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Lets goo
This commit is contained in:
mbsantiago
parent
113223be02
commit
e9e1f7ce2f
2
conf/config.yaml
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2
conf/config.yaml
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defaults:
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- datasets: diff
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64
conf/datasets/diff.yaml
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64
conf/datasets/diff.yaml
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datasets:
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train:
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- name: Bat Detective
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audio_dir: data/datasets/bat_detective_batdetect2/audio/
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annotations:
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format: batdetect2_file
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path: data/datasets/bat_detective_batdetect2/annotation_sets/train_set_bulgaria_batdetective_with_bbs.json
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- name: QEOP Empty
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audio_dir: data/datasets/bat_logger_qeop_empty/audio/
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annotations:
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format: batdetect2_file
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path: data/datasets/bat_logger_qeop_empty/annotation_sets/bat_logger_qeop_empty.json
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- name: Bat Logger 2016
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audio_dir: data/datasets/bat_logger_2016/audio/
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annotations:
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format: batdetect2_file
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path: data/datasets/bat_logger_2016/annotation_sets/train_set_bat_logger_2016_empty.json
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- name: EchoBank
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audio_dir: data/datasets/echobank_batdetect2/audio/
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annotations:
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format: batdetect2_file
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path: data/datasets/echobank_batdetect2/annotation_sets/Echobank_train_expert.json
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- name: SN Scotland Nor
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audio_dir: data/datasets/sn_scot_nor/audio/
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annotations:
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format: batdetect2_file
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path: data/datasets/sn_scot_nor/annotation_sets/sn_scot_nor_0.5_expert.json
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- name: BCT
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audio_dir: data/datasets/bct_1_sec/audio/
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annotations:
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format: batdetect2_file
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path: data/datasets/bct_1_sec/annotation_sets/BCT_1_sec_train_expert.json
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- name: BCIreland
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audio_dir: data/datasets/bcireland/audio/
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annotations:
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format: batdetect2_file
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path: data/datasets/bcireland/annotation_sets/bcireland_expert.json
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- name: Rhinolophus BCT
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audio_dir: data/datasets/rhinolophus_bct/audio/
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annotations:
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format: batdetect2_file
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path: data/datasets/rhinolophus_bct/annotation_sets/rhinolophus_BCT_expert.json
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test:
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- name: British Bat Calls 2018
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audio_dir: data/datasets/bat_data_2018/audio/
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annotations:
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format: batdetect2_file
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path: data/datasets/bat_data_2018/annotation_sets/BritishBatCalls_2018_1_sec_train_expert.json
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- name: British Bat Calls 2018 Test
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audio_dir: data/datasets/bat_data_2018_test/audio/
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annotations:
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format: batdetect2_file
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path: data/datasets/bat_data_2018_test/annotation_sets/BritishBatCalls_2018_1_sec_test_expert.json
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- name: British Bat Calls 2019
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audio_dir: data/datasets/bat_data_2019/audio/
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annotations:
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format: batdetect2_file
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path: data/datasets/bat_data_2019/annotation_sets/BritishBatCalls_2019_1_sec_train_expert.json
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- name: British Bat Calls 2019 Test
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audio_dir: data/datasets/bat_data_2019/audio/
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annotations:
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format: batdetect2_file
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path: data/datasets/bat_data_2019_test/annotation_sets/BritishBatCalls_2019_1_sec_test_expert.json
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743
notebooks/Augmentations.ipynb
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743
notebooks/Augmentations.ipynb
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notebooks/Migrations.ipynb
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notebooks/Migrations.ipynb
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@ -6,11 +6,11 @@
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"id": "cfb0b360-a204-4c27-a18f-3902e8758879",
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"id": "cfb0b360-a204-4c27-a18f-3902e8758879",
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"metadata": {
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"metadata": {
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"execution": {
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"execution": {
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"iopub.execute_input": "2024-07-16T00:27:20.598611Z",
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"iopub.execute_input": "2024-11-19T17:33:02.699871Z",
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}
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},
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"outputs": [],
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"outputs": [],
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@ -25,11 +25,11 @@
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"id": "326c5432-94e6-4abf-a332-fe902559461b",
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"id": "326c5432-94e6-4abf-a332-fe902559461b",
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"metadata": {
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"metadata": {
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"execution": {
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"execution": {
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"iopub.execute_input": "2024-07-16T00:27:20.676278Z",
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}
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},
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},
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"outputs": [
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"outputs": [
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@ -37,7 +37,7 @@
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"name": "stderr",
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"name": "stderr",
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"output_type": "stream",
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"output_type": "stream",
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"text": [
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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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"/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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" from .autonotebook import tqdm as notebook_tqdm\n"
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]
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]
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}
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}
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@ -45,26 +45,35 @@
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"source": [
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"source": [
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"from pathlib import Path\n",
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"from pathlib import Path\n",
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"from typing import List, Optional\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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"\n",
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"import pytorch_lightning as pl\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 batdetect2.train.modules import DetectorModel\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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"from batdetect2.train.augmentations import (\n",
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" add_echo,\n",
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" add_echo,\n",
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" select_random_subclip,\n",
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" select_random_subclip,\n",
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" warp_spectrogram,\n",
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" warp_spectrogram,\n",
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")\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.dataset import LabeledDataset, get_files\n",
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"from batdetect2.train.preprocess import PreprocessingConfig"
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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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},
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{
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{
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"cell_type": "markdown",
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"cell_type": "markdown",
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"id": "fa202af2-5c0d-4b5d-91a3-097ef5cd4272",
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"id": "9402a473-0b25-4123-9fa8-ad1f71a4237a",
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"metadata": {},
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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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"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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"source": [
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"## Training Datasets"
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"## Training Datasets"
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]
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]
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"id": "cfd97d83-8c2b-46c8-9eae-cea59f53bc61",
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"id": "cfd97d83-8c2b-46c8-9eae-cea59f53bc61",
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"metadata": {
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"metadata": {
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"execution": {
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"outputs": [],
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"outputs": [],
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"id": "d5131ae9-2efd-4758-b6e5-189a6d90789b",
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"id": "bc733d3d-7829-4e90-896d-a0dc76b33288",
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"id": "dfbb94ab-7b12-4689-9c15-4dc34cd17cb2",
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"id": "e2eedaa9-6be3-481a-8786-7618515d98f8",
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"id": "e2eedaa9-6be3-481a-8786-7618515d98f8",
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"metadata": {
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"execution": {
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"outputs": [],
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" \"Myotis mystacinus\",\n",
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" \"Myotis mystacinus\",\n",
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" \"Pipistrellus pipistrellus\",\n",
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" \"Pipistrellus pipistrellus\",\n",
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" \"Rhinolophus ferrumequinum\",\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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"\n",
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"\n",
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" def encode(self, x: data.SoundEventAnnotation) -> Optional[str]:\n",
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" def encode(self, x: data.SoundEventAnnotation) -> Optional[str]:\n",
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"id": "1ff6072c-511e-42fe-a74f-282f269b80f0",
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"id": "1ff6072c-511e-42fe-a74f-282f269b80f0",
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"metadata": {
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"metadata": {
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"execution": {
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"execution": {
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"iopub.execute_input": "2024-07-16T00:27:26.104877Z",
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"iopub.execute_input": "2024-11-19T17:33:09.262337Z",
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|
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"iopub.status.idle": "2024-07-16T00:27:26.159676Z",
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"iopub.status.idle": "2024-11-19T17:33:09.309793Z",
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"shell.execute_reply": "2024-07-16T00:27:26.157914Z",
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@ -215,11 +223,11 @@
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|||||||
"id": "3a763ee6-15bc-4105-a409-f06e0ad21a06",
|
"id": "3a763ee6-15bc-4105-a409-f06e0ad21a06",
|
||||||
"metadata": {
|
"metadata": {
|
||||||
"execution": {
|
"execution": {
|
||||||
"iopub.execute_input": "2024-07-16T00:27:26.162346Z",
|
"iopub.execute_input": "2024-11-19T17:33:09.310695Z",
|
||||||
"iopub.status.busy": "2024-07-16T00:27:26.161885Z",
|
"iopub.status.busy": "2024-11-19T17:33:09.310438Z",
|
||||||
"iopub.status.idle": "2024-07-16T00:27:26.374668Z",
|
"iopub.status.idle": "2024-11-19T17:33:09.366636Z",
|
||||||
"shell.execute_reply": "2024-07-16T00:27:26.373691Z",
|
"shell.execute_reply": "2024-11-19T17:33:09.366059Z",
|
||||||
"shell.execute_reply.started": "2024-07-16T00:27:26.162305Z"
|
"shell.execute_reply.started": "2024-11-19T17:33:09.310669Z"
|
||||||
}
|
}
|
||||||
},
|
},
|
||||||
"outputs": [
|
"outputs": [
|
||||||
@ -229,7 +237,6 @@
|
|||||||
"text": [
|
"text": [
|
||||||
"GPU available: False, used: False\n",
|
"GPU available: False, used: False\n",
|
||||||
"TPU available: False, using: 0 TPU cores\n",
|
"TPU available: False, using: 0 TPU cores\n",
|
||||||
"IPU available: False, using: 0 IPUs\n",
|
|
||||||
"HPU available: False, using: 0 HPUs\n"
|
"HPU available: False, using: 0 HPUs\n"
|
||||||
]
|
]
|
||||||
}
|
}
|
||||||
@ -248,11 +255,11 @@
|
|||||||
"id": "0b86d49d-3314-4257-94f5-f964855be385",
|
"id": "0b86d49d-3314-4257-94f5-f964855be385",
|
||||||
"metadata": {
|
"metadata": {
|
||||||
"execution": {
|
"execution": {
|
||||||
"iopub.execute_input": "2024-07-16T00:27:26.375918Z",
|
"iopub.execute_input": "2024-11-19T17:33:09.367499Z",
|
||||||
"iopub.status.busy": "2024-07-16T00:27:26.375632Z",
|
"iopub.status.busy": "2024-11-19T17:33:09.367242Z",
|
||||||
"iopub.status.idle": "2024-07-16T00:27:28.829650Z",
|
"iopub.status.idle": "2024-11-19T17:33:10.811300Z",
|
||||||
"shell.execute_reply": "2024-07-16T00:27:28.828219Z",
|
"shell.execute_reply": "2024-11-19T17:33:10.809823Z",
|
||||||
"shell.execute_reply.started": "2024-07-16T00:27:26.375889Z"
|
"shell.execute_reply.started": "2024-11-19T17:33:09.367473Z"
|
||||||
}
|
}
|
||||||
},
|
},
|
||||||
"outputs": [
|
"outputs": [
|
||||||
@ -261,37 +268,67 @@
|
|||||||
"output_type": "stream",
|
"output_type": "stream",
|
||||||
"text": [
|
"text": [
|
||||||
"\n",
|
"\n",
|
||||||
" | Name | Type | Params\n",
|
" | Name | Type | Params | Mode \n",
|
||||||
"------------------------------------------------\n",
|
"--------------------------------------------------------\n",
|
||||||
"0 | feature_extractor | Net2DFast | 119 K \n",
|
"0 | feature_extractor | Net2DFast | 119 K | train\n",
|
||||||
"1 | classifier | Conv2d | 54 \n",
|
"1 | classifier | Conv2d | 54 | train\n",
|
||||||
"2 | bbox | Conv2d | 18 \n",
|
"2 | bbox | Conv2d | 18 | train\n",
|
||||||
"------------------------------------------------\n",
|
"--------------------------------------------------------\n",
|
||||||
"119 K Trainable params\n",
|
"119 K Trainable params\n",
|
||||||
"448 Non-trainable params\n",
|
"448 Non-trainable params\n",
|
||||||
"119 K Total params\n",
|
"119 K Total params\n",
|
||||||
"0.480 Total estimated model params size (MB)\n"
|
"0.480 Total estimated model params size (MB)\n",
|
||||||
|
"32 Modules in train mode\n",
|
||||||
|
"0 Modules in eval mode\n"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"name": "stdout",
|
"name": "stdout",
|
||||||
"output_type": "stream",
|
"output_type": "stream",
|
||||||
"text": [
|
"text": [
|
||||||
"Epoch 1: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1.59it/s, v_num=13]"
|
"Epoch 0: 0%| | 0/1 [00:00<?, ?it/s]class heatmap shape torch.Size([3, 4, 128, 512])\n",
|
||||||
|
"class props shape torch.Size([3, 5, 128, 512])\n"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"name": "stderr",
|
"ename": "RuntimeError",
|
||||||
"output_type": "stream",
|
"evalue": "The size of tensor a (5) must match the size of tensor b (4) at non-singleton dimension 1",
|
||||||
"text": [
|
"output_type": "error",
|
||||||
"`Trainer.fit` stopped: `max_epochs=2` reached.\n"
|
"traceback": [
|
||||||
]
|
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
||||||
},
|
"\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
|
||||||
{
|
"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",
|
||||||
"name": "stdout",
|
"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",
|
||||||
"output_type": "stream",
|
"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",
|
||||||
"text": [
|
"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",
|
||||||
"Epoch 1: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1.54it/s, v_num=13]\n"
|
"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",
|
||||||
|
"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",
|
||||||
|
"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",
|
||||||
|
"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",
|
||||||
|
"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",
|
||||||
|
"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",
|
||||||
|
"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"
|
||||||
]
|
]
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
@ -301,15 +338,14 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 11,
|
"execution_count": null,
|
||||||
"id": "2f6924db-e520-49a1-bbe8-6c4956e46314",
|
"id": "2f6924db-e520-49a1-bbe8-6c4956e46314",
|
||||||
"metadata": {
|
"metadata": {
|
||||||
"execution": {
|
"execution": {
|
||||||
"iopub.execute_input": "2024-07-16T00:27:28.832222Z",
|
"iopub.status.busy": "2024-11-19T17:33:10.811729Z",
|
||||||
"iopub.status.busy": "2024-07-16T00:27:28.831642Z",
|
"iopub.status.idle": "2024-11-19T17:33:10.811955Z",
|
||||||
"iopub.status.idle": "2024-07-16T00:27:29.000595Z",
|
"shell.execute_reply": "2024-11-19T17:33:10.811858Z",
|
||||||
"shell.execute_reply": "2024-07-16T00:27:28.998078Z",
|
"shell.execute_reply.started": "2024-11-19T17:33:10.811849Z"
|
||||||
"shell.execute_reply.started": "2024-07-16T00:27:28.832157Z"
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|
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}
|
}
|
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},
|
},
|
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"outputs": [],
|
"outputs": [],
|
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@ -319,44 +355,54 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 12,
|
"execution_count": null,
|
||||||
"id": "23943e13-6875-49b8-9f18-2ba6528aa673",
|
"id": "23943e13-6875-49b8-9f18-2ba6528aa673",
|
||||||
"metadata": {
|
"metadata": {
|
||||||
"execution": {
|
"execution": {
|
||||||
"iopub.execute_input": "2024-07-16T00:27:29.004279Z",
|
"iopub.status.busy": "2024-11-19T17:33:10.812924Z",
|
||||||
"iopub.status.busy": "2024-07-16T00:27:29.003486Z",
|
"iopub.status.idle": "2024-11-19T17:33:10.813260Z",
|
||||||
"iopub.status.idle": "2024-07-16T00:27:29.595626Z",
|
"shell.execute_reply": "2024-11-19T17:33:10.813104Z",
|
||||||
"shell.execute_reply": "2024-07-16T00:27:29.594734Z",
|
"shell.execute_reply.started": "2024-11-19T17:33:10.813087Z"
|
||||||
"shell.execute_reply.started": "2024-07-16T00:27:29.004200Z"
|
|
||||||
}
|
}
|
||||||
},
|
},
|
||||||
"outputs": [],
|
"outputs": [],
|
||||||
"source": [
|
"source": [
|
||||||
"predictions = detector.compute_clip_predictions(clip_annotation.clip)"
|
"spec = detector.compute_spectrogram(clip_annotation.clip)\n",
|
||||||
|
"outputs = detector(torch.tensor(spec.values).unsqueeze(0).unsqueeze(0))"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 18,
|
"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",
|
"id": "eadd36ef-a04a-4665-b703-cec84cf1673b",
|
||||||
"metadata": {
|
"metadata": {
|
||||||
"execution": {
|
"execution": {
|
||||||
"iopub.execute_input": "2024-07-16T00:28:47.178783Z",
|
"iopub.status.busy": "2024-11-19T17:33:10.815603Z",
|
||||||
"iopub.status.busy": "2024-07-16T00:28:47.178143Z",
|
"iopub.status.idle": "2024-11-19T17:33:10.816065Z",
|
||||||
"iopub.status.idle": "2024-07-16T00:28:47.246613Z",
|
"shell.execute_reply": "2024-11-19T17:33:10.815894Z",
|
||||||
"shell.execute_reply": "2024-07-16T00:28:47.245496Z",
|
"shell.execute_reply.started": "2024-11-19T17:33:10.815877Z"
|
||||||
"shell.execute_reply.started": "2024-07-16T00:28:47.178729Z"
|
|
||||||
}
|
}
|
||||||
},
|
},
|
||||||
"outputs": [
|
"outputs": [],
|
||||||
{
|
|
||||||
"name": "stdout",
|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"Num predicted soundevents: 50\n"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
"source": [
|
||||||
"print(f\"Num predicted soundevents: {len(predictions.sound_events)}\")"
|
"print(f\"Num predicted soundevents: {len(predictions.sound_events)}\")"
|
||||||
]
|
]
|
||||||
@ -364,7 +410,7 @@
|
|||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": null,
|
"execution_count": null,
|
||||||
"id": "d3883c04-d91a-4d1d-b677-196c0179dde1",
|
"id": "e4e54f3e-6ddc-4fe5-8ce0-b527ff6f18ae",
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"outputs": [],
|
"outputs": [],
|
||||||
"source": []
|
"source": []
|
||||||
@ -386,7 +432,7 @@
|
|||||||
"name": "python",
|
"name": "python",
|
||||||
"nbconvert_exporter": "python",
|
"nbconvert_exporter": "python",
|
||||||
"pygments_lexer": "ipython3",
|
"pygments_lexer": "ipython3",
|
||||||
"version": "3.9.18"
|
"version": "3.12.5"
|
||||||
}
|
}
|
||||||
},
|
},
|
||||||
"nbformat": 4,
|
"nbformat": 4,
|
||||||
|
|||||||
11
tests/test_configs.py
Normal file
11
tests/test_configs.py
Normal file
@ -0,0 +1,11 @@
|
|||||||
|
from pathlib import Path
|
||||||
|
|
||||||
|
from batdetect2.configs import load_config
|
||||||
|
from batdetect2.data import DatasetsConfig, load_datasets
|
||||||
|
|
||||||
|
|
||||||
|
def test_can_load_dataset_configs():
|
||||||
|
root = Path(__file__).parent.parent
|
||||||
|
path = root / "conf.yaml"
|
||||||
|
config = load_config(path, schema=DatasetsConfig, field="datasets")
|
||||||
|
load_datasets(config)
|
||||||
Loading…
Reference in New Issue
Block a user