updates

train.py

@@ -73,7 +73,7 @@ def train(cfg,
           img_size=416,
           epochs=100,  # 500200 batches at bs 16, 117263 images = 273 epochs
           batch_size=16,
-          accumulate=4):  # effective bs = batch_size * accumulate = 8 * 8 = 64
+          accumulate=4):  # effective bs = batch_size * accumulate = 16 * 4 = 64
     # Initialize
     init_seeds()
     weights = 'weights' + os.sep
@@ -365,34 +365,34 @@ if __name__ == '__main__':
     opt = parser.parse_args()
     print(opt)
 
-    if opt.evolve:
+    if not opt.evolve:  # Train normally
+        results = train(opt.cfg,
+                        opt.data,
+                        img_size=opt.img_size,
+                        epochs=opt.epochs,
+                        batch_size=opt.batch_size,
+                        accumulate=opt.accumulate)
+
+    else:  # Evolve hyperparameters (optional)
         opt.notest = True  # only test final epoch
         opt.nosave = True  # only save final checkpoint
+        if opt.bucket:
+            os.system('gsutil cp gs://%s/evolve.txt .' % opt.bucket)  # download evolve.txt if exists
 
-    # Train
-    results = train(opt.cfg,
-                    opt.data,
-                    img_size=opt.img_size,
-                    epochs=opt.epochs,
-                    batch_size=opt.batch_size,
-                    accumulate=opt.accumulate)
-
-    # Evolve hyperparameters (optional)
-    if opt.evolve:
-        print_mutation(hyp, results)  # Write mutation results
         for _ in range(1000):  # generations to evolve
-            # Get best hyperparameters
-            x = np.loadtxt('evolve.txt', ndmin=2)
-            x = x[fitness(x).argmax()]  # select best fitness hyps
-            for i, k in enumerate(hyp.keys()):
-                hyp[k] = x[i + 5]
+            if os._exists('evolve.txt'):  # if evolve.txt exists: select best hyps and mutate
+                # Get best hyperparameters
+                x = np.loadtxt('evolve.txt', ndmin=2)
+                x = x[fitness(x).argmax()]  # select best fitness hyps
+                for i, k in enumerate(hyp.keys()):
+                    hyp[k] = x[i + 5]
 
-            # Mutate
-            init_seeds(seed=int(time.time()))
-            s = [.15, .15, .15, .15, .15, .15, .15, .15, .15, .00, .05, .20, .20, .20, .20, .20, .20, .20]  # sigmas
-            for i, k in enumerate(hyp.keys()):
-                x = (np.random.randn(1) * s[i] + 1) ** 2.0  # plt.hist(x.ravel(), 300)
-                hyp[k] *= float(x)  # vary by sigmas
+                # Mutate
+                init_seeds(seed=int(time.time()))
+                s = [.15, .15, .15, .15, .15, .15, .15, .15, .15, .00, .05, .20, .20, .20, .20, .20, .20, .20]  # sigmas
+                for i, k in enumerate(hyp.keys()):
+                    x = (np.random.randn(1) * s[i] + 1) ** 2.0  # plt.hist(x.ravel(), 300)
+                    hyp[k] *= float(x)  # vary by sigmas
 
             # Clip to limits
             keys = ['lr0', 'iou_t', 'momentum', 'weight_decay', 'hsv_s', 'hsv_v', 'translate', 'scale']

utils/torch_utils.py

@@ -37,14 +37,12 @@ def fuse_conv_and_bn(conv, bn):
     # https://tehnokv.com/posts/fusing-batchnorm-and-conv/
     with torch.no_grad():
         # init
-        fusedconv = torch.nn.Conv2d(
-            conv.in_channels,
-            conv.out_channels,
-            kernel_size=conv.kernel_size,
-            stride=conv.stride,
-            padding=conv.padding,
-            bias=True
-        )
+        fusedconv = torch.nn.Conv2d(conv.in_channels,
+                                    conv.out_channels,
+                                    kernel_size=conv.kernel_size,
+                                    stride=conv.stride,
+                                    padding=conv.padding,
+                                    bias=True)
 
         # prepare filters
         w_conv = conv.weight.clone().view(conv.out_channels, -1)