added utility to plot cmc curve

torchreid/engine/engine.py

@@ -11,10 +11,16 @@ from torch.utils.tensorboard import SummaryWriter
 from torchreid import metrics
 from torchreid.utils import (
     MetricMeter, AverageMeter, re_ranking, open_all_layers, save_checkpoint,
-    open_specified_layers, visualize_ranked_results
+    open_specified_layers, visualize_ranked_results,  tools
 )
 from torchreid.losses import DeepSupervision
 
+import matplotlib.pyplot as plt
+import numpy as np
+
+import matplotlib.pyplot as plt
+import numpy as np
+
 
 class Engine(object):
     r"""A generic base Engine class for both image- and video-reid.
@@ -412,6 +418,9 @@ class Engine(object):
             g_camids,
             use_metric_cuhk03=use_metric_cuhk03
         )
+        
+        print('Plotting CMC ...')
+        tools.plot_cmc(cmc, max_rank=max(ranks), save_path=f"{save_dir}/cmc_curve.png")
 
         print('** Results **')
         print('mAP: {:.1%}'.format(mAP))

torchreid/utils/tools.py

@@ -5,6 +5,7 @@ import json
 import time
 import errno
 import numpy as np
+import matplotlib.pyplot as plt
 import random
 import os.path as osp
 import warnings
@@ -141,3 +142,26 @@ def listdir_nohidden(path, sort=False):
     if sort:
         items.sort()
     return items
+
+def plot_cmc(cmc, max_rank=50, save_path="curve.png"):
+    """Plots the CMC curve and saves it as an image.
+    
+    Args:
+        cmc (numpy.ndarray): CMC values computed from the evaluation.
+        max_rank (int): Maximum rank to display.
+        save_path (str): Path to save the CMC curve image.
+    """
+    ranks = np.arange(1, len(cmc) + 1)
+
+    plt.figure(figsize=(8, 6))
+    plt.plot(ranks[:max_rank], cmc[:max_rank], marker='o', linestyle='-', color='b', label="CMC Curve")
+    plt.xlabel("Rank")
+    plt.ylabel("Matching Rate")
+    plt.title("Cumulative Matching Characteristics (CMC) Curve")
+    plt.legend()
+    plt.grid()
+    
+    # Save the plot
+    plt.savefig(save_path)
+    print(f"CMC curve saved to {save_path}")
+