CCS intergration training running
notebooks
This commit is contained in:
Steffen
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notebooks/Dataset Analysis.ipynb
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notebooks/Dataset Analysis.ipynb
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notebooks/Train Eval.ipynb
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notebooks/Train Eval.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": 6,
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"outputs": [],
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"source": [
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"from collections import defaultdict\n",
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"from pathlib import Path\n",
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"from natsort import natsorted\n",
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"from pytorch_lightning.core.saving import ModelIO\n",
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"from ml_lib.utils.model_io import SavedLightningModels\n",
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"from ml_lib.utils.tools import locate_and_import_class\n",
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"\n",
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"import yaml\n",
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"\n",
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"import numpy as np\n",
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"import torch\n",
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"import pytorch_lightning as pl\n",
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"import librosa\n",
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"import pandas as pd\n",
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"import variables as v\n",
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"import seaborn as sns\n",
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"from tqdm import tqdm\n",
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"from matplotlib import pyplot as plt"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% Imports go here\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": 12,
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"outputs": [],
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"source": [
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"# Settings and Variables\n",
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"\n",
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"# This Experiment (= Model and Parameter Configuration\n",
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"_ROOT = Path('..')\n",
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"out_path = Path('..') / Path('output')\n",
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"model_name = 'VisualTransformer'\n"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\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": 42,
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"outputs": [],
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"source": [
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"def print_stats(data_option, mean_duration, std_duration, min_duration, max_duration):\n",
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" print(f'For {data_option}; statistics are:')\n",
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" print(f'Scores - mean: {mean_duration:.3f}s\\tstd: {std_duration:.3f}s'\n",
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" f'min: {min_duration:.3f}s\\t max: {max_duration:.3f}s')\n",
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"\n",
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"def print_metrics(exp_path):\n",
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" print(f'--------------{exp_path.name}------------------')\n",
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" best_scores = []\n",
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" had_errors = []\n",
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" for run_folder in [x for x in exp_path.iterdir() if x.is_dir()]:\n",
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" # model_class = locate_and_import_class(model_name, 'models')\n",
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" # sorted_checkpoints = natsorted(run_folder.glob('*.ckpt'))\n",
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" # model = ModelIO.load_from_checkpoint(str(sorted_checkpoints[0]), strict=True)\n",
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" try:\n",
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" metrics = pd.read_csv(run_folder / 'metrics.csv')\n",
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"\n",
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" # Possible keys are:\n",
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" # -- CE - Losses:\n",
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" # val_max_vote_loss, val_mean_vote_loss, mean_val_loss\n",
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" # -- Fallback:\n",
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" # mean_loss,epoch,step,macro_f1_score, macro_roc_auc_ovr, uar_score, micro_f1_score\n",
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" # Pytorch Metrics:\n",
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" # PL_f1_score,PL_accuracy_score_score, PL_fbeta_score,PL_recall_score,PL_precision_score,\n",
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" score = metrics.PL_recall_score[-1]\n",
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" print(f'{exp_path.name} - {run_folder.name}: {score}')\n",
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" best_scores.append(score)\n",
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" had_errors.append(False)\n",
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" except (AttributeError, FileNotFoundError):\n",
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" had_errors.append(True)\n",
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" pass\n",
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" if any(had_errors):\n",
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" return\n",
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" else:\n",
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" print('\\n')\n",
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" stats = np.mean(best_scores), np.std(best_scores), np.min(best_scores), np.max(best_scores)\n",
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" print_stats(exp_path.name, *stats)\n",
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" print('--------------------------------------------')\n"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% Util Functions\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": 32,
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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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"--------------VT_259ee495ee2d2dc0e56bb23d12476f17------------------\n",
|
||||
"VT_259ee495ee2d2dc0e56bb23d12476f17 - version_1: 0.8403531908988953\n",
|
||||
"VT_259ee495ee2d2dc0e56bb23d12476f17 - version_3: 0.8312729001045227\n",
|
||||
"VT_259ee495ee2d2dc0e56bb23d12476f17 - version_0: 0.8342075347900391\n",
|
||||
"VT_259ee495ee2d2dc0e56bb23d12476f17 - version_5: 0.8459098935127258\n",
|
||||
"VT_259ee495ee2d2dc0e56bb23d12476f17 - version_2: 0.8468937277793884\n",
|
||||
"VT_259ee495ee2d2dc0e56bb23d12476f17 - version_4: 0.8404075503349304\n",
|
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"\n",
|
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"\n",
|
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"For VT_259ee495ee2d2dc0e56bb23d12476f17; statistics are:\n",
|
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"Scores - mean: 0.840s\tstd: 0.006smin: 0.831s\t max: 0.847s\n",
|
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"--------------------------------------------\n",
|
||||
"--------------VT_012aff7c1c667073aedafcbebfa35ec7------------------\n",
|
||||
"VT_012aff7c1c667073aedafcbebfa35ec7 - version_6: 0.8637051582336426\n",
|
||||
"VT_012aff7c1c667073aedafcbebfa35ec7 - version_1: 0.864475429058075\n",
|
||||
"VT_012aff7c1c667073aedafcbebfa35ec7 - version_3: 0.854859471321106\n",
|
||||
"VT_012aff7c1c667073aedafcbebfa35ec7 - version_0: 0.8631429672241211\n",
|
||||
"VT_012aff7c1c667073aedafcbebfa35ec7 - version_8: 0.8484407663345337\n",
|
||||
"VT_012aff7c1c667073aedafcbebfa35ec7 - version_5: 0.8564963340759277\n",
|
||||
"VT_012aff7c1c667073aedafcbebfa35ec7 - version_7: 0.8519455194473267\n",
|
||||
"VT_012aff7c1c667073aedafcbebfa35ec7 - version_2: 0.8683117032051086\n",
|
||||
"VT_012aff7c1c667073aedafcbebfa35ec7 - version_9: 0.8730489611625671\n",
|
||||
"VT_012aff7c1c667073aedafcbebfa35ec7 - version_4: 0.8658838272094727\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"For VT_012aff7c1c667073aedafcbebfa35ec7; statistics are:\n",
|
||||
"Scores - mean: 0.861s\tstd: 0.007smin: 0.848s\t max: 0.873s\n",
|
||||
"--------------------------------------------\n",
|
||||
"--------------VT_fdf2a86085b508c1325b181c830a4cf7------------------\n",
|
||||
"VT_fdf2a86085b508c1325b181c830a4cf7 - version_6: 0.854997456073761\n",
|
||||
"VT_fdf2a86085b508c1325b181c830a4cf7 - version_1: 0.8609604835510254\n",
|
||||
"VT_fdf2a86085b508c1325b181c830a4cf7 - version_3: 0.8558254837989807\n",
|
||||
"VT_fdf2a86085b508c1325b181c830a4cf7 - version_0: 0.8728921413421631\n",
|
||||
"VT_fdf2a86085b508c1325b181c830a4cf7 - version_8: 0.8631933927536011\n",
|
||||
"VT_fdf2a86085b508c1325b181c830a4cf7 - version_5: 0.8612215518951416\n",
|
||||
"VT_fdf2a86085b508c1325b181c830a4cf7 - version_7: 0.8661960959434509\n",
|
||||
"VT_fdf2a86085b508c1325b181c830a4cf7 - version_2: 0.8636621832847595\n",
|
||||
"VT_fdf2a86085b508c1325b181c830a4cf7 - version_9: 0.8614727258682251\n",
|
||||
"VT_fdf2a86085b508c1325b181c830a4cf7 - version_4: 0.8657329082489014\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"For VT_fdf2a86085b508c1325b181c830a4cf7; statistics are:\n",
|
||||
"Scores - mean: 0.863s\tstd: 0.005smin: 0.855s\t max: 0.873s\n",
|
||||
"--------------------------------------------\n",
|
||||
"--------------VT_cc64c06847a7ca26f5ea4d465f9cc5bc------------------\n",
|
||||
"VT_cc64c06847a7ca26f5ea4d465f9cc5bc - version_6: 0.8572231531143188\n",
|
||||
"VT_cc64c06847a7ca26f5ea4d465f9cc5bc - version_1: 0.8442623615264893\n",
|
||||
"VT_cc64c06847a7ca26f5ea4d465f9cc5bc - version_3: 0.8498414754867554\n",
|
||||
"VT_cc64c06847a7ca26f5ea4d465f9cc5bc - version_0: 0.8569087982177734\n",
|
||||
"VT_cc64c06847a7ca26f5ea4d465f9cc5bc - version_8: 0.8455194234848022\n",
|
||||
"VT_cc64c06847a7ca26f5ea4d465f9cc5bc - version_5: 0.8435630798339844\n",
|
||||
"VT_cc64c06847a7ca26f5ea4d465f9cc5bc - version_7: 0.845982551574707\n",
|
||||
"VT_cc64c06847a7ca26f5ea4d465f9cc5bc - version_2: 0.8571171164512634\n",
|
||||
"VT_cc64c06847a7ca26f5ea4d465f9cc5bc - version_9: 0.8448543548583984\n",
|
||||
"VT_cc64c06847a7ca26f5ea4d465f9cc5bc - version_4: 0.845399022102356\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"For VT_cc64c06847a7ca26f5ea4d465f9cc5bc; statistics are:\n",
|
||||
"Scores - mean: 0.849s\tstd: 0.005smin: 0.844s\t max: 0.857s\n",
|
||||
"--------------------------------------------\n",
|
||||
"--------------VT_2c7afd50e127f5a2339db0ddfd6bfd7c------------------\n",
|
||||
"VT_2c7afd50e127f5a2339db0ddfd6bfd7c - version_6: 0.8630585670471191\n",
|
||||
"VT_2c7afd50e127f5a2339db0ddfd6bfd7c - version_1: 0.8686699271202087\n",
|
||||
"VT_2c7afd50e127f5a2339db0ddfd6bfd7c - version_3: 0.8729345798492432\n",
|
||||
"VT_2c7afd50e127f5a2339db0ddfd6bfd7c - version_0: 0.8636038899421692\n",
|
||||
"VT_2c7afd50e127f5a2339db0ddfd6bfd7c - version_8: 0.8558077812194824\n",
|
||||
"VT_2c7afd50e127f5a2339db0ddfd6bfd7c - version_5: 0.8710847496986389\n",
|
||||
"VT_2c7afd50e127f5a2339db0ddfd6bfd7c - version_7: 0.8619015216827393\n",
|
||||
"VT_2c7afd50e127f5a2339db0ddfd6bfd7c - version_2: 0.8499867916107178\n",
|
||||
"VT_2c7afd50e127f5a2339db0ddfd6bfd7c - version_9: 0.8507344722747803\n",
|
||||
"VT_2c7afd50e127f5a2339db0ddfd6bfd7c - version_4: 0.8555077314376831\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"For VT_2c7afd50e127f5a2339db0ddfd6bfd7c; statistics are:\n",
|
||||
"Scores - mean: 0.861s\tstd: 0.008smin: 0.850s\t max: 0.873s\n",
|
||||
"--------------------------------------------\n",
|
||||
"--------------VT_63b9fee765cdda91756af1f35cd320a3------------------\n",
|
||||
"VT_63b9fee765cdda91756af1f35cd320a3 - version_6: 0.8663593530654907\n",
|
||||
"VT_63b9fee765cdda91756af1f35cd320a3 - version_1: 0.8519773483276367\n",
|
||||
"VT_63b9fee765cdda91756af1f35cd320a3 - version_3: 0.8519774675369263\n",
|
||||
"VT_63b9fee765cdda91756af1f35cd320a3 - version_0: 0.8603388071060181\n",
|
||||
"VT_63b9fee765cdda91756af1f35cd320a3 - version_8: 0.8614517450332642\n",
|
||||
"VT_63b9fee765cdda91756af1f35cd320a3 - version_5: 0.8558711409568787\n",
|
||||
"VT_63b9fee765cdda91756af1f35cd320a3 - version_7: 0.8537712097167969\n",
|
||||
"VT_63b9fee765cdda91756af1f35cd320a3 - version_2: 0.8558205962181091\n",
|
||||
"VT_63b9fee765cdda91756af1f35cd320a3 - version_9: 0.8647329211235046\n",
|
||||
"VT_63b9fee765cdda91756af1f35cd320a3 - version_4: 0.8546129465103149\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"For VT_63b9fee765cdda91756af1f35cd320a3; statistics are:\n",
|
||||
"Scores - mean: 0.858s\tstd: 0.005smin: 0.852s\t max: 0.866s\n",
|
||||
"--------------------------------------------\n",
|
||||
"--------------VT_aca900a5b9566af61c91aea6525190e6------------------\n",
|
||||
"VT_aca900a5b9566af61c91aea6525190e6 - version_6: 0.8575441241264343\n",
|
||||
"VT_aca900a5b9566af61c91aea6525190e6 - version_1: 0.8453981280326843\n",
|
||||
"VT_aca900a5b9566af61c91aea6525190e6 - version_3: 0.8621359467506409\n",
|
||||
"VT_aca900a5b9566af61c91aea6525190e6 - version_0: 0.8547767400741577\n",
|
||||
"VT_aca900a5b9566af61c91aea6525190e6 - version_8: 0.8613359928131104\n",
|
||||
"VT_aca900a5b9566af61c91aea6525190e6 - version_5: 0.8667657375335693\n",
|
||||
"VT_aca900a5b9566af61c91aea6525190e6 - version_7: 0.8474754095077515\n",
|
||||
"VT_aca900a5b9566af61c91aea6525190e6 - version_2: 0.8628634214401245\n",
|
||||
"VT_aca900a5b9566af61c91aea6525190e6 - version_9: 0.8585749268531799\n",
|
||||
"VT_aca900a5b9566af61c91aea6525190e6 - version_4: 0.8380126357078552\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"For VT_aca900a5b9566af61c91aea6525190e6; statistics are:\n",
|
||||
"Scores - mean: 0.855s\tstd: 0.009smin: 0.838s\t max: 0.867s\n",
|
||||
"--------------------------------------------\n",
|
||||
"--------------VT_fb6b96a190455106d29f0630f002ac6f------------------\n",
|
||||
"VT_fb6b96a190455106d29f0630f002ac6f - version_6: 0.8635155558586121\n",
|
||||
"VT_fb6b96a190455106d29f0630f002ac6f - version_1: 0.8261691927909851\n",
|
||||
"VT_fb6b96a190455106d29f0630f002ac6f - version_3: 0.8444902896881104\n",
|
||||
"VT_fb6b96a190455106d29f0630f002ac6f - version_0: 0.865719735622406\n",
|
||||
"VT_fb6b96a190455106d29f0630f002ac6f - version_8: 0.8533784747123718\n",
|
||||
"VT_fb6b96a190455106d29f0630f002ac6f - version_5: 0.8555656671524048\n",
|
||||
"VT_fb6b96a190455106d29f0630f002ac6f - version_7: 0.837948739528656\n",
|
||||
"VT_fb6b96a190455106d29f0630f002ac6f - version_2: 0.8545827865600586\n",
|
||||
"VT_fb6b96a190455106d29f0630f002ac6f - version_9: 0.8541560769081116\n",
|
||||
"VT_fb6b96a190455106d29f0630f002ac6f - version_4: 0.85297691822052\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"For VT_fb6b96a190455106d29f0630f002ac6f; statistics are:\n",
|
||||
"Scores - mean: 0.851s\tstd: 0.011smin: 0.826s\t max: 0.866s\n",
|
||||
"--------------------------------------------\n",
|
||||
"--------------VT_378971720b930050ad7662bb96699e20------------------\n",
|
||||
"VT_378971720b930050ad7662bb96699e20 - version_6: 0.8388294577598572\n",
|
||||
"VT_378971720b930050ad7662bb96699e20 - version_1: 0.8333806395530701\n",
|
||||
"VT_378971720b930050ad7662bb96699e20 - version_3: 0.847841203212738\n",
|
||||
"VT_378971720b930050ad7662bb96699e20 - version_0: 0.8287097811698914\n",
|
||||
"VT_378971720b930050ad7662bb96699e20 - version_8: 0.8436978459358215\n",
|
||||
"VT_378971720b930050ad7662bb96699e20 - version_5: 0.8392724990844727\n",
|
||||
"VT_378971720b930050ad7662bb96699e20 - version_7: 0.8410612344741821\n",
|
||||
"VT_378971720b930050ad7662bb96699e20 - version_2: 0.8407015204429626\n",
|
||||
"VT_378971720b930050ad7662bb96699e20 - version_9: 0.8334627151489258\n",
|
||||
"VT_378971720b930050ad7662bb96699e20 - version_4: 0.8400266766548157\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"For VT_378971720b930050ad7662bb96699e20; statistics are:\n",
|
||||
"Scores - mean: 0.839s\tstd: 0.005smin: 0.829s\t max: 0.848s\n",
|
||||
"--------------------------------------------\n",
|
||||
"--------------VT_d55f1492ff29a3cd1026013948ce7fa7------------------\n",
|
||||
"VT_d55f1492ff29a3cd1026013948ce7fa7 - version_6: 0.8385945558547974\n",
|
||||
"VT_d55f1492ff29a3cd1026013948ce7fa7 - version_1: 0.8324360251426697\n",
|
||||
"VT_d55f1492ff29a3cd1026013948ce7fa7 - version_3: 0.8386826515197754\n",
|
||||
"VT_d55f1492ff29a3cd1026013948ce7fa7 - version_0: 0.8366813063621521\n",
|
||||
"VT_d55f1492ff29a3cd1026013948ce7fa7 - version_8: 0.8460721969604492\n",
|
||||
"VT_d55f1492ff29a3cd1026013948ce7fa7 - version_5: 0.8374781608581543\n",
|
||||
"VT_d55f1492ff29a3cd1026013948ce7fa7 - version_7: 0.8320286273956299\n",
|
||||
"VT_d55f1492ff29a3cd1026013948ce7fa7 - version_2: 0.8370164632797241\n",
|
||||
"VT_d55f1492ff29a3cd1026013948ce7fa7 - version_9: 0.8495808839797974\n",
|
||||
"VT_d55f1492ff29a3cd1026013948ce7fa7 - version_4: 0.8332125544548035\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"For VT_d55f1492ff29a3cd1026013948ce7fa7; statistics are:\n",
|
||||
"Scores - mean: 0.838s\tstd: 0.005smin: 0.832s\t max: 0.850s\n",
|
||||
"--------------------------------------------\n",
|
||||
"--------------VT_15cbb349b2b50dbb97beec16af2bedab------------------\n",
|
||||
"VT_15cbb349b2b50dbb97beec16af2bedab - version_6: 0.8407894372940063\n",
|
||||
"VT_15cbb349b2b50dbb97beec16af2bedab - version_1: 0.836580216884613\n",
|
||||
"VT_15cbb349b2b50dbb97beec16af2bedab - version_3: 0.8312996029853821\n",
|
||||
"VT_15cbb349b2b50dbb97beec16af2bedab - version_0: 0.8336991667747498\n",
|
||||
"VT_15cbb349b2b50dbb97beec16af2bedab - version_8: 0.8231534957885742\n",
|
||||
"VT_15cbb349b2b50dbb97beec16af2bedab - version_5: 0.8243923187255859\n",
|
||||
"VT_15cbb349b2b50dbb97beec16af2bedab - version_7: 0.8342592120170593\n",
|
||||
"VT_15cbb349b2b50dbb97beec16af2bedab - version_2: 0.8349334001541138\n",
|
||||
"VT_15cbb349b2b50dbb97beec16af2bedab - version_9: 0.8382810950279236\n",
|
||||
"VT_15cbb349b2b50dbb97beec16af2bedab - version_4: 0.8381868600845337\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"For VT_15cbb349b2b50dbb97beec16af2bedab; statistics are:\n",
|
||||
"Scores - mean: 0.834s\tstd: 0.006smin: 0.823s\t max: 0.841s\n",
|
||||
"--------------------------------------------\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"for model_configuration in [x for x in (out_path / model_name).iterdir() if x.is_dir()]:\n",
|
||||
" # Print metrics\n",
|
||||
" print_metrics(model_configuration)"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"pycharm": {
|
||||
"name": "#%% Mass - Load Model and read Metrics\n"
|
||||
}
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"--------------VT_fdf2a86085b508c1325b181c830a4cf7------------------\n",
|
||||
"--------------VT_fdf2a86085b508c1325b181c830a4cf7------------------\n",
|
||||
"VT_fdf2a86085b508c1325b181c830a4cf7 - version_6: 0.854997456073761\n",
|
||||
"VT_fdf2a86085b508c1325b181c830a4cf7 - version_1: 0.8609604835510254\n",
|
||||
"VT_fdf2a86085b508c1325b181c830a4cf7 - version_3: 0.8558254837989807\n",
|
||||
"VT_fdf2a86085b508c1325b181c830a4cf7 - version_0: 0.8728921413421631\n",
|
||||
"VT_fdf2a86085b508c1325b181c830a4cf7 - version_8: 0.8631933927536011\n",
|
||||
"VT_fdf2a86085b508c1325b181c830a4cf7 - version_5: 0.8612215518951416\n",
|
||||
"VT_fdf2a86085b508c1325b181c830a4cf7 - version_7: 0.8661960959434509\n",
|
||||
"VT_fdf2a86085b508c1325b181c830a4cf7 - version_2: 0.8636621832847595\n",
|
||||
"VT_fdf2a86085b508c1325b181c830a4cf7 - version_9: 0.8614727258682251\n",
|
||||
"VT_fdf2a86085b508c1325b181c830a4cf7 - version_4: 0.8657329082489014\n",
|
||||
"--------------------------------------------\n",
|
||||
"--------------------------------------------\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# fingerprint = '012aff7c1c667073aedafcbebfa35ec7'\n",
|
||||
"fingerprint = 'fdf2a86085b508c1325b181c830a4cf7'\n",
|
||||
"exp_name = f'{\"\".join([x for x in model_name if x.isupper()])}_{fingerprint}'\n",
|
||||
"\n",
|
||||
"# Print metrics\n",
|
||||
"print_metrics(out_path/model_name/exp_name)\n",
|
||||
"\n"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"pycharm": {
|
||||
"name": "#%% Single - Load Model and read Metrics\n"
|
||||
}
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 39,
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" filenames prediction prediction_named\n",
|
||||
"0 test_00001 1 chimpanze\n",
|
||||
"1 test_00002 0 background\n",
|
||||
"2 test_00003 0 background\n",
|
||||
"3 test_00004 1 chimpanze\n",
|
||||
"4 test_00005 4 redcap\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"predictions_file = out_path/model_name/'VT_15cbb349b2b50dbb97beec16af2bedab'/'version_9'/'predictions.csv'\n",
|
||||
"df_predictions = pd.read_csv(predictions_file)\n",
|
||||
"print(df_predictions.head())\n",
|
||||
"df_predictions = df_predictions[['filenames', 'prediction_named']]\n",
|
||||
"df_predictions.columns = ['filename', 'prediction']\n",
|
||||
"df_predictions['filename'] = df_predictions['filename'] + '.wav'\n",
|
||||
"predictions_file_new = predictions_file.parent / 'prediction_final.csv'\n",
|
||||
"df_predictions.to_csv(index=False, path_or_buf=predictions_file_new)\n",
|
||||
"\n",
|
||||
"\n"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"pycharm": {
|
||||
"name": "#%% Combine Predictions#\n"
|
||||
}
|
||||
}
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 2
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython2",
|
||||
"version": "2.7.6"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
@@ -1,104 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 47,
|
||||
"metadata": {
|
||||
"collapsed": true,
|
||||
"pycharm": {
|
||||
"name": "#%% IMPORTS\n"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from pathlib import Path\n",
|
||||
"from natsort import natsorted\n",
|
||||
"from pytorch_lightning.core.saving import *\n",
|
||||
"from ml_lib.utils.model_io import SavedLightningModels\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 48,
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from ml_lib.utils.tools import locate_and_import_class\n",
|
||||
"from models.transformer_model import VisualTransformer\n",
|
||||
"_ROOT = Path('..')\n",
|
||||
"out_path = 'output'\n",
|
||||
"model_class = VisualTransformer\n",
|
||||
"model_name = model_class.name()\n",
|
||||
"\n",
|
||||
"exp_name = 'VT_01123c93daaffa92d2ed341bda32426d'\n",
|
||||
"version = 'version_2'"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"pycharm": {
|
||||
"name": "#%%M Path resolving and variables\n"
|
||||
}
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 50,
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "ValueError",
|
||||
"evalue": "When you set `reduce` as 'macro', you have to provide the number of classes.",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
|
||||
"\u001B[1;31mValueError\u001B[0m Traceback (most recent call last)",
|
||||
"\u001B[1;32m<ipython-input-50-0216292a172f>\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[0;32m 6\u001B[0m \u001B[0madditional_kwargs\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mdict\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mvariable_length\u001B[0m \u001B[1;33m=\u001B[0m \u001B[1;32mFalse\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mc_classes\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;36m5\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 7\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m----> 8\u001B[1;33m \u001B[0mmodel\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mmodel_class\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mload_from_checkpoint\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mcheckpoint\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mhparams_file\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mstr\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mhparams_yaml\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m**\u001B[0m\u001B[0madditional_kwargs\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 9\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
|
||||
"\u001B[1;32mc:\\users\\steff\\envs\\compare_21\\lib\\site-packages\\pytorch_lightning\\core\\saving.py\u001B[0m in \u001B[0;36mload_from_checkpoint\u001B[1;34m(cls, checkpoint_path, map_location, hparams_file, strict, **kwargs)\u001B[0m\n\u001B[0;32m 154\u001B[0m \u001B[0mcheckpoint\u001B[0m\u001B[1;33m[\u001B[0m\u001B[0mcls\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mCHECKPOINT_HYPER_PARAMS_KEY\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mupdate\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 155\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 156\u001B[1;33m \u001B[0mmodel\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mcls\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_load_model_state\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mcheckpoint\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mstrict\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mstrict\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 157\u001B[0m \u001B[1;32mreturn\u001B[0m \u001B[0mmodel\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 158\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
|
||||
"\u001B[1;32mc:\\users\\steff\\envs\\compare_21\\lib\\site-packages\\pytorch_lightning\\core\\saving.py\u001B[0m in \u001B[0;36m_load_model_state\u001B[1;34m(cls, checkpoint, strict, **cls_kwargs_new)\u001B[0m\n\u001B[0;32m 196\u001B[0m \u001B[0m_cls_kwargs\u001B[0m \u001B[1;33m=\u001B[0m \u001B[1;33m{\u001B[0m\u001B[0mk\u001B[0m\u001B[1;33m:\u001B[0m \u001B[0mv\u001B[0m \u001B[1;32mfor\u001B[0m \u001B[0mk\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mv\u001B[0m \u001B[1;32min\u001B[0m \u001B[0m_cls_kwargs\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mitems\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m \u001B[1;32mif\u001B[0m \u001B[0mk\u001B[0m \u001B[1;32min\u001B[0m \u001B[0mcls_init_args_name\u001B[0m\u001B[1;33m}\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 197\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 198\u001B[1;33m \u001B[0mmodel\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mcls\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m**\u001B[0m\u001B[0m_cls_kwargs\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 199\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 200\u001B[0m \u001B[1;31m# give model a chance to load something\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
|
||||
"\u001B[1;32m~\\projects\\compare_21\\models\\transformer_model.py\u001B[0m in \u001B[0;36m__init__\u001B[1;34m(self, in_shape, n_classes, weight_init, activation, embedding_size, heads, attn_depth, patch_size, use_residual, variable_length, use_bias, use_norm, dropout, lat_dim, loss, scheduler, mlp_dim, head_dim, lr, weight_decay, sto_weight_avg, lr_scheduler_parameter, opt_reset_interval)\u001B[0m\n\u001B[0;32m 27\u001B[0m \u001B[0ma\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mdict\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mlocals\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 28\u001B[0m \u001B[0mparams\u001B[0m \u001B[1;33m=\u001B[0m \u001B[1;33m{\u001B[0m\u001B[0marg\u001B[0m\u001B[1;33m:\u001B[0m \u001B[0ma\u001B[0m\u001B[1;33m[\u001B[0m\u001B[0marg\u001B[0m\u001B[1;33m]\u001B[0m \u001B[1;32mfor\u001B[0m \u001B[0marg\u001B[0m \u001B[1;32min\u001B[0m \u001B[0minspect\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0msignature\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m__init__\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mparameters\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mkeys\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m \u001B[1;32mif\u001B[0m \u001B[0marg\u001B[0m \u001B[1;33m!=\u001B[0m \u001B[1;34m'self'\u001B[0m\u001B[1;33m}\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m---> 29\u001B[1;33m \u001B[0msuper\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mVisualTransformer\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m__init__\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mparams\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 30\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 31\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0min_shape\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0min_shape\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
|
||||
"\u001B[1;32m~\\projects\\compare_21\\ml_lib\\modules\\util.py\u001B[0m in \u001B[0;36m__init__\u001B[1;34m(self, model_parameters, weight_init)\u001B[0m\n\u001B[0;32m 112\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mparams\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mModelParameters\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mmodel_parameters\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 113\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 114\u001B[1;33m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mmetrics\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mPLMetrics\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mparams\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mn_classes\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mtag\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;34m'PL'\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 115\u001B[0m \u001B[1;32mpass\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 116\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
|
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"\u001B[1;32m~\\projects\\compare_21\\ml_lib\\modules\\util.py\u001B[0m in \u001B[0;36m__init__\u001B[1;34m(self, n_classes, tag)\u001B[0m\n\u001B[0;32m 30\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 31\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0maccuracy_score\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mpl\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mmetrics\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mAccuracy\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mcompute_on_step\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;32mFalse\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m---> 32\u001B[1;33m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mprecision\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mpl\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mmetrics\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mPrecision\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mnum_classes\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mn_classes\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0maverage\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;34m'macro'\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mcompute_on_step\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;32mFalse\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 33\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mrecall\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mpl\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mmetrics\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mRecall\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mnum_classes\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mn_classes\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0maverage\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;34m'macro'\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mcompute_on_step\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;32mFalse\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 34\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mconfusion_matrix\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mpl\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mmetrics\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mConfusionMatrix\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mn_classes\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mnormalize\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;34m'true'\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mcompute_on_step\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;32mFalse\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
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"\u001B[1;32mc:\\users\\steff\\envs\\compare_21\\lib\\site-packages\\pytorch_lightning\\metrics\\classification\\precision_recall.py\u001B[0m in \u001B[0;36m__init__\u001B[1;34m(self, num_classes, threshold, average, multilabel, mdmc_average, ignore_index, top_k, is_multiclass, compute_on_step, dist_sync_on_step, process_group, dist_sync_fn)\u001B[0m\n\u001B[0;32m 139\u001B[0m \u001B[1;32mraise\u001B[0m \u001B[0mValueError\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;34mf\"The `average` has to be one of {allowed_average}, got {average}.\"\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 140\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 141\u001B[1;33m super().__init__(\n\u001B[0m\u001B[0;32m 142\u001B[0m \u001B[0mreduce\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;34m\"macro\"\u001B[0m \u001B[1;32mif\u001B[0m \u001B[0maverage\u001B[0m \u001B[1;32min\u001B[0m \u001B[1;33m[\u001B[0m\u001B[1;34m\"weighted\"\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;34m\"none\"\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;32mNone\u001B[0m\u001B[1;33m]\u001B[0m \u001B[1;32melse\u001B[0m \u001B[0maverage\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 143\u001B[0m \u001B[0mmdmc_reduce\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mmdmc_average\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
|
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"\u001B[1;32mc:\\users\\steff\\envs\\compare_21\\lib\\site-packages\\pytorch_lightning\\metrics\\classification\\stat_scores.py\u001B[0m in \u001B[0;36m__init__\u001B[1;34m(self, threshold, top_k, reduce, num_classes, ignore_index, mdmc_reduce, is_multiclass, compute_on_step, dist_sync_on_step, process_group, dist_sync_fn)\u001B[0m\n\u001B[0;32m 157\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 158\u001B[0m \u001B[1;32mif\u001B[0m \u001B[0mreduce\u001B[0m \u001B[1;33m==\u001B[0m \u001B[1;34m\"macro\"\u001B[0m \u001B[1;32mand\u001B[0m \u001B[1;33m(\u001B[0m\u001B[1;32mnot\u001B[0m \u001B[0mnum_classes\u001B[0m \u001B[1;32mor\u001B[0m \u001B[0mnum_classes\u001B[0m \u001B[1;33m<\u001B[0m \u001B[1;36m1\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 159\u001B[1;33m \u001B[1;32mraise\u001B[0m \u001B[0mValueError\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;34m\"When you set `reduce` as 'macro', you have to provide the number of classes.\"\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 160\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 161\u001B[0m \u001B[1;32mif\u001B[0m \u001B[0mnum_classes\u001B[0m \u001B[1;32mand\u001B[0m \u001B[0mignore_index\u001B[0m \u001B[1;32mis\u001B[0m \u001B[1;32mnot\u001B[0m \u001B[1;32mNone\u001B[0m \u001B[1;32mand\u001B[0m \u001B[1;33m(\u001B[0m\u001B[1;32mnot\u001B[0m \u001B[1;36m0\u001B[0m \u001B[1;33m<=\u001B[0m \u001B[0mignore_index\u001B[0m \u001B[1;33m<\u001B[0m \u001B[0mnum_classes\u001B[0m \u001B[1;32mor\u001B[0m \u001B[0mnum_classes\u001B[0m \u001B[1;33m==\u001B[0m \u001B[1;36m1\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
|
||||
"\u001B[1;31mValueError\u001B[0m: When you set `reduce` as 'macro', you have to provide the number of classes."
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"exp_path = _ROOT / out_path / model_name / exp_name / version\n",
|
||||
"checkpoint = natsorted(exp_path.glob('*.ckpt'))[-1]\n",
|
||||
"hparams_yaml = next(exp_path.glob('*.yaml'))\n",
|
||||
"\n",
|
||||
"hparams_file = load_hparams_from_yaml(hparams_yaml)\n",
|
||||
"additional_kwargs = dict(variable_length = False, c_classes=5)\n",
|
||||
"\n",
|
||||
"model = model_class.load_from_checkpoint(checkpoint, hparams_file=str(hparams_yaml), **additional_kwargs)\n"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"pycharm": {
|
||||
"name": "#%%\n"
|
||||
}
|
||||
}
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 2
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython2",
|
||||
"version": "2.7.6"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
Reference in New Issue
Block a user