rename ch to input_channels to clarity

train.py

@@ -127,14 +127,14 @@ def train(hyp, opt, device, callbacks):  # hyp is path/to/hyp.yaml or hyp dictio
         with torch_distributed_zero_first(LOCAL_RANK):
             weights = attempt_download(weights)  # download if not found locally
         ckpt = torch.load(weights, map_location='cpu')  # load checkpoint to CPU to avoid CUDA memory leak
-        model = Model(cfg or ckpt['model'].yaml, ch=hyp.get('ch', 3), nc=nc, anchors=hyp.get('anchors')).to(device)  # create
+        model = Model(cfg or ckpt['model'].yaml, ch=hyp.get('input_channels', 3), nc=nc, anchors=hyp.get('anchors')).to(device)  # create
         exclude = ['anchor'] if (cfg or hyp.get('anchors')) and not resume else []  # exclude keys
         csd = ckpt['model'].float().state_dict()  # checkpoint state_dict as FP32
         csd = intersect_dicts(csd, model.state_dict(), exclude=exclude)  # intersect
         model.load_state_dict(csd, strict=False)  # load
         LOGGER.info(f'Transferred {len(csd)}/{len(model.state_dict())} items from {weights}')  # report
     else:
-        model = Model(cfg, ch=hyp.get('ch', 3), nc=nc, anchors=hyp.get('anchors')).to(device)  # create
+        model = Model(cfg, ch=hyp.get('input_channels', 3), nc=nc, anchors=hyp.get('anchors')).to(device)  # create
     amp = check_amp(model)  # check AMP
 
     # Freeze