python",
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"name": "python3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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