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Adding notebook folder
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mbsantiago
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.gitignore
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@ -110,5 +110,6 @@ experiments/*
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!batdetect2_notebook.ipynb
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!batdetect2/models/*.pth.tar
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!tests/data/*.wav
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!notebooks/*.ipynb
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notebooks/lightning_logs
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example_data/preprocessed
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notebooks/Training Preprocess.ipynb
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notebooks/Training Preprocess.ipynb
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notebooks/Training.ipynb
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notebooks/Training.ipynb
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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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}
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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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"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/python3.9/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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"\n",
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"import pytorch_lightning as pl\n",
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"from soundevent import data\n",
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"from torch.utils.data import DataLoader\n",
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"\n",
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"from batdetect2.data.labels import ClassMapper\n",
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"from batdetect2.models.detectors 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"
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]
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},
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{
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"cell_type": "markdown",
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"id": "fa202af2-5c0d-4b5d-91a3-097ef5cd4272",
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"metadata": {},
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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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"shell.execute_reply.started": "2024-07-16T00:27:25.874206Z"
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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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"shell.execute_reply.started": "2024-07-16T00:27:25.914410Z"
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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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}
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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": "2024-07-16T00:27:26.003195Z",
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"shell.execute_reply.started": "2024-07-16T00:27:26.003158Z"
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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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}
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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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" \"social\",\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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"execution_count": 9,
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"id": "3a763ee6-15bc-4105-a409-f06e0ad21a06",
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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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"GPU available: False, used: False\n",
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"TPU available: False, using: 0 TPU cores\n",
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"IPU available: False, using: 0 IPUs\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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}
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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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"\n",
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" | Name | Type | Params\n",
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"------------------------------------------------\n",
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"0 | feature_extractor | Net2DFast | 119 K \n",
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"1 | classifier | Conv2d | 54 \n",
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"2 | bbox | Conv2d | 18 \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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]
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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 1: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1.59it/s, v_num=13]"
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]
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},
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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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"`Trainer.fit` stopped: `max_epochs=2` reached.\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 1: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1.54it/s, v_num=13]\n"
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]
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}
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],
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"source": [
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"trainer.fit(detector, train_dataloaders=train_dataloader)"
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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": 11,
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"id": "2f6924db-e520-49a1-bbe8-6c4956e46314",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2024-07-16T00:27:28.832222Z",
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"shell.execute_reply.started": "2024-07-16T00:27:28.832157Z"
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}
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},
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"outputs": [],
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"source": [
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"clip_annotation = train_dataset.get_clip_annotation(0)"
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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": 12,
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"id": "23943e13-6875-49b8-9f18-2ba6528aa673",
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"shell.execute_reply.started": "2024-07-16T00:27:29.004200Z"
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}
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},
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"outputs": [],
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"source": [
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"predictions = detector.compute_clip_predictions(clip_annotation.clip)"
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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": 18,
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"id": "eadd36ef-a04a-4665-b703-cec84cf1673b",
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"metadata": {
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"execution": {
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},
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"outputs": [
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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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"Num predicted soundevents: 50\n"
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]
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}
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],
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"source": [
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"print(f\"Num predicted soundevents: {len(predictions.sound_events)}\")"
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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": "d3883c04-d91a-4d1d-b677-196c0179dde1",
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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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"kernelspec": {
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"display_name": "batdetect2-dev",
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"language": "python",
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"name": "batdetect2-dev"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.18"
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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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