Test

arguments.py

@@ -11,6 +11,9 @@ parser.add_argument(
 )
 
 # Data configuration =========================================================
+parser.add_argument(
+    "--worker", type=int, default=4, help="Number of data loader processes"
+)
 parser.add_argument(
     "--train_dataset", type=str, default="ShanghaiA", help="Training dataset"
 )
@@ -28,19 +31,16 @@ parser.add_argument(
     "--save_path", type=str, default="./save/default/",
     help="Directory to save checkpoints in"
 )
+parser.add_argument(
+    "--resume", type=str, default=None, help="Path to checkpoint-ed model pth"
+)
 
 # Model configuration ========================================================
 parser.add_argument(
-    "--load_revnet_from", type=str, default=None,
-    help="Pre-trained reverse perspective model path"
+    "--model", type=str, default="base", help="Model variant: <base|stn>"
 )
 parser.add_argument(
-    "--load_csrnet_from", type=str, default=None,
-    help="Pre-trained CSRNet model path"
-)
-parser.add_argument(
-    "--load_transcrowd_from", type=str, default=None,
-    help="Pre-trained TransCrowd model path"
+    "--pth_tar", type=str, default=None, help="Path to pre-training model pth"
 )
 
 # Optimizer configuration ====================================================

train.py

@@ -103,10 +103,15 @@ def worker(rank: int, args: Namespace):
         setup_process_group(rank, world_size)
 
     # Setup device for one proc
-    if args.use_ddp:
-        device = torch.device(rank if torch.cuda.is_available() else "cpu")
+    if args.use_ddp and torch.cuda.is_available():
+        device = torch.device(rank)
+    elif torch.cuda.is_available():
+        device = torch.device(args.gpus)
+    elif torch.backends.mps.is_available():
+        device = torch.device("mps")
     else:
-        device = torch.device(args.gpus if torch.cuda.is_available() else "cpu")
+        print("[!!!] Using CPU for inference. This will be slow...")
+        device = torch.device("cpu")
     torch.set_default_device(device)
 
     # Prepare training data
@@ -130,6 +135,11 @@ def worker(rank: int, args: Namespace):
             find_unused_parameters=True,
             gradient_as_bucket_view=True    # XXX: vital, otherwise OOM
         )
+    else:
+        model = nn.DataParallel(
+            model,
+            device_ids=args.gpus
+        )
 
     # criterion, optimizer, scheduler
     criterion = nn.L1Loss(size_average=False).to(device)
@@ -148,13 +158,13 @@ def worker(rank: int, args: Namespace):
         if not os.path.exists(args.save_path):
             os.makedirs(args.save_path)
 
-    if args.progress:
-        if os.path.isfile(args.progress):
-            print("=> Loading checkpoint \'{}\'".format(args.progress))
-            checkpoint = torch.load(args.progress)
+    if args.resume:
+        if os.path.isfile(args.resume):
+            print("=> Loading checkpoint \'{}\'".format(args.resume))
+            checkpoint = torch.load(args.resume)
             model.load_state_dict(checkpoint['state_dict'], strict=False)
             args.start_epoch = checkpoint['epoch']
-            args.best_pred = checkpoint['best_prec1']
+            args.best_pred = checkpoint['best_prec1']   # sic.
         else:
             print("=> Checkpoint not found!")
 
@@ -187,7 +197,7 @@ def worker(rank: int, args: Namespace):
             if not args.use_ddp or torch.distributed.get_rank() == 0:
                 save_checkpoint({
                     "epoch": epoch + 1,
-                    "arch": args.progress,
+                    "arch": args.resume,
                     "state_dict": model.state_dict(),
                     "best_prec1": args.best_pred,
                     "optimizer": optimizer.state_dict(),
@@ -298,7 +308,8 @@ if __name__ == "__main__":
         # Use DDP, spawn threads
         torch_mp.spawn(
             worker,
-            args=(combined_params, ), # rank supplied automatically as 1st param
+            args=(combined_params, ), # rank supplied at callee as 1st param
+            # also above *has* to be 1-tuple else runtime expands Namespace.
             nprocs=combined_params.world_size,
         )
     else: