updates

train.py

@@ -294,28 +294,20 @@ def print_mutation(hyp, results):
     b = '%11.4g' * len(hyp) % tuple(hyp.values())  # hyperparam values
     c = '%11.3g' * len(results) % results  # results (P, R, mAP, F1, test_loss)
     print('\n%s\n%s\nEvolved fitness: %s\n' % (a, b, c))
+
+    if opt.cloud_evolve:
+        os.system('gsutil cp gs://yolov4/evolve.txt .')  # download evolve.txt
+        with open('evolve.txt', 'a') as f:  # append result to evolve.txt
+            f.write(c + b + '\n')
+        os.system('gsutil cp evolve.txt gs://yolov4')  # upload evolve.txt
+    else:
         with open('evolve.txt', 'a') as f:
             f.write(c + b + '\n')
 
-    cloud_evolve = False
-    if cloud_evolve:
-        # download cloud_evolve.txt
-        cloud_file = 'https://storage.googleapis.com/yolov4/cloud_evolve.txt'
-        local_file = cloud_file.replace('https://', '')
-        name = Path(local_file).name
-        google_utils.download_blob(bucket_name='yolov4', source_blob_name=name, destination_file_name=local_file)
-
-        # add result to local cloud_evolve.txt
-        with open(local_file, 'a') as f:
-            f.write(c + b + '\n')
-
-        # upload cloud_evolve.txt
-        google_utils.upload_blob(bucket_name='yolov4', source_file_name=local_file, destination_blob_name=name)
-
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
-    parser.add_argument('--epochs', type=int, default=100, help='number of epochs')
+    parser.add_argument('--epochs', type=int, default=1, help='number of epochs')
     parser.add_argument('--batch-size', type=int, default=8, help='batch size')
     parser.add_argument('--accumulate', type=int, default=8, help='number of batches to accumulate before optimizing')
     parser.add_argument('--cfg', type=str, default='cfg/yolov3-spp.cfg', help='cfg file path')
@@ -329,10 +321,12 @@ if __name__ == '__main__':
     parser.add_argument('--notest', action='store_true', help='only test final epoch')
     parser.add_argument('--giou', action='store_true', help='use GIoU loss instead of xy, wh loss')
     parser.add_argument('--evolve', action='store_true', help='run hyperparameter evolution')
+    parser.add_argument('--cloud_evolve', action='store_true', help='--evolve from a central source')
     parser.add_argument('--var', default=0, type=int, help='debug variable')
     opt = parser.parse_args()
     print(opt)
 
+    opt.evolve = opt.cloud_evolve or opt.evolve
     if opt.evolve:
         opt.notest = True  # only test final epoch
         opt.nosave = True  # only save final checkpoint
@@ -347,16 +341,17 @@ if __name__ == '__main__':
 
     # Evolve hyperparameters (optional)
     if opt.evolve:
-        best_fitness = results[2]  # use mAP for fitness
-
-        # Write mutation results
-        print_mutation(hyp, results)
-
         gen = 1000  # generations to evolve
-        for _ in range(gen):
+        print_mutation(hyp, results)  # Write mutation results
 
-            # Mutate hyperparameters
+        for _ in range(gen):
-            old_hyp = hyp.copy()
+            # Get best hyperparamters
+            x = np.loadtxt('evolve.txt', ndmin=2)
+            x = x[x[:, 2].argmax()]  # select best mAP for fitness (col 2)
+            for i, k in enumerate(hyp.keys()):
+                hyp[k] = x[i + 5]
+
+            # Mutate
             init_seeds(seed=int(time.time()))
             s = [.2, .2, .2, .2, .2, .2, .2, .2, .2 * 0, .2 * 0, .05 * 0, .2 * 0]  # fractional sigmas
             for i, k in enumerate(hyp.keys()):
@@ -369,25 +364,17 @@ if __name__ == '__main__':
             for k, v in zip(keys, limits):
                 hyp[k] = np.clip(hyp[k], v[0], v[1])
 
-            # Determine mutation fitness
+            # Train mutation
             results = train(opt.cfg,
                             opt.data_cfg,
                             img_size=opt.img_size,
                             epochs=opt.epochs,
                             batch_size=opt.batch_size,
                             accumulate=opt.accumulate)
-            mutation_fitness = results[2]
 
             # Write mutation results
             print_mutation(hyp, results)
 
-            # Update hyperparameters if fitness improved
-            if mutation_fitness > best_fitness:
-                print('Fitness improved!')
-                best_fitness = mutation_fitness
-            else:
-                hyp = old_hyp.copy()  # reset hyp to
-
             # # Plot results
             # import numpy as np
             # import matplotlib.pyplot as plt