add training curve

Author: w86763777

.gitignore

@@ -13,3 +13,4 @@ logs
 # data
 data
 stats
+images

Readme.md

@@ -83,6 +83,10 @@ For more defails about calculating Inception Score and FID using pytorch can be
     ```
     Though the training procedures of different GANs are almost identical, I still separate different methods into different files for clear reading.
 
+## Learning curve
+![inception_score_curve](https://drive.google.com/uc?export=view&id=12JTJS5--2dDjFyVhHJ-b264Qp3S-v8xS)
+![fid_curve](https://drive.google.com/uc?export=view&id=1P4e_DEyW4wvFubPSu5t_i2gVRoecGqs5)
+
 ## Change Log
 - 2021-04-16
     - Update pytorch to 1.8.1

images/dcgan_cifar10.png

@@ -0,0 +1,46 @@
+import os
+import glob
+
+import matplotlib.pyplot as plt
+import pandas as pd
+
+
+if __name__ == '__main__':
+    IS = []
+    FID = []
+    for path in glob.glob('./logs/*.csv'):
+        df = pd.read_csv(path)
+        _, name, _, tag = os.path.splitext(
+            os.path.basename(path))[0].split('-')
+        if '_CIFAR10_' in name:
+            name = name.replace('_CIFAR10_', '(')
+            name = name + ')'
+        if name.endswith('_CIFAR10'):
+            name = name.replace('_CIFAR10', '')
+        if tag == 'inception_score' or tag == 'Inception_Score':
+            IS.append((name, df.values[:, 1], df.values[:, 2]))
+        elif tag == 'fid_score' or tag == 'FID':
+            FID.append((name, df.values[:, 1], df.values[:, 2]))
+        else:
+            raise ValueError("???")
+    IS = sorted(IS, key=lambda x: x[2][-1], reverse=True)
+    FID = sorted(FID, key=lambda x: x[2][-1])
+
+    for name, x, y in IS:
+        plt.plot(x, y, label=name)
+    plt.legend()
+    plt.xlabel('Step', fontsize=16)
+    plt.ylabel('Inception Score', fontsize=16)
+    # plt.title('Inception Score', fontsize=16)
+    plt.savefig('./IS.png')
+    plt.cla()
+
+    for name, x, y in FID:
+        plt.plot(x, y, label=name)
+    plt.legend()
+    plt.ylim(0, 100)
+    plt.yticks(range(0, 101, 10))
+    plt.ylabel('FID', fontsize=16)
+    plt.xlabel('Step', fontsize=16)
+    # plt.title('FID curve', fontsize=16)
+    plt.savefig('./FID.png')