Remove autoanchor and class checks on resumed training (#889)

* Class frequency not calculated on resuming training Calculation of class frequency is not needed when resuming training. Anchors can still be recalculated whether resuming or not. * Check rank for autoanchor * Update train.py no autoanchor checks on resume Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

Remove autoanchor and class checks on resumed training (#889)
6f3db5e6 · Naman Gupta · GitHub · c5969f79 · 6f3db5e6
--- a/train.py
+++ b/train.py
@@ -185,18 +185,18 @@ def train(hyp, opt, device, tb_writer=None):
    model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device)  # attach class weights
    model.names = names
-    # Class frequency
+    # Classes and Anchors
-    if rank in [-1, 0]:
+    if rank in [-1, 0] and not opt.resume:
        labels = np.concatenate(dataset.labels, 0)
        c = torch.tensor(labels[:, 0])  # classes
-        # cf = torch.bincount(c.long(), minlength=nc) + 1.
+        # cf = torch.bincount(c.long(), minlength=nc) + 1.  # frequency
        # model._initialize_biases(cf.to(device))
        plot_labels(labels, save_dir=log_dir)
        if tb_writer:
            # tb_writer.add_hparams(hyp, {})  # causes duplicate https://github.com/ultralytics/yolov5/pull/384
            tb_writer.add_histogram('classes', c, 0)
-        # Check anchors
+        # Anchors
        if not opt.noautoanchor:
            check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz)