diff --git a/image_ref/hyperparameter_res_analysis.py b/image_ref/hyperparameter_res_analysis.py new file mode 100644 index 0000000000000000000000000000000000000000..cdabac5f5e441d1037416bc4a1eedfebdc82abb8 --- /dev/null +++ b/image_ref/hyperparameter_res_analysis.py @@ -0,0 +1,6 @@ +import pandas as pd +import numpy as np + +df = pd.read_csv('../df_results_contrastive.csv') + +best_param = df[df['val loss']<0.003] \ No newline at end of file diff --git a/image_ref/main.py b/image_ref/main.py index eaf030dca3abe6018aab659d44f9d22aa9413b1b..bab6e980df95e1f786ad1ce376e5420c9ddf3191 100644 --- a/image_ref/main.py +++ b/image_ref/main.py @@ -2,7 +2,7 @@ import os import wandb as wdb import matplotlib.pyplot as plt import numpy as np - +import PIL from config import load_args_contrastive from dataset_ref import load_data_duo import torch @@ -180,6 +180,7 @@ def run_duo(args): plt.show() plt.savefig(args.base_out+'_training_plot.png') + # load and evaluate best model load_model(model, args.save_path) if args.dataset_test_dir is not None : @@ -190,6 +191,16 @@ def run_duo(args): args.base_out+'_confidence_matrix_val.png') if args.wandb is not None: + if args.dataset_test_dir is not None: + wdb.log({ + 'confidence matrix val' : wdb.Image(args.base_out+'_confidence_matrix_val.png'), + 'confidence matrix test' : wdb.Image(args.base_out+'_confidence_matrix_test.png'), + 'confusion matrix val' : wdb.Image(args.base_out+'_confusion_matrix_val.png'), + 'confusion matrix test' : wdb.Image(args.base_out+'_confusion_matrix_test.png')}) + else : + wdb.log({ + 'confidence matrix val': wdb.Image(args.base_out + '_confidence_matrix_val.png'), + 'confidence matrix test': wdb.Image(args.base_out + '_confidence_matrix_test.png'),}) wdb.finish()