Merge branch 'main' into caojiaolong-main

Author: rwightman

timm/data/loader.py

@@ -77,18 +77,18 @@ class PrefetchLoader:
 
     def __init__(
             self,
-            loader,
-            mean=IMAGENET_DEFAULT_MEAN,
-            std=IMAGENET_DEFAULT_STD,
-            channels=3,
-            device=torch.device('cuda'),
-            img_dtype=torch.float32,
-            fp16=False,
-            re_prob=0.,
-            re_mode='const',
-            re_count=1,
-            re_num_splits=0):
-
+            loader: torch.utils.data.DataLoader,
+            mean: Tuple[float, ...] = IMAGENET_DEFAULT_MEAN,
+            std: Tuple[float, ...] = IMAGENET_DEFAULT_STD,
+            channels: int = 3,
+            device: torch.device = torch.device('cuda'),
+            img_dtype: Optional[torch.dtype] = None,
+            fp16: bool = False,
+            re_prob: float = 0.,
+            re_mode: str = 'const',
+            re_count: int = 1,
+            re_num_splits: int = 0,
+    ):
         mean = adapt_to_chs(mean, channels)
         std = adapt_to_chs(std, channels)
         normalization_shape = (1, channels, 1, 1)
@@ -98,7 +98,7 @@ def __init__(
         if fp16:
             # fp16 arg is deprecated, but will override dtype arg if set for bwd compat
             img_dtype = torch.float16
-        self.img_dtype = img_dtype
+        self.img_dtype = img_dtype or torch.float32
         self.mean = torch.tensor(
             [x * 255 for x in mean], device=device, dtype=img_dtype).view(normalization_shape)
         self.std = torch.tensor(

train.py

@@ -178,6 +178,8 @@
                    help='lower precision AMP dtype (default: float16)')
 group.add_argument('--amp-impl', default='native', type=str,
                    help='AMP impl to use, "native" or "apex" (default: native)')
+group.add_argument('--model-dtype', default=None, type=str,
+                   help='Model dtype override (non-AMP) (default: float32)')
 group.add_argument('--no-ddp-bb', action='store_true', default=False,
                    help='Force broadcast buffers for native DDP to off.')
 group.add_argument('--synchronize-step', action='store_true', default=False,
@@ -436,10 +438,18 @@ def main():
         _logger.info(f'Training with a single process on 1 device ({args.device}).')
     assert args.rank >= 0
 
+    model_dtype = None
+    if args.model_dtype:
+        assert args.model_dtype in ('float32', 'float16', 'bfloat16')
+        model_dtype = getattr(torch, args.model_dtype)
+        if model_dtype == torch.float16:
+            _logger.warning('float16 is not recommended for training, for half precision bfloat16 is recommended.')
+
     # resolve AMP arguments based on PyTorch / Apex availability
     use_amp = None
     amp_dtype = torch.float16
     if args.amp:
+        assert model_dtype is None or model_dtype == torch.float32, 'float32 model dtype must be used with AMP'
         if args.amp_impl == 'apex':
             assert has_apex, 'AMP impl specified as APEX but APEX is not installed.'
             use_amp = 'apex'
@@ -519,7 +529,7 @@ def main():
         model = convert_splitbn_model(model, max(num_aug_splits, 2))
 
     # move model to GPU, enable channels last layout if set
-    model.to(device=device)
+    model.to(device=device, dtype=model_dtype)  # FIXME move model device & dtype into create_model
     if args.channels_last:
         model.to(memory_format=torch.channels_last)
 
@@ -589,7 +599,7 @@ def main():
             _logger.info('Using native Torch AMP. Training in mixed precision.')
     else:
         if utils.is_primary(args):
-            _logger.info('AMP not enabled. Training in float32.')
+            _logger.info(f'AMP not enabled. Training in {model_dtype or torch.float32}.')
 
     # optionally resume from a checkpoint
     resume_epoch = None
@@ -734,6 +744,7 @@ def main():
         distributed=args.distributed,
         collate_fn=collate_fn,
         pin_memory=args.pin_mem,
+        img_dtype=model_dtype,
         device=device,
         use_prefetcher=args.prefetcher,
         use_multi_epochs_loader=args.use_multi_epochs_loader,
@@ -758,6 +769,7 @@ def main():
             distributed=args.distributed,
             crop_pct=data_config['crop_pct'],
             pin_memory=args.pin_mem,
+            img_dtype=model_dtype,
             device=device,
             use_prefetcher=args.prefetcher,
         )
@@ -822,21 +834,21 @@ def main():
         with open(os.path.join(output_dir, 'args.yaml'), 'w') as f:
             f.write(args_text)
 
-    if utils.is_primary(args) and args.log_wandb:
-        if has_wandb:
-            assert not args.wandb_resume_id or args.resume
-            wandb.init(
-                project=args.wandb_project,
-                name=args.experiment,
-                config=args,
-                tags=args.wandb_tags,
-                resume="must" if args.wandb_resume_id else None,
-                id=args.wandb_resume_id if args.wandb_resume_id else None,
-            )
-        else:
-            _logger.warning(
-                "You've requested to log metrics to wandb but package not found. "
-                "Metrics not being logged to wandb, try `pip install wandb`")
+        if args.log_wandb:
+            if has_wandb:
+                assert not args.wandb_resume_id or args.resume
+                wandb.init(
+                    project=args.wandb_project,
+                    name=exp_name,
+                    config=args,
+                    tags=args.wandb_tags,
+                    resume="must" if args.wandb_resume_id else None,
+                    id=args.wandb_resume_id if args.wandb_resume_id else None,
+                )
+            else:
+                _logger.warning(
+                    "You've requested to log metrics to wandb but package not found. "
+                    "Metrics not being logged to wandb, try `pip install wandb`")
 
     # setup learning rate schedule and starting epoch
     updates_per_epoch = (len(loader_train) + args.grad_accum_steps - 1) // args.grad_accum_steps
@@ -886,6 +898,7 @@ def main():
                 output_dir=output_dir,
                 amp_autocast=amp_autocast,
                 loss_scaler=loss_scaler,
+                model_dtype=model_dtype,
                 model_ema=model_ema,
                 mixup_fn=mixup_fn,
                 num_updates_total=num_epochs * updates_per_epoch,
@@ -904,6 +917,7 @@ def main():
                     args,
                     device=device,
                     amp_autocast=amp_autocast,
+                    model_dtype=model_dtype,
                 )
 
                 if model_ema is not None and not args.model_ema_force_cpu:
@@ -986,6 +1000,7 @@ def train_one_epoch(
         output_dir=None,
         amp_autocast=suppress,
         loss_scaler=None,
+        model_dtype=None,
         model_ema=None,
         mixup_fn=None,
         num_updates_total=None,
@@ -1022,7 +1037,7 @@ def train_one_epoch(
             accum_steps = last_accum_steps
 
         if not args.prefetcher:
-            input, target = input.to(device), target.to(device)
+            input, target = input.to(device=device, dtype=model_dtype), target.to(device=device)
             if mixup_fn is not None:
                 input, target = mixup_fn(input, target)
         if args.channels_last:
@@ -1149,6 +1164,7 @@ def validate(
         args,
         device=torch.device('cuda'),
         amp_autocast=suppress,
+        model_dtype=None,
         log_suffix=''
 ):
     batch_time_m = utils.AverageMeter()
@@ -1164,8 +1180,8 @@ def validate(
         for batch_idx, (input, target) in enumerate(loader):
             last_batch = batch_idx == last_idx
             if not args.prefetcher:
-                input = input.to(device)
-                target = target.to(device)
+                input = input.to(device=device, dtype=model_dtype)
+                target = target.to(device=device)
             if args.channels_last:
                 input = input.contiguous(memory_format=torch.channels_last)
 

validate.py

@@ -123,6 +123,8 @@
                     help='lower precision AMP dtype (default: float16)')
 parser.add_argument('--amp-impl', default='native', type=str,
                     help='AMP impl to use, "native" or "apex" (default: native)')
+parser.add_argument('--model-dtype', default=None, type=str,
+                   help='Model dtype override (non-AMP) (default: float32)')
 parser.add_argument('--tf-preprocessing', action='store_true', default=False,
                     help='Use Tensorflow preprocessing pipeline (require CPU TF installed')
 parser.add_argument('--use-ema', dest='use_ema', action='store_true',
@@ -168,10 +170,16 @@ def validate(args):
 
     device = torch.device(args.device)
 
+    model_dtype = None
+    if args.model_dtype:
+        assert args.model_dtype in ('float32', 'float16', 'bfloat16')
+        model_dtype = getattr(torch, args.model_dtype)
+
     # resolve AMP arguments based on PyTorch / Apex availability
     use_amp = None
     amp_autocast = suppress
     if args.amp:
+        assert model_dtype is None or model_dtype == torch.float32, 'float32 model dtype must be used with AMP'
         if args.amp_impl == 'apex':
             assert has_apex, 'AMP impl specified as APEX but APEX is not installed.'
             assert args.amp_dtype == 'float16'
@@ -184,7 +192,7 @@ def validate(args):
             amp_autocast = partial(torch.autocast, device_type=device.type, dtype=amp_dtype)
             _logger.info('Validating in mixed precision with native PyTorch AMP.')
     else:
-        _logger.info('Validating in float32. AMP not enabled.')
+        _logger.info(f'Validating in {model_dtype or torch.float32}. AMP not enabled.')
 
     if args.fuser:
         set_jit_fuser(args.fuser)
@@ -231,7 +239,7 @@ def validate(args):
     if args.test_pool:
         model, test_time_pool = apply_test_time_pool(model, data_config)
 
-    model = model.to(device)
+    model = model.to(device=device, dtype=model_dtype)  # FIXME move model device & dtype into create_model
     if args.channels_last:
         model = model.to(memory_format=torch.channels_last)
 
@@ -299,6 +307,7 @@ def validate(args):
         crop_border_pixels=args.crop_border_pixels,
         pin_memory=args.pin_mem,
         device=device,
+        img_dtype=model_dtype,
         tf_preprocessing=args.tf_preprocessing,
     )
 
@@ -310,7 +319,7 @@ def validate(args):
     model.eval()
     with torch.no_grad():
         # warmup, reduce variability of first batch time, especially for comparing torchscript vs non
-        input = torch.randn((args.batch_size,) + tuple(data_config['input_size'])).to(device)
+        input = torch.randn((args.batch_size,) + tuple(data_config['input_size'])).to(device=device, dtype=model_dtype)
         if args.channels_last:
             input = input.contiguous(memory_format=torch.channels_last)
         with amp_autocast():
@@ -319,8 +328,8 @@ def validate(args):
         end = time.time()
         for batch_idx, (input, target) in enumerate(loader):
             if args.no_prefetcher:
-                target = target.to(device)
-                input = input.to(device)
+                target = target.to(device=device)
+                input = input.to(device=device, dtype=model_dtype)
             if args.channels_last:
                 input = input.contiguous(memory_format=torch.channels_last)