pretrained model loading bug fix (#450)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

train.py

@@ -1,13 +1,12 @@
 import argparse
 
-import torch
 import torch.distributed as dist
 import torch.nn.functional as F
 import torch.optim as optim
 import torch.optim.lr_scheduler as lr_scheduler
 import torch.utils.data
-from torch.utils.tensorboard import SummaryWriter
 from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.utils.tensorboard import SummaryWriter
 
 import test  # import test.py to get mAP after each epoch
 from models.yolo import Model
@@ -70,7 +69,7 @@ def train(hyp, tb_writer, opt, device):
     # Since I see lots of print here, the logging configuration is skipped here. We may see repeated outputs.
 
     # Configure
-    init_seeds(2+local_rank)
+    init_seeds(2 + local_rank)
     with open(opt.data) as f:
         data_dict = yaml.load(f, Loader=yaml.FullLoader)  # model dict
     train_path = data_dict['train']
@@ -131,7 +130,8 @@ def train(hyp, tb_writer, opt, device):
 
         # load model
         try:
-            ckpt['model'] = {k: v for k, v in ckpt['model'].float().state_dict().items() if k in model.state_dict()}
+            ckpt['model'] = {k: v for k, v in ckpt['model'].float().state_dict().items()
+                             if k in model.state_dict() and model.state_dict()[k].shape == v.shape}
             model.load_state_dict(ckpt['model'], strict=False)
         except KeyError as e:
             s = "%s is not compatible with %s. This may be due to model differences or %s may be out of date. " \
@@ -187,7 +187,8 @@ def train(hyp, tb_writer, opt, device):
 
     # Trainloader
     dataloader, dataset = create_dataloader(train_path, imgsz, batch_size, gs, opt, hyp=hyp, augment=True,
-                                        cache=opt.cache_images, rect=opt.rect, local_rank=local_rank, world_size=opt.world_size)
+                                            cache=opt.cache_images, rect=opt.rect, local_rank=local_rank,
+                                            world_size=opt.world_size)
     mlc = np.concatenate(dataset.labels, 0)[:, 0].max()  # max label class
     nb = len(dataloader)  # number of batches
     assert mlc < nc, 'Label class %g exceeds nc=%g in %s. Possible class labels are 0-%g' % (mlc, nc, opt.data, nc - 1)
@@ -242,7 +243,8 @@ def train(hyp, tb_writer, opt, device):
             if local_rank in [-1, 0]:
                 w = model.class_weights.cpu().numpy() * (1 - maps) ** 2  # class weights
                 image_weights = labels_to_image_weights(dataset.labels, nc=nc, class_weights=w)
-                dataset.indices = random.choices(range(dataset.n), weights=image_weights, k=dataset.n)  # rand weighted idx
+                dataset.indices = random.choices(range(dataset.n), weights=image_weights,
+                                                 k=dataset.n)  # rand weighted idx
             # Broadcast.
             if local_rank != -1:
                 indices = torch.zeros([dataset.n], dtype=torch.int)
@@ -402,7 +404,7 @@ def train(hyp, tb_writer, opt, device):
             plot_results()  # save as results.png
         print('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1, (time.time() - t0) / 3600))
 
-    dist.destroy_process_group() if local_rank not in [-1,0] else None
+    dist.destroy_process_group() if local_rank not in [-1, 0] else None
     torch.cuda.empty_cache()
     return results
 
@@ -431,7 +433,8 @@ if __name__ == '__main__':
     parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')
     parser.add_argument("--sync-bn", action="store_true", help="Use sync-bn, only avaible in DDP mode.")
     # Parameter For DDP.
-    parser.add_argument('--local_rank', type=int, default=-1, help="Extra parameter for DDP implementation. Don't use it manually.")
+    parser.add_argument('--local_rank', type=int, default=-1,
+                        help="Extra parameter for DDP implementation. Don't use it manually.")
     opt = parser.parse_args()
 
     last = get_latest_run() if opt.resume == 'get_last' else opt.resume  # resume from most recent run