Replace verbose with logging

3 files changed, 14 insertions, 8 deletions

diff --git a/evaluate.py b/evaluate.py
index ace647f..76ee472 100755
--- a/evaluate.py
+++ b/evaluate.py
@@ -31,6 +31,8 @@ from scipy.spatial.distance import cdist
 sys.path.append('lib')
 from rerank import re_ranking
 from kmean import create_kmean_clusters
+import logging
+from logging import debug
 
 parser = argparse.ArgumentParser()
 parser.add_argument("-t", "--train", help="Use test data instead of query", action='store_true')
@@ -53,10 +55,8 @@ parser.add_argument("--data", help="Data folder with features data", default='da
 
 
 args = parser.parse_args()
-
-def verbose(*text):
-    if args.verbose:
-        print(text)
+if args.verbose:
+    logging.basicConfig(level=logging.DEBUG)
 
 def draw_results(test_label, pred_label):
     acc_sc = accuracy_score(test_label, pred_label)
@@ -72,8 +72,8 @@ def draw_results(test_label, pred_label):
 
 def test_model(gallery_data, probe_data, gallery_label, probe_label, gallery_cam, probe_cam, showfiles_train, showfiles_test, args):
 
-    verbose("probe shape:", probe_data.shape)
-    verbose("gallery shape:", gallery_data.shape)
+    debug("probe shape:", probe_data.shape)
+    debug("gallery shape:", gallery_data.shape)
 
     if args.rerank:
         distances = re_ranking(probe_data, gallery_data,
@@ -137,6 +137,7 @@ def test_model(gallery_data, probe_data, gallery_label, probe_label, gallery_cam
     return target_pred
 
 def main():
+    logging.debug("Verbose mode is on")
     mat = scipy.io.loadmat(os.path.join(args.data,'cuhk03_new_protocol_config_labeled.mat'))
     camId = mat['camId']
     filelist = mat['filelist']
@@ -167,9 +168,11 @@ def main():
     test_table = np.arange(1, args.multrank+1)
 
     if (args.normalise):
+        debug("Normalising data")
         train_data = np.divide(train_data,LA.norm(train_data, axis=0))
         test_data = np.divide(test_data, LA.norm(test_data, axis=0))
     if(args.kmean):
+        debug("Using Kmeans")
         train_data, train_label, train_cam = create_kmean_clusters(feature_vectors,
                                                                    labels,
                                                                    gallery_idx,
diff --git a/lib/kmean.py b/lib/kmean.py
index 58309bc..f041b19 100644
--- a/lib/kmean.py
+++ b/lib/kmean.py
@@ -1,3 +1,4 @@
+from logging import debug
 import numpy as np
 from sklearn.cluster import KMeans
 
@@ -39,6 +40,7 @@ def create_kmean_clusters(feature_vectors, labels, gallery_idx, camId):
             cllab.append(km_labels[i][j])
 
     train_data = np.array(cl)
+    debug("Kmean data has shape", train_data.shape)
     train_label = np.array(cllab)
     train_cam = np.array([clustercam[i] for i in range(2)]).reshape(train_label.shape[0])
     return train_data, train_label, train_cam
diff --git a/lib/rerank.py b/lib/rerank.py
index 6b20f53..6fb5b7b 100644
--- a/lib/rerank.py
+++ b/lib/rerank.py
@@ -1,5 +1,6 @@
 from scipy.spatial.distance import cdist
 import numpy as np
+from logging import debug
 
 def re_ranking(probFea,galFea,k1,k2,lambda_value, MemorySave = False, Minibatch = 2000):
 
@@ -7,7 +8,7 @@ def re_ranking(probFea,galFea,k1,k2,lambda_value, MemorySave = False, Minibatch
     all_num = query_num + galFea.shape[0]    
     feat = np.append(probFea,galFea,axis = 0)
     feat = feat.astype(np.float16)
-    print('computing original distance')
+    debug('computing original distance')
     if MemorySave:
         original_dist = np.zeros(shape = [all_num,all_num],dtype = np.float16)
         i = 0
@@ -29,7 +30,7 @@ def re_ranking(probFea,galFea,k1,k2,lambda_value, MemorySave = False, Minibatch
     initial_rank = np.argsort(original_dist).astype(np.int32)
 
     
-    print('starting re_ranking')
+    debug('starting re_ranking')
     for i in range(all_num):
         # k-reciprocal neighbors
         forward_k_neigh_index = initial_rank[i,:k1+1]