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Ignore notebooks for now
This commit is contained in:
mbsantiago
parent
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commit
c9bcaebcde
3
.gitignore
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@ -124,3 +124,6 @@ example_data/preprocessed
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# Dev notebooks
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# Dev notebooks
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notebooks/tmp
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notebooks/tmp
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/tmp
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/tmp
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/.agents/skills
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/notebooks
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/AGENTS.md
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "cfb0b360-a204-4c27-a18f-3902e8758879",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2024-11-19T17:33:02.699871Z",
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"iopub.status.busy": "2024-11-19T17:33:02.699590Z",
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"iopub.status.idle": "2024-11-19T17:33:02.710312Z",
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"shell.execute_reply": "2024-11-19T17:33:02.709798Z",
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"shell.execute_reply.started": "2024-11-19T17:33:02.699839Z"
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}
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},
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"outputs": [],
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"source": [
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"%load_ext autoreload\n",
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"%autoreload 2"
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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": 2,
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"id": "326c5432-94e6-4abf-a332-fe902559461b",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2024-11-19T17:33:02.711324Z",
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"iopub.status.busy": "2024-11-19T17:33:02.711067Z",
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"iopub.status.idle": "2024-11-19T17:33:09.092380Z",
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"shell.execute_reply": "2024-11-19T17:33:09.091830Z",
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/home/santiago/Software/bat_detectors/batdetect2/.venv/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
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" from .autonotebook import tqdm as notebook_tqdm\n"
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]
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}
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],
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"source": [
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"from pathlib import Path\n",
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"from typing import List, Optional\n",
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"import torch\n",
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"\n",
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"import pytorch_lightning as pl\n",
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"from batdetect2.train.modules import DetectorModel\n",
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"from batdetect2.train.augmentations import (\n",
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" add_echo,\n",
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" select_random_subclip,\n",
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" warp_spectrogram,\n",
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")\n",
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"from batdetect2.train.dataset import LabeledDataset, get_files\n",
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"from batdetect2.train.preprocess import PreprocessingConfig\n",
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"from soundevent import data\n",
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"import matplotlib.pyplot as plt\n",
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"from soundevent.types import ClassMapper\n",
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"from torch.utils.data import DataLoader"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9402a473-0b25-4123-9fa8-ad1f71a4237a",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2024-11-18T22:39:12.395329Z",
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"iopub.status.busy": "2024-11-18T22:39:12.393444Z",
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"iopub.status.idle": "2024-11-18T22:39:12.405938Z",
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"shell.execute_reply": "2024-11-18T22:39:12.402980Z",
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"shell.execute_reply.started": "2024-11-18T22:39:12.395236Z"
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}
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},
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"source": [
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"## Training Datasets"
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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": 3,
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"id": "cfd97d83-8c2b-46c8-9eae-cea59f53bc61",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2024-11-19T17:33:09.093487Z",
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"iopub.status.busy": "2024-11-19T17:33:09.092990Z",
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"iopub.status.idle": "2024-11-19T17:33:09.121636Z",
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"shell.execute_reply": "2024-11-19T17:33:09.121143Z",
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"shell.execute_reply.started": "2024-11-19T17:33:09.093459Z"
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}
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},
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"outputs": [],
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"source": [
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"data_dir = Path.cwd().parent / \"example_data\""
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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": 4,
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"id": "d5131ae9-2efd-4758-b6e5-189a6d90789b",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2024-11-19T17:33:09.122685Z",
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"iopub.status.busy": "2024-11-19T17:33:09.122270Z",
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"iopub.status.idle": "2024-11-19T17:33:09.151386Z",
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"shell.execute_reply": "2024-11-19T17:33:09.150788Z",
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"shell.execute_reply.started": "2024-11-19T17:33:09.122661Z"
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}
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},
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"outputs": [],
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"source": [
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"files = get_files(data_dir / \"preprocessed\")"
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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": 5,
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"id": "bc733d3d-7829-4e90-896d-a0dc76b33288",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2024-11-19T17:33:09.152327Z",
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"iopub.status.busy": "2024-11-19T17:33:09.152060Z",
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"iopub.status.idle": "2024-11-19T17:33:09.184041Z",
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"shell.execute_reply": "2024-11-19T17:33:09.183372Z",
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"shell.execute_reply.started": "2024-11-19T17:33:09.152305Z"
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}
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},
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"outputs": [],
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"source": [
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"train_dataset = LabeledDataset(files)"
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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": 6,
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"id": "dfbb94ab-7b12-4689-9c15-4dc34cd17cb2",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2024-11-19T17:33:09.186393Z",
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"iopub.status.busy": "2024-11-19T17:33:09.186117Z",
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"iopub.status.idle": "2024-11-19T17:33:09.220175Z",
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"shell.execute_reply": "2024-11-19T17:33:09.219322Z",
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"shell.execute_reply.started": "2024-11-19T17:33:09.186375Z"
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}
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},
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"outputs": [],
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"source": [
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"train_dataloader = DataLoader(\n",
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" train_dataset,\n",
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" shuffle=True,\n",
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" batch_size=32,\n",
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" num_workers=4,\n",
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")"
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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": 7,
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"id": "e2eedaa9-6be3-481a-8786-7618515d98f8",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2024-11-19T17:33:09.221653Z",
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"iopub.status.busy": "2024-11-19T17:33:09.221242Z",
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"iopub.status.idle": "2024-11-19T17:33:09.260977Z",
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"shell.execute_reply": "2024-11-19T17:33:09.260375Z",
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"shell.execute_reply.started": "2024-11-19T17:33:09.221616Z"
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}
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},
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"outputs": [],
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"source": [
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"# List of all possible classes\n",
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"class Mapper(ClassMapper):\n",
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" class_labels = [\n",
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" \"Eptesicus serotinus\",\n",
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" \"Myotis mystacinus\",\n",
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" \"Pipistrellus pipistrellus\",\n",
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" \"Rhinolophus ferrumequinum\",\n",
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" ]\n",
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"\n",
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" def encode(self, x: data.SoundEventAnnotation) -> Optional[str]:\n",
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" event_tag = data.find_tag(x.tags, \"event\")\n",
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"\n",
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" if event_tag.value == \"Social\":\n",
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" return \"social\"\n",
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"\n",
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" if event_tag.value != \"Echolocation\":\n",
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" # Ignore all other types of calls\n",
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" return None\n",
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"\n",
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" species_tag = data.find_tag(x.tags, \"class\")\n",
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" return species_tag.value\n",
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"\n",
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" def decode(self, class_name: str) -> List[data.Tag]:\n",
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" if class_name == \"social\":\n",
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" return [data.Tag(key=\"event\", value=\"social\")]\n",
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"\n",
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" return [data.Tag(key=\"class\", value=class_name)]"
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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": 8,
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"id": "1ff6072c-511e-42fe-a74f-282f269b80f0",
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"metadata": {
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}
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},
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"outputs": [],
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"source": [
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"detector = DetectorModel(class_mapper=Mapper())"
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]
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},
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{
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"cell_type": "code",
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"outputs": [
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"GPU available: False, used: False\n",
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"TPU available: False, using: 0 TPU cores\n",
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"HPU available: False, using: 0 HPUs\n"
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]
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}
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],
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"source": [
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"trainer = pl.Trainer(\n",
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" limit_train_batches=100,\n",
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" max_epochs=2,\n",
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" log_every_n_steps=1,\n",
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")"
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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": 10,
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"id": "0b86d49d-3314-4257-94f5-f964855be385",
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"execution": {
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"outputs": [
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"output_type": "stream",
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"text": [
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"\n",
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" | Name | Type | Params | Mode \n",
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"--------------------------------------------------------\n",
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"0 | feature_extractor | Net2DFast | 119 K | train\n",
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"1 | classifier | Conv2d | 54 | train\n",
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"2 | bbox | Conv2d | 18 | train\n",
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"--------------------------------------------------------\n",
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"119 K Trainable params\n",
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"448 Non-trainable params\n",
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"119 K Total params\n",
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"0.480 Total estimated model params size (MB)\n",
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"32 Modules in train mode\n",
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"0 Modules in eval mode\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epoch 0: 0%| | 0/1 [00:00<?, ?it/s]class heatmap shape torch.Size([3, 4, 128, 512])\n",
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"class props shape torch.Size([3, 5, 128, 512])\n"
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]
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},
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{
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"ename": "RuntimeError",
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"evalue": "The size of tensor a (5) must match the size of tensor b (4) at non-singleton dimension 1",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn[10], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdetector\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_dataloaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrain_dataloader\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/Software/bat_detectors/batdetect2/.venv/lib/python3.12/site-packages/pytorch_lightning/trainer/trainer.py:538\u001b[0m, in \u001b[0;36mTrainer.fit\u001b[0;34m(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)\u001b[0m\n\u001b[1;32m 536\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mstatus \u001b[38;5;241m=\u001b[39m TrainerStatus\u001b[38;5;241m.\u001b[39mRUNNING\n\u001b[1;32m 537\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtraining \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m--> 538\u001b[0m \u001b[43mcall\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_and_handle_interrupt\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 539\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit_impl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_dataloaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mval_dataloaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdatamodule\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mckpt_path\u001b[49m\n\u001b[1;32m 540\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/Software/bat_detectors/batdetect2/.venv/lib/python3.12/site-packages/pytorch_lightning/trainer/call.py:47\u001b[0m, in \u001b[0;36m_call_and_handle_interrupt\u001b[0;34m(trainer, trainer_fn, *args, **kwargs)\u001b[0m\n\u001b[1;32m 45\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mstrategy\u001b[38;5;241m.\u001b[39mlauncher \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 46\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mstrategy\u001b[38;5;241m.\u001b[39mlauncher\u001b[38;5;241m.\u001b[39mlaunch(trainer_fn, \u001b[38;5;241m*\u001b[39margs, trainer\u001b[38;5;241m=\u001b[39mtrainer, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m---> 47\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtrainer_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 49\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m _TunerExitException:\n\u001b[1;32m 50\u001b[0m _call_teardown_hook(trainer)\n",
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"File \u001b[0;32m~/Software/bat_detectors/batdetect2/.venv/lib/python3.12/site-packages/pytorch_lightning/trainer/trainer.py:574\u001b[0m, in \u001b[0;36mTrainer._fit_impl\u001b[0;34m(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)\u001b[0m\n\u001b[1;32m 567\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mfn \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 568\u001b[0m ckpt_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_checkpoint_connector\u001b[38;5;241m.\u001b[39m_select_ckpt_path(\n\u001b[1;32m 569\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mfn,\n\u001b[1;32m 570\u001b[0m ckpt_path,\n\u001b[1;32m 571\u001b[0m model_provided\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m 572\u001b[0m model_connected\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlightning_module \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 573\u001b[0m )\n\u001b[0;32m--> 574\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mckpt_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mckpt_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 576\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mstopped\n\u001b[1;32m 577\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtraining \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
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"File \u001b[0;32m~/Software/bat_detectors/batdetect2/.venv/lib/python3.12/site-packages/pytorch_lightning/trainer/trainer.py:981\u001b[0m, in \u001b[0;36mTrainer._run\u001b[0;34m(self, model, ckpt_path)\u001b[0m\n\u001b[1;32m 976\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_signal_connector\u001b[38;5;241m.\u001b[39mregister_signal_handlers()\n\u001b[1;32m 978\u001b[0m \u001b[38;5;66;03m# ----------------------------\u001b[39;00m\n\u001b[1;32m 979\u001b[0m \u001b[38;5;66;03m# RUN THE TRAINER\u001b[39;00m\n\u001b[1;32m 980\u001b[0m \u001b[38;5;66;03m# ----------------------------\u001b[39;00m\n\u001b[0;32m--> 981\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run_stage\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 983\u001b[0m \u001b[38;5;66;03m# ----------------------------\u001b[39;00m\n\u001b[1;32m 984\u001b[0m \u001b[38;5;66;03m# POST-Training CLEAN UP\u001b[39;00m\n\u001b[1;32m 985\u001b[0m \u001b[38;5;66;03m# ----------------------------\u001b[39;00m\n\u001b[1;32m 986\u001b[0m log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: trainer tearing down\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
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"File \u001b[0;32m~/Software/bat_detectors/batdetect2/.venv/lib/python3.12/site-packages/pytorch_lightning/trainer/trainer.py:1025\u001b[0m, in \u001b[0;36mTrainer._run_stage\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1023\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_run_sanity_check()\n\u001b[1;32m 1024\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mautograd\u001b[38;5;241m.\u001b[39mset_detect_anomaly(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_detect_anomaly):\n\u001b[0;32m-> 1025\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_loop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1026\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1027\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnexpected state \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
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"File \u001b[0;32m~/Software/bat_detectors/batdetect2/.venv/lib/python3.12/site-packages/pytorch_lightning/loops/fit_loop.py:205\u001b[0m, in \u001b[0;36m_FitLoop.run\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 203\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 204\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mon_advance_start()\n\u001b[0;32m--> 205\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madvance\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 206\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mon_advance_end()\n\u001b[1;32m 207\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_restarting \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
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"File \u001b[0;32m~/Software/bat_detectors/batdetect2/.venv/lib/python3.12/site-packages/pytorch_lightning/loops/fit_loop.py:363\u001b[0m, in \u001b[0;36m_FitLoop.advance\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 361\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrainer\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mprofile(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_training_epoch\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m 362\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_data_fetcher \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 363\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mepoch_loop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_data_fetcher\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/Software/bat_detectors/batdetect2/.venv/lib/python3.12/site-packages/pytorch_lightning/loops/training_epoch_loop.py:140\u001b[0m, in \u001b[0;36m_TrainingEpochLoop.run\u001b[0;34m(self, data_fetcher)\u001b[0m\n\u001b[1;32m 138\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdone:\n\u001b[1;32m 139\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 140\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madvance\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata_fetcher\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 141\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mon_advance_end(data_fetcher)\n\u001b[1;32m 142\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_restarting \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
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"File \u001b[0;32m~/Software/bat_detectors/batdetect2/.venv/lib/python3.12/site-packages/pytorch_lightning/loops/training_epoch_loop.py:250\u001b[0m, in \u001b[0;36m_TrainingEpochLoop.advance\u001b[0;34m(self, data_fetcher)\u001b[0m\n\u001b[1;32m 247\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mprofile(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_training_batch\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m 248\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mlightning_module\u001b[38;5;241m.\u001b[39mautomatic_optimization:\n\u001b[1;32m 249\u001b[0m \u001b[38;5;66;03m# in automatic optimization, there can only be one optimizer\u001b[39;00m\n\u001b[0;32m--> 250\u001b[0m batch_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautomatic_optimization\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptimizers\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 251\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 252\u001b[0m batch_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmanual_optimization\u001b[38;5;241m.\u001b[39mrun(kwargs)\n",
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"File \u001b[0;32m~/Software/bat_detectors/batdetect2/.venv/lib/python3.12/site-packages/pytorch_lightning/loops/optimization/automatic.py:190\u001b[0m, in \u001b[0;36m_AutomaticOptimization.run\u001b[0;34m(self, optimizer, batch_idx, kwargs)\u001b[0m\n\u001b[1;32m 183\u001b[0m closure()\n\u001b[1;32m 185\u001b[0m \u001b[38;5;66;03m# ------------------------------\u001b[39;00m\n\u001b[1;32m 186\u001b[0m \u001b[38;5;66;03m# BACKWARD PASS\u001b[39;00m\n\u001b[1;32m 187\u001b[0m \u001b[38;5;66;03m# ------------------------------\u001b[39;00m\n\u001b[1;32m 188\u001b[0m \u001b[38;5;66;03m# gradient update with accumulated gradients\u001b[39;00m\n\u001b[1;32m 189\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 190\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_optimizer_step\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclosure\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 192\u001b[0m result \u001b[38;5;241m=\u001b[39m closure\u001b[38;5;241m.\u001b[39mconsume_result()\n\u001b[1;32m 193\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result\u001b[38;5;241m.\u001b[39mloss \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
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"File \u001b[0;32m~/Software/bat_detectors/batdetect2/.venv/lib/python3.12/site-packages/pytorch_lightning/loops/optimization/automatic.py:268\u001b[0m, in \u001b[0;36m_AutomaticOptimization._optimizer_step\u001b[0;34m(self, batch_idx, train_step_and_backward_closure)\u001b[0m\n\u001b[1;32m 265\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptim_progress\u001b[38;5;241m.\u001b[39moptimizer\u001b[38;5;241m.\u001b[39mstep\u001b[38;5;241m.\u001b[39mincrement_ready()\n\u001b[1;32m 267\u001b[0m \u001b[38;5;66;03m# model hook\u001b[39;00m\n\u001b[0;32m--> 268\u001b[0m \u001b[43mcall\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_lightning_module_hook\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 269\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrainer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 270\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43moptimizer_step\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 271\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcurrent_epoch\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 272\u001b[0m \u001b[43m \u001b[49m\u001b[43mbatch_idx\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 273\u001b[0m \u001b[43m \u001b[49m\u001b[43moptimizer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 274\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrain_step_and_backward_closure\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 275\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 277\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m should_accumulate:\n\u001b[1;32m 278\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptim_progress\u001b[38;5;241m.\u001b[39moptimizer\u001b[38;5;241m.\u001b[39mstep\u001b[38;5;241m.\u001b[39mincrement_completed()\n",
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"File \u001b[0;32m~/Software/bat_detectors/batdetect2/.venv/lib/python3.12/site-packages/pytorch_lightning/trainer/call.py:167\u001b[0m, in \u001b[0;36m_call_lightning_module_hook\u001b[0;34m(trainer, hook_name, pl_module, *args, **kwargs)\u001b[0m\n\u001b[1;32m 164\u001b[0m pl_module\u001b[38;5;241m.\u001b[39m_current_fx_name \u001b[38;5;241m=\u001b[39m hook_name\n\u001b[1;32m 166\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mprofile(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m[LightningModule]\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpl_module\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mhook_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m--> 167\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 169\u001b[0m \u001b[38;5;66;03m# restore current_fx when nested context\u001b[39;00m\n\u001b[1;32m 170\u001b[0m pl_module\u001b[38;5;241m.\u001b[39m_current_fx_name \u001b[38;5;241m=\u001b[39m prev_fx_name\n",
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"File \u001b[0;32m~/Software/bat_detectors/batdetect2/.venv/lib/python3.12/site-packages/pytorch_lightning/core/module.py:1306\u001b[0m, in \u001b[0;36mLightningModule.optimizer_step\u001b[0;34m(self, epoch, batch_idx, optimizer, optimizer_closure)\u001b[0m\n\u001b[1;32m 1275\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21moptimizer_step\u001b[39m(\n\u001b[1;32m 1276\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 1277\u001b[0m epoch: \u001b[38;5;28mint\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1280\u001b[0m optimizer_closure: Optional[Callable[[], Any]] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 1281\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1282\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"Override this method to adjust the default way the :class:`~pytorch_lightning.trainer.trainer.Trainer` calls\u001b[39;00m\n\u001b[1;32m 1283\u001b[0m \u001b[38;5;124;03m the optimizer.\u001b[39;00m\n\u001b[1;32m 1284\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1304\u001b[0m \n\u001b[1;32m 1305\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1306\u001b[0m \u001b[43moptimizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43mclosure\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moptimizer_closure\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/Software/bat_detectors/batdetect2/.venv/lib/python3.12/site-packages/pytorch_lightning/core/optimizer.py:153\u001b[0m, in \u001b[0;36mLightningOptimizer.step\u001b[0;34m(self, closure, **kwargs)\u001b[0m\n\u001b[1;32m 150\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m MisconfigurationException(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWhen `optimizer.step(closure)` is called, the closure should be callable\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 152\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_strategy \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 153\u001b[0m step_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_strategy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptimizer_step\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_optimizer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclosure\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 155\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_on_after_step()\n\u001b[1;32m 157\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m step_output\n",
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"File \u001b[0;32m~/Software/bat_detectors/batdetect2/.venv/lib/python3.12/site-packages/pytorch_lightning/strategies/strategy.py:238\u001b[0m, in \u001b[0;36mStrategy.optimizer_step\u001b[0;34m(self, optimizer, closure, model, **kwargs)\u001b[0m\n\u001b[1;32m 236\u001b[0m \u001b[38;5;66;03m# TODO(fabric): remove assertion once strategy's optimizer_step typing is fixed\u001b[39;00m\n\u001b[1;32m 237\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(model, pl\u001b[38;5;241m.\u001b[39mLightningModule)\n\u001b[0;32m--> 238\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprecision_plugin\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptimizer_step\u001b[49m\u001b[43m(\u001b[49m\u001b[43moptimizer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclosure\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclosure\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/Software/bat_detectors/batdetect2/.venv/lib/python3.12/site-packages/pytorch_lightning/plugins/precision/precision.py:122\u001b[0m, in \u001b[0;36mPrecision.optimizer_step\u001b[0;34m(self, optimizer, model, closure, **kwargs)\u001b[0m\n\u001b[1;32m 120\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Hook to run the optimizer step.\"\"\"\u001b[39;00m\n\u001b[1;32m 121\u001b[0m closure \u001b[38;5;241m=\u001b[39m partial(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_wrap_closure, model, optimizer, closure)\n\u001b[0;32m--> 122\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43moptimizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43mclosure\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclosure\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/Software/bat_detectors/batdetect2/.venv/lib/python3.12/site-packages/torch/optim/lr_scheduler.py:130\u001b[0m, in \u001b[0;36mLRScheduler.__init__.<locals>.patch_track_step_called.<locals>.wrap_step.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 128\u001b[0m opt \u001b[38;5;241m=\u001b[39m opt_ref()\n\u001b[1;32m 129\u001b[0m opt\u001b[38;5;241m.\u001b[39m_opt_called \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m \u001b[38;5;66;03m# type: ignore[union-attr]\u001b[39;00m\n\u001b[0;32m--> 130\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__get__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mopt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mopt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;18;43m__class__\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/Software/bat_detectors/batdetect2/.venv/lib/python3.12/site-packages/torch/optim/optimizer.py:484\u001b[0m, in \u001b[0;36mOptimizer.profile_hook_step.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 479\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 480\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m 481\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must return None or a tuple of (new_args, new_kwargs), but got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresult\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 482\u001b[0m )\n\u001b[0;32m--> 484\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 485\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_optimizer_step_code()\n\u001b[1;32m 487\u001b[0m \u001b[38;5;66;03m# call optimizer step post hooks\u001b[39;00m\n",
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"File \u001b[0;32m~/Software/bat_detectors/batdetect2/.venv/lib/python3.12/site-packages/torch/optim/optimizer.py:89\u001b[0m, in \u001b[0;36m_use_grad_for_differentiable.<locals>._use_grad\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 87\u001b[0m torch\u001b[38;5;241m.\u001b[39mset_grad_enabled(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdefaults[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdifferentiable\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[1;32m 88\u001b[0m torch\u001b[38;5;241m.\u001b[39m_dynamo\u001b[38;5;241m.\u001b[39mgraph_break()\n\u001b[0;32m---> 89\u001b[0m ret \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 90\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 91\u001b[0m torch\u001b[38;5;241m.\u001b[39m_dynamo\u001b[38;5;241m.\u001b[39mgraph_break()\n",
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"File \u001b[0;32m~/Software/bat_detectors/batdetect2/.venv/lib/python3.12/site-packages/torch/optim/adam.py:205\u001b[0m, in \u001b[0;36mAdam.step\u001b[0;34m(self, closure)\u001b[0m\n\u001b[1;32m 203\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m closure \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 204\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39menable_grad():\n\u001b[0;32m--> 205\u001b[0m loss \u001b[38;5;241m=\u001b[39m \u001b[43mclosure\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 207\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m group \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mparam_groups:\n\u001b[1;32m 208\u001b[0m params_with_grad: List[Tensor] \u001b[38;5;241m=\u001b[39m []\n",
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"File \u001b[0;32m~/Software/bat_detectors/batdetect2/.venv/lib/python3.12/site-packages/pytorch_lightning/plugins/precision/precision.py:108\u001b[0m, in \u001b[0;36mPrecision._wrap_closure\u001b[0;34m(self, model, optimizer, closure)\u001b[0m\n\u001b[1;32m 95\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_wrap_closure\u001b[39m(\n\u001b[1;32m 96\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 97\u001b[0m model: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpl.LightningModule\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 98\u001b[0m optimizer: Steppable,\n\u001b[1;32m 99\u001b[0m closure: Callable[[], Any],\n\u001b[1;32m 100\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[1;32m 101\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"This double-closure allows makes sure the ``closure`` is executed before the ``on_before_optimizer_step``\u001b[39;00m\n\u001b[1;32m 102\u001b[0m \u001b[38;5;124;03m hook is called.\u001b[39;00m\n\u001b[1;32m 103\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 106\u001b[0m \n\u001b[1;32m 107\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 108\u001b[0m closure_result \u001b[38;5;241m=\u001b[39m \u001b[43mclosure\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 109\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_after_closure(model, optimizer)\n\u001b[1;32m 110\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m closure_result\n",
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"File \u001b[0;32m~/Software/bat_detectors/batdetect2/.venv/lib/python3.12/site-packages/pytorch_lightning/loops/optimization/automatic.py:144\u001b[0m, in \u001b[0;36mClosure.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 142\u001b[0m \u001b[38;5;129m@override\u001b[39m\n\u001b[1;32m 143\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs: Any, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Optional[Tensor]:\n\u001b[0;32m--> 144\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclosure\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 145\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_result\u001b[38;5;241m.\u001b[39mloss\n",
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"File \u001b[0;32m~/Software/bat_detectors/batdetect2/.venv/lib/python3.12/site-packages/torch/utils/_contextlib.py:116\u001b[0m, in \u001b[0;36mcontext_decorator.<locals>.decorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 113\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m 114\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdecorate_context\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 115\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m ctx_factory():\n\u001b[0;32m--> 116\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/Software/bat_detectors/batdetect2/.venv/lib/python3.12/site-packages/pytorch_lightning/loops/optimization/automatic.py:129\u001b[0m, in \u001b[0;36mClosure.closure\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 126\u001b[0m \u001b[38;5;129m@override\u001b[39m\n\u001b[1;32m 127\u001b[0m \u001b[38;5;129m@torch\u001b[39m\u001b[38;5;241m.\u001b[39menable_grad()\n\u001b[1;32m 128\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mclosure\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs: Any, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ClosureResult:\n\u001b[0;32m--> 129\u001b[0m step_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_step_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 131\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m step_output\u001b[38;5;241m.\u001b[39mclosure_loss \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 132\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwarning_cache\u001b[38;5;241m.\u001b[39mwarn(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m`training_step` returned `None`. If this was on purpose, ignore this warning...\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
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"File \u001b[0;32m~/Software/bat_detectors/batdetect2/.venv/lib/python3.12/site-packages/pytorch_lightning/loops/optimization/automatic.py:317\u001b[0m, in \u001b[0;36m_AutomaticOptimization._training_step\u001b[0;34m(self, kwargs)\u001b[0m\n\u001b[1;32m 306\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Performs the actual train step with the tied hooks.\u001b[39;00m\n\u001b[1;32m 307\u001b[0m \n\u001b[1;32m 308\u001b[0m \u001b[38;5;124;03mArgs:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 313\u001b[0m \n\u001b[1;32m 314\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 315\u001b[0m trainer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrainer\n\u001b[0;32m--> 317\u001b[0m training_step_output \u001b[38;5;241m=\u001b[39m \u001b[43mcall\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_strategy_hook\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrainer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtraining_step\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 318\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrainer\u001b[38;5;241m.\u001b[39mstrategy\u001b[38;5;241m.\u001b[39mpost_training_step() \u001b[38;5;66;03m# unused hook - call anyway for backward compatibility\u001b[39;00m\n\u001b[1;32m 320\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m training_step_output \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mworld_size \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n",
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"File \u001b[0;32m~/Software/bat_detectors/batdetect2/.venv/lib/python3.12/site-packages/pytorch_lightning/trainer/call.py:319\u001b[0m, in \u001b[0;36m_call_strategy_hook\u001b[0;34m(trainer, hook_name, *args, **kwargs)\u001b[0m\n\u001b[1;32m 316\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 318\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mprofile(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m[Strategy]\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtrainer\u001b[38;5;241m.\u001b[39mstrategy\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mhook_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m--> 319\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 321\u001b[0m \u001b[38;5;66;03m# restore current_fx when nested context\u001b[39;00m\n\u001b[1;32m 322\u001b[0m pl_module\u001b[38;5;241m.\u001b[39m_current_fx_name \u001b[38;5;241m=\u001b[39m prev_fx_name\n",
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"File \u001b[0;32m~/Software/bat_detectors/batdetect2/.venv/lib/python3.12/site-packages/pytorch_lightning/strategies/strategy.py:390\u001b[0m, in \u001b[0;36mStrategy.training_step\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 388\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlightning_module:\n\u001b[1;32m 389\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_redirection(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlightning_module, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtraining_step\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m--> 390\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlightning_module\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtraining_step\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/Software/bat_detectors/batdetect2/batdetect2/train/modules.py:167\u001b[0m, in \u001b[0;36mDetectorModel.training_step\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m 165\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mtraining_step\u001b[39m(\u001b[38;5;28mself\u001b[39m, batch: TrainExample):\n\u001b[1;32m 166\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mforward(batch\u001b[38;5;241m.\u001b[39mspec)\n\u001b[0;32m--> 167\u001b[0m loss \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompute_loss\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 168\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m loss\n",
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"File \u001b[0;32m~/Software/bat_detectors/batdetect2/batdetect2/train/modules.py:150\u001b[0m, in \u001b[0;36mDetectorModel.compute_loss\u001b[0;34m(self, outputs, batch)\u001b[0m\n\u001b[1;32m 147\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mclass props shape\u001b[39m\u001b[38;5;124m\"\u001b[39m, outputs\u001b[38;5;241m.\u001b[39mclass_probs\u001b[38;5;241m.\u001b[39mshape)\n\u001b[1;32m 149\u001b[0m valid_mask \u001b[38;5;241m=\u001b[39m batch\u001b[38;5;241m.\u001b[39mclass_heatmap\u001b[38;5;241m.\u001b[39many(dim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m, keepdim\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\u001b[38;5;241m.\u001b[39mfloat()\n\u001b[0;32m--> 150\u001b[0m classification_loss \u001b[38;5;241m=\u001b[39m \u001b[43mlosses\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfocal_loss\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 151\u001b[0m \u001b[43m \u001b[49m\u001b[43moutputs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclass_probs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 152\u001b[0m \u001b[43m \u001b[49m\u001b[43mbatch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclass_heatmap\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 153\u001b[0m \u001b[43m \u001b[49m\u001b[43mweights\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclass_weights\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 154\u001b[0m \u001b[43m \u001b[49m\u001b[43mvalid_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalid_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 155\u001b[0m \u001b[43m \u001b[49m\u001b[43mbeta\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclassification\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfocal\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbeta\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 156\u001b[0m \u001b[43m \u001b[49m\u001b[43malpha\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclassification\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfocal\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43malpha\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 157\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 159\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m (\n\u001b[1;32m 160\u001b[0m detection_loss \u001b[38;5;241m*\u001b[39m conf\u001b[38;5;241m.\u001b[39mdetection\u001b[38;5;241m.\u001b[39mweight\n\u001b[1;32m 161\u001b[0m \u001b[38;5;241m+\u001b[39m size_loss \u001b[38;5;241m*\u001b[39m conf\u001b[38;5;241m.\u001b[39msize\u001b[38;5;241m.\u001b[39mweight\n\u001b[1;32m 162\u001b[0m \u001b[38;5;241m+\u001b[39m classification_loss \u001b[38;5;241m*\u001b[39m conf\u001b[38;5;241m.\u001b[39mclassification\u001b[38;5;241m.\u001b[39mweight\n\u001b[1;32m 163\u001b[0m )\n",
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|
||||||
"File \u001b[0;32m~/Software/bat_detectors/batdetect2/batdetect2/train/losses.py:38\u001b[0m, in \u001b[0;36mfocal_loss\u001b[0;34m(pred, gt, weights, valid_mask, eps, beta, alpha)\u001b[0m\n\u001b[1;32m 35\u001b[0m pos_inds \u001b[38;5;241m=\u001b[39m gt\u001b[38;5;241m.\u001b[39meq(\u001b[38;5;241m1\u001b[39m)\u001b[38;5;241m.\u001b[39mfloat()\n\u001b[1;32m 36\u001b[0m neg_inds \u001b[38;5;241m=\u001b[39m gt\u001b[38;5;241m.\u001b[39mlt(\u001b[38;5;241m1\u001b[39m)\u001b[38;5;241m.\u001b[39mfloat()\n\u001b[0;32m---> 38\u001b[0m pos_loss \u001b[38;5;241m=\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpred\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43meps\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpow\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mpred\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43malpha\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mpos_inds\u001b[49m\n\u001b[1;32m 39\u001b[0m neg_loss \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 40\u001b[0m torch\u001b[38;5;241m.\u001b[39mlog(\u001b[38;5;241m1\u001b[39m \u001b[38;5;241m-\u001b[39m pred \u001b[38;5;241m+\u001b[39m eps)\n\u001b[1;32m 41\u001b[0m \u001b[38;5;241m*\u001b[39m torch\u001b[38;5;241m.\u001b[39mpow(pred, alpha)\n\u001b[1;32m 42\u001b[0m \u001b[38;5;241m*\u001b[39m torch\u001b[38;5;241m.\u001b[39mpow(\u001b[38;5;241m1\u001b[39m \u001b[38;5;241m-\u001b[39m gt, beta)\n\u001b[1;32m 43\u001b[0m \u001b[38;5;241m*\u001b[39m neg_inds\n\u001b[1;32m 44\u001b[0m )\n\u001b[1;32m 46\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m weights \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
|
|
||||||
"\u001b[0;31mRuntimeError\u001b[0m: The size of tensor a (5) must match the size of tensor b (4) at non-singleton dimension 1"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"trainer.fit(detector, train_dataloaders=train_dataloader)"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": null,
|
|
||||||
"id": "2f6924db-e520-49a1-bbe8-6c4956e46314",
|
|
||||||
"metadata": {
|
|
||||||
"execution": {
|
|
||||||
"iopub.status.busy": "2024-11-19T17:33:10.811729Z",
|
|
||||||
"iopub.status.idle": "2024-11-19T17:33:10.811955Z",
|
|
||||||
"shell.execute_reply": "2024-11-19T17:33:10.811858Z",
|
|
||||||
"shell.execute_reply.started": "2024-11-19T17:33:10.811849Z"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"clip_annotation = train_dataset.get_clip_annotation(0)"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": null,
|
|
||||||
"id": "23943e13-6875-49b8-9f18-2ba6528aa673",
|
|
||||||
"metadata": {
|
|
||||||
"execution": {
|
|
||||||
"iopub.status.busy": "2024-11-19T17:33:10.812924Z",
|
|
||||||
"iopub.status.idle": "2024-11-19T17:33:10.813260Z",
|
|
||||||
"shell.execute_reply": "2024-11-19T17:33:10.813104Z",
|
|
||||||
"shell.execute_reply.started": "2024-11-19T17:33:10.813087Z"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"spec = detector.compute_spectrogram(clip_annotation.clip)\n",
|
|
||||||
"outputs = detector(torch.tensor(spec.values).unsqueeze(0).unsqueeze(0))"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": null,
|
|
||||||
"id": "dd1fe346-0873-4b14-ae1b-92ef1f4f27a5",
|
|
||||||
"metadata": {
|
|
||||||
"execution": {
|
|
||||||
"iopub.status.busy": "2024-11-19T17:33:10.814343Z",
|
|
||||||
"iopub.status.idle": "2024-11-19T17:33:10.814806Z",
|
|
||||||
"shell.execute_reply": "2024-11-19T17:33:10.814628Z",
|
|
||||||
"shell.execute_reply.started": "2024-11-19T17:33:10.814611Z"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"_, ax= plt.subplots(figsize=(15, 5))\n",
|
|
||||||
"spec.plot(ax=ax, add_colorbar=False)\n",
|
|
||||||
"ax.pcolormesh(spec.time, spec.frequency, outputs.detection_probs.detach().squeeze())"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": null,
|
|
||||||
"id": "eadd36ef-a04a-4665-b703-cec84cf1673b",
|
|
||||||
"metadata": {
|
|
||||||
"execution": {
|
|
||||||
"iopub.status.busy": "2024-11-19T17:33:10.815603Z",
|
|
||||||
"iopub.status.idle": "2024-11-19T17:33:10.816065Z",
|
|
||||||
"shell.execute_reply": "2024-11-19T17:33:10.815894Z",
|
|
||||||
"shell.execute_reply.started": "2024-11-19T17:33:10.815877Z"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"print(f\"Num predicted soundevents: {len(predictions.sound_events)}\")"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": null,
|
|
||||||
"id": "e4e54f3e-6ddc-4fe5-8ce0-b527ff6f18ae",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": []
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"metadata": {
|
|
||||||
"kernelspec": {
|
|
||||||
"display_name": "batdetect2-dev",
|
|
||||||
"language": "python",
|
|
||||||
"name": "batdetect2-dev"
|
|
||||||
},
|
|
||||||
"language_info": {
|
|
||||||
"codemirror_mode": {
|
|
||||||
"name": "ipython",
|
|
||||||
"version": 3
|
|
||||||
},
|
|
||||||
"file_extension": ".py",
|
|
||||||
"mimetype": "text/x-python",
|
|
||||||
"name": "python",
|
|
||||||
"nbconvert_exporter": "python",
|
|
||||||
"pygments_lexer": "ipython3",
|
|
||||||
"version": "3.12.5"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"nbformat": 4,
|
|
||||||
"nbformat_minor": 5
|
|
||||||
}
|
|
||||||
@ -1,194 +0,0 @@
|
|||||||
import marimo
|
|
||||||
|
|
||||||
__generated_with = "0.14.16"
|
|
||||||
app = marimo.App(width="medium")
|
|
||||||
|
|
||||||
|
|
||||||
@app.cell
|
|
||||||
def _():
|
|
||||||
import marimo as mo
|
|
||||||
return (mo,)
|
|
||||||
|
|
||||||
|
|
||||||
@app.cell
|
|
||||||
def _():
|
|
||||||
from batdetect2.data import (
|
|
||||||
load_dataset_config,
|
|
||||||
load_dataset,
|
|
||||||
extract_recordings_df,
|
|
||||||
extract_sound_events_df,
|
|
||||||
compute_class_summary,
|
|
||||||
)
|
|
||||||
return (
|
|
||||||
compute_class_summary,
|
|
||||||
extract_recordings_df,
|
|
||||||
extract_sound_events_df,
|
|
||||||
load_dataset,
|
|
||||||
load_dataset_config,
|
|
||||||
)
|
|
||||||
|
|
||||||
|
|
||||||
@app.cell
|
|
||||||
def _(mo):
|
|
||||||
dataset_config_browser = mo.ui.file_browser(
|
|
||||||
selection_mode="file",
|
|
||||||
multiple=False,
|
|
||||||
)
|
|
||||||
dataset_config_browser
|
|
||||||
return (dataset_config_browser,)
|
|
||||||
|
|
||||||
|
|
||||||
@app.cell
|
|
||||||
def _(dataset_config_browser, load_dataset_config, mo):
|
|
||||||
mo.stop(dataset_config_browser.path() is None)
|
|
||||||
dataset_config = load_dataset_config(dataset_config_browser.path())
|
|
||||||
return (dataset_config,)
|
|
||||||
|
|
||||||
|
|
||||||
@app.cell
|
|
||||||
def _(dataset_config, load_dataset):
|
|
||||||
dataset = load_dataset(dataset_config, base_dir="../paper/")
|
|
||||||
return (dataset,)
|
|
||||||
|
|
||||||
|
|
||||||
@app.cell
|
|
||||||
def _():
|
|
||||||
from batdetect2.targets import load_target_config, build_targets
|
|
||||||
return build_targets, load_target_config
|
|
||||||
|
|
||||||
|
|
||||||
@app.cell
|
|
||||||
def _(mo):
|
|
||||||
targets_config_browser = mo.ui.file_browser(
|
|
||||||
selection_mode="file",
|
|
||||||
multiple=False,
|
|
||||||
)
|
|
||||||
targets_config_browser
|
|
||||||
return (targets_config_browser,)
|
|
||||||
|
|
||||||
|
|
||||||
@app.cell
|
|
||||||
def _(load_target_config, mo, targets_config_browser):
|
|
||||||
mo.stop(targets_config_browser.path() is None)
|
|
||||||
targets_config = load_target_config(targets_config_browser.path())
|
|
||||||
return (targets_config,)
|
|
||||||
|
|
||||||
|
|
||||||
@app.cell
|
|
||||||
def _(build_targets, targets_config):
|
|
||||||
targets = build_targets(targets_config)
|
|
||||||
return (targets,)
|
|
||||||
|
|
||||||
|
|
||||||
@app.cell
|
|
||||||
def _():
|
|
||||||
import pandas as pd
|
|
||||||
from soundevent.geometry import compute_bounds
|
|
||||||
return
|
|
||||||
|
|
||||||
|
|
||||||
@app.cell
|
|
||||||
def _(dataset, extract_recordings_df):
|
|
||||||
recordings = extract_recordings_df(dataset)
|
|
||||||
recordings
|
|
||||||
return
|
|
||||||
|
|
||||||
|
|
||||||
@app.cell
|
|
||||||
def _(dataset, extract_sound_events_df, targets):
|
|
||||||
sound_events = extract_sound_events_df(dataset, targets)
|
|
||||||
sound_events
|
|
||||||
return
|
|
||||||
|
|
||||||
|
|
||||||
@app.cell
|
|
||||||
def _(compute_class_summary, dataset, targets):
|
|
||||||
compute_class_summary(dataset, targets)
|
|
||||||
return
|
|
||||||
|
|
||||||
|
|
||||||
@app.cell
|
|
||||||
def _():
|
|
||||||
from batdetect2.data.split import split_dataset_by_recordings
|
|
||||||
return (split_dataset_by_recordings,)
|
|
||||||
|
|
||||||
|
|
||||||
@app.cell
|
|
||||||
def _(dataset, split_dataset_by_recordings, targets):
|
|
||||||
train_dataset, val_dataset = split_dataset_by_recordings(dataset, targets, random_state=42)
|
|
||||||
return train_dataset, val_dataset
|
|
||||||
|
|
||||||
|
|
||||||
@app.cell
|
|
||||||
def _(compute_class_summary, targets, train_dataset):
|
|
||||||
compute_class_summary(train_dataset, targets)
|
|
||||||
return
|
|
||||||
|
|
||||||
|
|
||||||
@app.cell
|
|
||||||
def _(compute_class_summary, targets, val_dataset):
|
|
||||||
compute_class_summary(val_dataset, targets)
|
|
||||||
return
|
|
||||||
|
|
||||||
|
|
||||||
@app.cell
|
|
||||||
def _():
|
|
||||||
from soundevent import io, data
|
|
||||||
from pathlib import Path
|
|
||||||
return Path, data, io
|
|
||||||
|
|
||||||
|
|
||||||
@app.cell
|
|
||||||
def _(Path, data, io, train_dataset):
|
|
||||||
io.save(
|
|
||||||
data.AnnotationSet(
|
|
||||||
name="batdetect2_tuning_train",
|
|
||||||
description="Set of annotations used as the train dataset for the hyper-parameter tuning stage.",
|
|
||||||
clip_annotations=train_dataset,
|
|
||||||
),
|
|
||||||
Path("../paper/data/datasets/annotation_sets/tuning_train.json"),
|
|
||||||
audio_dir=Path("../paper/data/datasets/"),
|
|
||||||
)
|
|
||||||
return
|
|
||||||
|
|
||||||
|
|
||||||
@app.cell
|
|
||||||
def _(Path, data, io, val_dataset):
|
|
||||||
io.save(
|
|
||||||
data.AnnotationSet(
|
|
||||||
name="batdetect2_tuning_val",
|
|
||||||
description="Set of annotations used as the validation dataset for the hyper-parameter tuning stage.",
|
|
||||||
clip_annotations=val_dataset,
|
|
||||||
),
|
|
||||||
Path("../paper/data/datasets/annotation_sets/tuning_val.json"),
|
|
||||||
audio_dir=Path("../paper/data/datasets/"),
|
|
||||||
)
|
|
||||||
return
|
|
||||||
|
|
||||||
|
|
||||||
@app.cell
|
|
||||||
def _(load_dataset, load_dataset_config):
|
|
||||||
config = load_dataset_config("../paper/conf/datasets/train/uk_tune.yaml")
|
|
||||||
rec = load_dataset(config, base_dir="../paper/")
|
|
||||||
return (rec,)
|
|
||||||
|
|
||||||
|
|
||||||
@app.cell
|
|
||||||
def _(rec):
|
|
||||||
dict(rec[0].sound_events[0].tags[0].term)
|
|
||||||
return
|
|
||||||
|
|
||||||
|
|
||||||
@app.cell
|
|
||||||
def _(compute_class_summary, rec, targets):
|
|
||||||
compute_class_summary(rec,targets)
|
|
||||||
return
|
|
||||||
|
|
||||||
|
|
||||||
@app.cell
|
|
||||||
def _():
|
|
||||||
return
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
|
||||||
app.run()
|
|
||||||
@ -1,273 +0,0 @@
|
|||||||
import marimo
|
|
||||||
|
|
||||||
__generated_with = "0.14.16"
|
|
||||||
app = marimo.App(width="medium")
|
|
||||||
|
|
||||||
|
|
||||||
@app.cell
|
|
||||||
def _():
|
|
||||||
import marimo as mo
|
|
||||||
return
|
|
||||||
|
|
||||||
|
|
||||||
@app.cell
|
|
||||||
def _():
|
|
||||||
from batdetect2.data import load_dataset_config, load_dataset
|
|
||||||
from batdetect2.preprocess import load_preprocessing_config, build_preprocessor
|
|
||||||
from batdetect2 import api
|
|
||||||
from soundevent import data
|
|
||||||
from batdetect2.evaluate.types import MatchEvaluation
|
|
||||||
from batdetect2.types import Annotation
|
|
||||||
from batdetect2.compat import annotation_to_sound_event_prediction
|
|
||||||
from batdetect2.plotting import (
|
|
||||||
plot_clip,
|
|
||||||
plot_clip_annotation,
|
|
||||||
plot_clip_prediction,
|
|
||||||
plot_matches,
|
|
||||||
plot_false_positive_match,
|
|
||||||
plot_false_negative_match,
|
|
||||||
plot_true_positive_match,
|
|
||||||
plot_cross_trigger_match,
|
|
||||||
)
|
|
||||||
return (
|
|
||||||
MatchEvaluation,
|
|
||||||
annotation_to_sound_event_prediction,
|
|
||||||
api,
|
|
||||||
build_preprocessor,
|
|
||||||
data,
|
|
||||||
load_dataset,
|
|
||||||
load_dataset_config,
|
|
||||||
load_preprocessing_config,
|
|
||||||
plot_clip_annotation,
|
|
||||||
plot_clip_prediction,
|
|
||||||
plot_cross_trigger_match,
|
|
||||||
plot_false_negative_match,
|
|
||||||
plot_false_positive_match,
|
|
||||||
plot_matches,
|
|
||||||
plot_true_positive_match,
|
|
||||||
)
|
|
||||||
|
|
||||||
|
|
||||||
@app.cell
|
|
||||||
def _(build_preprocessor, load_dataset_config, load_preprocessing_config):
|
|
||||||
dataset_config = load_dataset_config(
|
|
||||||
path="example_data/config.yaml", field="datasets.train"
|
|
||||||
)
|
|
||||||
|
|
||||||
preprocessor_config = load_preprocessing_config(
|
|
||||||
path="example_data/config.yaml", field="preprocess"
|
|
||||||
)
|
|
||||||
|
|
||||||
preprocessor = build_preprocessor(preprocessor_config)
|
|
||||||
return dataset_config, preprocessor
|
|
||||||
|
|
||||||
|
|
||||||
@app.cell
|
|
||||||
def _(dataset_config, load_dataset):
|
|
||||||
dataset = load_dataset(dataset_config)
|
|
||||||
return (dataset,)
|
|
||||||
|
|
||||||
|
|
||||||
@app.cell
|
|
||||||
def _(dataset):
|
|
||||||
clip_annotation = dataset[1]
|
|
||||||
return (clip_annotation,)
|
|
||||||
|
|
||||||
|
|
||||||
@app.cell
|
|
||||||
def _(clip_annotation, plot_clip_annotation, preprocessor):
|
|
||||||
plot_clip_annotation(
|
|
||||||
clip_annotation, preprocessor=preprocessor, figsize=(15, 5)
|
|
||||||
)
|
|
||||||
return
|
|
||||||
|
|
||||||
|
|
||||||
@app.cell
|
|
||||||
def _(annotation_to_sound_event_prediction, api, clip_annotation, data):
|
|
||||||
audio = api.load_audio(clip_annotation.clip.recording.path)
|
|
||||||
detections, features, spec = api.process_audio(audio)
|
|
||||||
clip_prediction = data.ClipPrediction(
|
|
||||||
clip=clip_annotation.clip,
|
|
||||||
sound_events=[
|
|
||||||
annotation_to_sound_event_prediction(
|
|
||||||
prediction, clip_annotation.clip.recording
|
|
||||||
)
|
|
||||||
for prediction in detections
|
|
||||||
],
|
|
||||||
)
|
|
||||||
return (clip_prediction,)
|
|
||||||
|
|
||||||
|
|
||||||
@app.cell
|
|
||||||
def _(clip_prediction, plot_clip_prediction):
|
|
||||||
plot_clip_prediction(clip_prediction, figsize=(15, 5))
|
|
||||||
return
|
|
||||||
|
|
||||||
|
|
||||||
@app.cell
|
|
||||||
def _():
|
|
||||||
from batdetect2.evaluate import match_predictions_and_annotations
|
|
||||||
import random
|
|
||||||
return match_predictions_and_annotations, random
|
|
||||||
|
|
||||||
|
|
||||||
@app.cell
|
|
||||||
def _(data, random):
|
|
||||||
def add_noise(clip_annotation, time_buffer=0.003, freq_buffer=1000):
|
|
||||||
def _add_bbox_noise(bbox):
|
|
||||||
start_time, low_freq, end_time, high_freq = bbox.coordinates
|
|
||||||
return data.BoundingBox(
|
|
||||||
coordinates=[
|
|
||||||
start_time + random.uniform(-time_buffer, time_buffer),
|
|
||||||
low_freq + random.uniform(-freq_buffer, freq_buffer),
|
|
||||||
end_time + random.uniform(-time_buffer, time_buffer),
|
|
||||||
high_freq + random.uniform(-freq_buffer, freq_buffer),
|
|
||||||
]
|
|
||||||
)
|
|
||||||
|
|
||||||
def _add_noise(se):
|
|
||||||
return se.model_copy(
|
|
||||||
update=dict(
|
|
||||||
sound_event=se.sound_event.model_copy(
|
|
||||||
update=dict(
|
|
||||||
geometry=_add_bbox_noise(se.sound_event.geometry)
|
|
||||||
)
|
|
||||||
)
|
|
||||||
)
|
|
||||||
)
|
|
||||||
|
|
||||||
return clip_annotation.model_copy(
|
|
||||||
update=dict(
|
|
||||||
sound_events=[
|
|
||||||
_add_noise(se) for se in clip_annotation.sound_events
|
|
||||||
]
|
|
||||||
)
|
|
||||||
)
|
|
||||||
|
|
||||||
|
|
||||||
def drop_random(obj, p=0.5):
|
|
||||||
return obj.model_copy(
|
|
||||||
update=dict(
|
|
||||||
sound_events=[se for se in obj.sound_events if random.random() > p]
|
|
||||||
)
|
|
||||||
)
|
|
||||||
return add_noise, drop_random
|
|
||||||
|
|
||||||
|
|
||||||
@app.cell
|
|
||||||
def _(
|
|
||||||
add_noise,
|
|
||||||
clip_annotation,
|
|
||||||
clip_prediction,
|
|
||||||
drop_random,
|
|
||||||
match_predictions_and_annotations,
|
|
||||||
):
|
|
||||||
|
|
||||||
|
|
||||||
matches = match_predictions_and_annotations(
|
|
||||||
drop_random(add_noise(clip_annotation), p=0.2),
|
|
||||||
drop_random(clip_prediction),
|
|
||||||
)
|
|
||||||
return (matches,)
|
|
||||||
|
|
||||||
|
|
||||||
@app.cell
|
|
||||||
def _(clip_annotation, matches, plot_matches):
|
|
||||||
plot_matches(matches, clip_annotation.clip, figsize=(15, 5))
|
|
||||||
return
|
|
||||||
|
|
||||||
|
|
||||||
@app.cell
|
|
||||||
def _(matches):
|
|
||||||
true_positives = []
|
|
||||||
false_positives = []
|
|
||||||
false_negatives = []
|
|
||||||
|
|
||||||
for match in matches:
|
|
||||||
if match.source is None and match.target is not None:
|
|
||||||
false_negatives.append(match)
|
|
||||||
elif match.target is None and match.source is not None:
|
|
||||||
false_positives.append(match)
|
|
||||||
elif match.target is not None and match.source is not None:
|
|
||||||
true_positives.append(match)
|
|
||||||
else:
|
|
||||||
continue
|
|
||||||
|
|
||||||
return false_negatives, false_positives, true_positives
|
|
||||||
|
|
||||||
|
|
||||||
@app.cell
|
|
||||||
def _(MatchEvaluation, false_positives, plot_false_positive_match):
|
|
||||||
false_positive = false_positives[0]
|
|
||||||
false_positive_eval = MatchEvaluation(
|
|
||||||
match=false_positive,
|
|
||||||
gt_det=False,
|
|
||||||
gt_class=None,
|
|
||||||
pred_score=false_positive.source.score,
|
|
||||||
pred_class_scores={
|
|
||||||
"myomyo": 0.2
|
|
||||||
}
|
|
||||||
)
|
|
||||||
|
|
||||||
plot_false_positive_match(false_positive_eval)
|
|
||||||
return
|
|
||||||
|
|
||||||
|
|
||||||
@app.cell
|
|
||||||
def _(MatchEvaluation, false_negatives, plot_false_negative_match):
|
|
||||||
false_negative = false_negatives[0]
|
|
||||||
false_negative_eval = MatchEvaluation(
|
|
||||||
match=false_negative,
|
|
||||||
gt_det=True,
|
|
||||||
gt_class="myomyo",
|
|
||||||
pred_score=None,
|
|
||||||
pred_class_scores={}
|
|
||||||
)
|
|
||||||
|
|
||||||
plot_false_negative_match(false_negative_eval)
|
|
||||||
|
|
||||||
return
|
|
||||||
|
|
||||||
|
|
||||||
@app.cell
|
|
||||||
def _(MatchEvaluation, plot_true_positive_match, true_positives):
|
|
||||||
true_positive = true_positives[0]
|
|
||||||
true_positive_eval = MatchEvaluation(
|
|
||||||
match=true_positive,
|
|
||||||
gt_det=True,
|
|
||||||
gt_class="myomyo",
|
|
||||||
pred_score=0.87,
|
|
||||||
pred_class_scores={
|
|
||||||
"pyomyo": 0.84,
|
|
||||||
"pippip": 0.84,
|
|
||||||
}
|
|
||||||
)
|
|
||||||
|
|
||||||
plot_true_positive_match(true_positive_eval)
|
|
||||||
return (true_positive,)
|
|
||||||
|
|
||||||
|
|
||||||
@app.cell
|
|
||||||
def _(MatchEvaluation, plot_cross_trigger_match, true_positive):
|
|
||||||
cross_trigger_eval = MatchEvaluation(
|
|
||||||
match=true_positive,
|
|
||||||
gt_det=True,
|
|
||||||
gt_class="myomyo",
|
|
||||||
pred_score=0.87,
|
|
||||||
pred_class_scores={
|
|
||||||
"pippip": 0.84,
|
|
||||||
"myomyo": 0.84,
|
|
||||||
}
|
|
||||||
)
|
|
||||||
|
|
||||||
plot_cross_trigger_match(cross_trigger_eval)
|
|
||||||
return
|
|
||||||
|
|
||||||
|
|
||||||
@app.cell
|
|
||||||
def _():
|
|
||||||
return
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
|
||||||
app.run()
|
|
||||||
@ -1,19 +0,0 @@
|
|||||||
import marimo
|
|
||||||
|
|
||||||
__generated_with = "0.13.15"
|
|
||||||
app = marimo.App(width="medium")
|
|
||||||
|
|
||||||
|
|
||||||
@app.cell
|
|
||||||
def _():
|
|
||||||
from batdetect2.preprocess import build_preprocessor
|
|
||||||
return
|
|
||||||
|
|
||||||
|
|
||||||
@app.cell
|
|
||||||
def _():
|
|
||||||
return
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
|
||||||
app.run()
|
|
||||||
@ -1,97 +0,0 @@
|
|||||||
import marimo
|
|
||||||
|
|
||||||
__generated_with = "0.14.5"
|
|
||||||
app = marimo.App(width="medium")
|
|
||||||
|
|
||||||
|
|
||||||
@app.cell
|
|
||||||
def _():
|
|
||||||
import marimo as mo
|
|
||||||
return (mo,)
|
|
||||||
|
|
||||||
|
|
||||||
@app.cell
|
|
||||||
def _():
|
|
||||||
from batdetect2.data import load_dataset, load_dataset_config
|
|
||||||
return load_dataset, load_dataset_config
|
|
||||||
|
|
||||||
|
|
||||||
@app.cell
|
|
||||||
def _(mo):
|
|
||||||
dataset_input = mo.ui.file_browser(label="dataset config file")
|
|
||||||
dataset_input
|
|
||||||
return (dataset_input,)
|
|
||||||
|
|
||||||
|
|
||||||
@app.cell
|
|
||||||
def _(mo):
|
|
||||||
audio_dir_input = mo.ui.file_browser(
|
|
||||||
label="audio directory", selection_mode="directory"
|
|
||||||
)
|
|
||||||
audio_dir_input
|
|
||||||
return (audio_dir_input,)
|
|
||||||
|
|
||||||
|
|
||||||
@app.cell
|
|
||||||
def _(dataset_input, load_dataset_config):
|
|
||||||
dataset_config = load_dataset_config(
|
|
||||||
path=dataset_input.path(), field="datasets.train",
|
|
||||||
)
|
|
||||||
return (dataset_config,)
|
|
||||||
|
|
||||||
|
|
||||||
@app.cell
|
|
||||||
def _(audio_dir_input, dataset_config, load_dataset):
|
|
||||||
dataset = load_dataset(dataset_config, base_dir=audio_dir_input.path())
|
|
||||||
return (dataset,)
|
|
||||||
|
|
||||||
|
|
||||||
@app.cell
|
|
||||||
def _(dataset):
|
|
||||||
len(dataset)
|
|
||||||
return
|
|
||||||
|
|
||||||
|
|
||||||
@app.cell
|
|
||||||
def _(dataset):
|
|
||||||
tag_groups = [
|
|
||||||
se.tags
|
|
||||||
for clip in dataset
|
|
||||||
for se in clip.sound_events
|
|
||||||
if se.tags
|
|
||||||
]
|
|
||||||
all_tags = [
|
|
||||||
tag for group in tag_groups for tag in group
|
|
||||||
]
|
|
||||||
return (all_tags,)
|
|
||||||
|
|
||||||
|
|
||||||
@app.cell
|
|
||||||
def _(mo):
|
|
||||||
key_search = mo.ui.text(label="key", debounce=0.1)
|
|
||||||
value_search = mo.ui.text(label="value", debounce=0.1)
|
|
||||||
mo.hstack([key_search, value_search]).left()
|
|
||||||
return key_search, value_search
|
|
||||||
|
|
||||||
|
|
||||||
@app.cell
|
|
||||||
def _(all_tags, key_search, mo, value_search):
|
|
||||||
filtered_tags = list(set(all_tags))
|
|
||||||
|
|
||||||
if key_search.value:
|
|
||||||
filtered_tags = [tag for tag in filtered_tags if key_search.value.lower() in tag.key.lower()]
|
|
||||||
|
|
||||||
if value_search.value:
|
|
||||||
filtered_tags = [tag for tag in filtered_tags if value_search.value.lower() in tag.value.lower()]
|
|
||||||
|
|
||||||
mo.vstack([mo.md(f"key={tag.key} value={tag.value}") for tag in filtered_tags[:5]])
|
|
||||||
return
|
|
||||||
|
|
||||||
|
|
||||||
@app.cell
|
|
||||||
def _():
|
|
||||||
return
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
|
||||||
app.run()
|
|
||||||
Loading…
Reference in New Issue
Block a user