Merge pull request #5 from dronedeploy/polish

minor polish

Author: staceysv

libs/scoring.py

@@ -26,7 +26,7 @@ def plot_confusion_matrix(y_true, y_pred, classes,
                           normalize=True,
                           title=None,
                           cmap=plt.cm.Blues,
-                          savedir="score_files"):
+                          savedir="predictions"):
     """
     This function prints and plots the confusion matrix.
     Normalization can be applied by setting `normalize=True`.
@@ -79,8 +79,7 @@ def plot_confusion_matrix(y_true, y_pred, classes,
     # save to directory
     if not os.path.isdir(savedir):
         os.mkdir(savedir)
-    savefile = savedir + '/score-' + title
-
+    savefile = title
     plt.savefig(savefile)
     return savefile, cm
 
@@ -119,7 +118,7 @@ def score_masks(labelfile, predictionfile):
 
     return precision, recall, f1, savefile
 
-def score_predictions(dataset, basedir='.'):
+def score_predictions(dataset, basedir='predictions'):
 
     scores = []
 

main.py

@@ -11,7 +11,7 @@
     # dataset = 'dataset-medium' # 9.0 GB download
 
     config = {
-        'name' : 'baseline',
+        'name' : 'baseline-fastai',
         'dataset' : dataset,
     }
 
@@ -28,7 +28,8 @@
     # scores all the test images compared to the ground truth labels then
     # send the scores (f1, precision, recall) and prediction images to wandb
     score, predictions = scoring.score_predictions(dataset)
-    wandb.config.update(score)
+    print(score)
+    wandb.log(score)
 
     for f1, f2 in predictions:
         wandb.save( f1 )

main_keras.py

@@ -8,7 +8,7 @@
 
 if __name__ == '__main__':
     dataset = 'dataset-sample'  #  0.5 GB download
-    # dataset = 'dataset-medium' # 9.0 GB download
+    #dataset = 'dataset-medium' # 9.0 GB download
 
     config = {
         'name' : 'baseline-keras',
@@ -29,4 +29,5 @@
     # scores all the test images compared to the ground truth labels then
     # send the scores (f1, precision, recall) and prediction images to wandb
     score, _ = scoring.score_predictions(dataset, basedir=wandb.run.dir)
-    wandb.config.update(score)
+    print(score)
+    wandb.log(score)

requirements.txt

@@ -4,7 +4,7 @@ keras
 numpy==1.17.1
 opencv_python==3.4.3.18
 sklearn
-tensorflow
+tensorflow-gpu
 torch
 typing==3.6.6
 wandb