Add back old ModelEma and rename new one to ModelEmaV2 to avoid compat breaks in dependant code. Shuffle train script, add a few comments, remove DataParallel support, support experimental torchscript training.

timm/utils/__init__.py

@@ -6,5 +6,5 @@ from .log import setup_default_logging, FormatterNoInfo
 from .metrics import AverageMeter, accuracy
 from .misc import natural_key, add_bool_arg
 from .model import unwrap_model, get_state_dict
-from .model_ema import ModelEma
+from .model_ema import ModelEma, ModelEmaV2
 from .summary import update_summary, get_outdir

timm/utils/model.py

@@ -6,7 +6,10 @@ from .model_ema import ModelEma
 
 
 def unwrap_model(model):
-    return model.module if hasattr(model, 'module') else model
+    if isinstance(model, ModelEma):
+        return unwrap_model(model.ema)
+    else:
+        return model.module if hasattr(model, 'module') else model
 
 
 def get_state_dict(model, unwrap_fn=unwrap_model):

timm/utils/model_ema.py

@@ -2,15 +2,89 @@
 
 Hacked together by / Copyright 2020 Ross Wightman
 """
+import logging
+from collections import OrderedDict
 from copy import deepcopy
 
 import torch
 import torch.nn as nn
 
+_logger = logging.getLogger(__name__)
+
+
+class ModelEma:
+    """ Model Exponential Moving Average (DEPRECATED)
+
+    Keep a moving average of everything in the model state_dict (parameters and buffers).
+    This version is deprecated, it does not work with scripted models. Will be removed eventually.
+
+    This is intended to allow functionality like
+    https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage
+
+    A smoothed version of the weights is necessary for some training schemes to perform well.
+    E.g. Google's hyper-params for training MNASNet, MobileNet-V3, EfficientNet, etc that use
+    RMSprop with a short 2.4-3 epoch decay period and slow LR decay rate of .96-.99 requires EMA
+    smoothing of weights to match results. Pay attention to the decay constant you are using
+    relative to your update count per epoch.
+
+    To keep EMA from using GPU resources, set device='cpu'. This will save a bit of memory but
+    disable validation of the EMA weights. Validation will have to be done manually in a separate
+    process, or after the training stops converging.
+
+    This class is sensitive where it is initialized in the sequence of model init,
+    GPU assignment and distributed training wrappers.
+    """
+    def __init__(self, model, decay=0.9999, device='', resume=''):
+        # make a copy of the model for accumulating moving average of weights
+        self.ema = deepcopy(model)
+        self.ema.eval()
+        self.decay = decay
+        self.device = device  # perform ema on different device from model if set
+        if device:
+            self.ema.to(device=device)
+        self.ema_has_module = hasattr(self.ema, 'module')
+        if resume:
+            self._load_checkpoint(resume)
+        for p in self.ema.parameters():
+            p.requires_grad_(False)
+
+    def _load_checkpoint(self, checkpoint_path):
+        checkpoint = torch.load(checkpoint_path, map_location='cpu')
+        assert isinstance(checkpoint, dict)
+        if 'state_dict_ema' in checkpoint:
+            new_state_dict = OrderedDict()
+            for k, v in checkpoint['state_dict_ema'].items():
+                # ema model may have been wrapped by DataParallel, and need module prefix
+                if self.ema_has_module:
+                    name = 'module.' + k if not k.startswith('module') else k
+                else:
+                    name = k
+                new_state_dict[name] = v
+            self.ema.load_state_dict(new_state_dict)
+            _logger.info("Loaded state_dict_ema")
+        else:
+            _logger.warning("Failed to find state_dict_ema, starting from loaded model weights")
+
+    def update(self, model):
+        # correct a mismatch in state dict keys
+        needs_module = hasattr(model, 'module') and not self.ema_has_module
+        with torch.no_grad():
+            msd = model.state_dict()
+            for k, ema_v in self.ema.state_dict().items():
+                if needs_module:
+                    k = 'module.' + k
+                model_v = msd[k].detach()
+                if self.device:
+                    model_v = model_v.to(device=self.device)
+                ema_v.copy_(ema_v * self.decay + (1. - self.decay) * model_v)
+
+
+class ModelEmaV2(nn.Module):
+    """ Model Exponential Moving Average V2
 
-class ModelEma(nn.Module):
-    """ Model Exponential Moving Average
     Keep a moving average of everything in the model state_dict (parameters and buffers).
+    V2 of this module is simpler, it does not match params/buffers based on name but simply
+    iterates in order. It works with torchscript (JIT of full model).
 
     This is intended to allow functionality like
     https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage
@@ -27,22 +101,20 @@ class ModelEma(nn.Module):
 
     This class is sensitive where it is initialized in the sequence of model init,
     GPU assignment and distributed training wrappers.
-    I've tested with the sequence in my own train.py for torch.DataParallel, apex.DDP, and single-GPU.
     """
     def __init__(self, model, decay=0.9999, device=None):
-        super(ModelEma, self).__init__()
+        super(ModelEmaV2, self).__init__()
         # make a copy of the model for accumulating moving average of weights
         self.module = deepcopy(model)
         self.module.eval()
         self.decay = decay
         self.device = device  # perform ema on different device from model if set
-        if device is not None:
+        if self.device is not None:
             self.module.to(device=device)
 
     def update(self, model):
         with torch.no_grad():
             for ema_v, model_v in zip(self.module.state_dict().values(), model.state_dict().values()):
-                assert ema_v.shape == model_v.shape
-                if self.device:
+                if self.device is not None:
                     model_v = model_v.to(device=self.device)
                 ema_v.copy_(ema_v * self.decay + (1. - self.decay) * model_v)

train.py

@@ -29,7 +29,7 @@ import torchvision.utils
 from torch.nn.parallel import DistributedDataParallel as NativeDDP
 
 from timm.data import Dataset, create_loader, resolve_data_config, Mixup, FastCollateMixup, AugMixDataset
-from timm.models import create_model, resume_checkpoint, convert_splitbn_model
+from timm.models import create_model, resume_checkpoint, load_checkpoint, convert_splitbn_model
 from timm.utils import *
 from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy, JsdCrossEntropy
 from timm.optim import create_optimizer
@@ -230,8 +230,6 @@ parser.add_argument('--recovery-interval', type=int, default=0, metavar='N',
                     help='how many batches to wait before writing recovery checkpoint')
 parser.add_argument('-j', '--workers', type=int, default=4, metavar='N',
                     help='how many training processes to use (default: 1)')
-parser.add_argument('--num-gpu', type=int, default=1,
-                    help='Number of GPUS to use')
 parser.add_argument('--save-images', action='store_true', default=False,
                     help='save images of input bathes every log interval for debugging')
 parser.add_argument('--amp', action='store_true', default=False,
@@ -255,6 +253,8 @@ parser.add_argument('--tta', type=int, default=0, metavar='N',
 parser.add_argument("--local_rank", default=0, type=int)
 parser.add_argument('--use-multi-epochs-loader', action='store_true', default=False,
                     help='use the multi-epochs-loader to save time at the beginning of every epoch')
+parser.add_argument('--torchscript', dest='torchscript', action='store_true',
+                    help='convert model torchscript for inference')
 
 
 def _parse_args():
@@ -282,28 +282,36 @@ def main():
     args.distributed = False
     if 'WORLD_SIZE' in os.environ:
         args.distributed = int(os.environ['WORLD_SIZE']) > 1
-        if args.distributed and args.num_gpu > 1:
-            _logger.warning(
-                'Using more than one GPU per process in distributed mode is not allowed.Setting num_gpu to 1.')
-            args.num_gpu = 1
-
     args.device = 'cuda:0'
     args.world_size = 1
     args.rank = 0  # global rank
     if args.distributed:
-        args.num_gpu = 1
         args.device = 'cuda:%d' % args.local_rank
         torch.cuda.set_device(args.local_rank)
         torch.distributed.init_process_group(backend='nccl', init_method='env://')
         args.world_size = torch.distributed.get_world_size()
         args.rank = torch.distributed.get_rank()
-    assert args.rank >= 0
-
-    if args.distributed:
         _logger.info('Training in distributed mode with multiple processes, 1 GPU per process. Process %d, total %d.'
                      % (args.rank, args.world_size))
     else:
-        _logger.info('Training with a single process on %d GPUs.' % args.num_gpu)
+        _logger.info('Training with a single process on 1 GPUs.')
+    assert args.rank >= 0
+
+    # resolve AMP arguments based on PyTorch / Apex availability
+    use_amp = None
+    if args.amp:
+        # for backwards compat, `--amp` arg tries apex before native amp
+        if has_apex:
+            args.apex_amp = True
+        elif has_native_amp:
+            args.native_amp = True
+    if args.apex_amp and has_apex:
+        use_amp = 'apex'
+    elif args.native_amp and has_native_amp:
+        use_amp = 'native'
+    elif args.apex_amp or args.native_amp:
+        _logger.warning("Neither APEX or native Torch AMP is available, using float32. "
+                        "Install NVIDA apex or upgrade to PyTorch 1.6")
 
     torch.manual_seed(args.seed + args.rank)
 
@@ -327,44 +335,44 @@ def main():
 
     data_config = resolve_data_config(vars(args), model=model, verbose=args.local_rank == 0)
 
+    # setup augmentation batch splits for contrastive loss or split bn
     num_aug_splits = 0
     if args.aug_splits > 0:
         assert args.aug_splits > 1, 'A split of 1 makes no sense'
         num_aug_splits = args.aug_splits
 
+    # enable split bn (separate bn stats per batch-portion)
     if args.split_bn:
         assert num_aug_splits > 1 or args.resplit
         model = convert_splitbn_model(model, max(num_aug_splits, 2))
 
-    use_amp = None
-    if args.amp:
-        # for backwards compat, `--amp` arg tries apex before native amp
-        if has_apex:
-            args.apex_amp = True
-        elif has_native_amp:
-            args.native_amp = True
-    if args.apex_amp and has_apex:
-        use_amp = 'apex'
-    elif args.native_amp and has_native_amp:
-        use_amp = 'native'
-    elif args.apex_amp or args.native_amp:
-        _logger.warning("Neither APEX or native Torch AMP is available, using float32. "
-                        "Install NVIDA apex or upgrade to PyTorch 1.6")
+    # move model to GPU, enable channels last layout if set
+    model.cuda()
+    if args.channels_last:
+        model = model.to(memory_format=torch.channels_last)
 
-    if args.num_gpu > 1:
-        if use_amp == 'apex':
-            _logger.warning(
-                'Apex AMP does not work well with nn.DataParallel, disabling. Use DDP or Torch AMP.')
-            use_amp = None
-        model = nn.DataParallel(model, device_ids=list(range(args.num_gpu))).cuda()
-        assert not args.channels_last, "Channels last not supported with DP, use DDP."
-    else:
-        model.cuda()
-        if args.channels_last:
-            model = model.to(memory_format=torch.channels_last)
+    # setup synchronized BatchNorm for distributed training
+    if args.distributed and args.sync_bn:
+        assert not args.split_bn
+        if has_apex and use_amp != 'native':
+            # Apex SyncBN preferred unless native amp is activated
+            model = convert_syncbn_model(model)
+        else:
+            model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
+        if args.local_rank == 0:
+            _logger.info(
+                'Converted model to use Synchronized BatchNorm. WARNING: You may have issues if using '
+                'zero initialized BN layers (enabled by default for ResNets) while sync-bn enabled.')
+
+    if args.torchscript:
+        assert not use_amp == 'apex', 'Cannot use APEX AMP with torchscripted model'
+        assert not args.sync_bn, 'Cannot use SyncBatchNorm with torchscripted model'
+        # FIXME I ran into a bug w/ AMP + torchscript + Linear layers
+        model = torch.jit.script(model)
 
     optimizer = create_optimizer(args, model)
 
+    # setup automatic mixed-precision (AMP) loss scaling and op casting
     amp_autocast = suppress  # do nothing
     loss_scaler = None
     if use_amp == 'apex':
@@ -390,30 +398,17 @@ def main():
             loss_scaler=None if args.no_resume_opt else loss_scaler,
             log_info=args.local_rank == 0)
 
+    # setup exponential moving average of model weights, SWA could be used here too
     model_ema = None
     if args.model_ema:
         # Important to create EMA model after cuda(), DP wrapper, and AMP but before SyncBN and DDP wrapper
-        model_ema = ModelEma(
-            model,
-            decay=args.model_ema_decay,
-            device='cpu' if args.model_ema_force_cpu else '',
-            resume=args.resume)
+        model_ema = ModelEmaV2(
+            model, decay=args.model_ema_decay, device='cpu' if args.model_ema_force_cpu else None)
+        if args.resume:
+            load_checkpoint(model_ema.module, args.resume, use_ema=True)
 
+    # setup distributed training
     if args.distributed:
-        if args.sync_bn:
-            assert not args.split_bn
-            try:
-                if has_apex and use_amp != 'native':
-                    # Apex SyncBN preferred unless native amp is activated
-                    model = convert_syncbn_model(model)
-                else:
-                    model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
-                if args.local_rank == 0:
-                    _logger.info(
-                        'Converted model to use Synchronized BatchNorm. WARNING: You may have issues if using '
-                        'zero initialized BN layers (enabled by default for ResNets) while sync-bn enabled.')
-            except Exception as e:
-                _logger.error('Failed to enable Synchronized BatchNorm. Install Apex or Torch >= 1.1')
         if has_apex and use_amp != 'native':
             # Apex DDP preferred unless native amp is activated
             if args.local_rank == 0:
@@ -425,6 +420,7 @@ def main():
             model = NativeDDP(model, device_ids=[args.local_rank])  # can use device str in Torch >= 1.1
         # NOTE: EMA model does not need to be wrapped by DDP
 
+    # setup learning rate schedule and starting epoch
     lr_scheduler, num_epochs = create_scheduler(args, optimizer)
     start_epoch = 0
     if args.start_epoch is not None:
@@ -438,12 +434,22 @@ def main():
     if args.local_rank == 0:
         _logger.info('Scheduled epochs: {}'.format(num_epochs))
 
+    # create the train and eval datasets
     train_dir = os.path.join(args.data, 'train')
     if not os.path.exists(train_dir):
         _logger.error('Training folder does not exist at: {}'.format(train_dir))
         exit(1)
     dataset_train = Dataset(train_dir)
 
+    eval_dir = os.path.join(args.data, 'val')
+    if not os.path.isdir(eval_dir):
+        eval_dir = os.path.join(args.data, 'validation')
+        if not os.path.isdir(eval_dir):
+            _logger.error('Validation folder does not exist at: {}'.format(eval_dir))
+            exit(1)
+    dataset_eval = Dataset(eval_dir)
+
+    # setup mixup / cutmix
     collate_fn = None
     mixup_fn = None
     mixup_active = args.mixup > 0 or args.cutmix > 0. or args.cutmix_minmax is not None
@@ -458,9 +464,11 @@ def main():
         else:
             mixup_fn = Mixup(**mixup_args)
 
+    # wrap dataset in AugMix helper
     if num_aug_splits > 1:
         dataset_train = AugMixDataset(dataset_train, num_splits=num_aug_splits)
 
+    # create data loaders w/ augmentation pipeiine
     train_interpolation = args.train_interpolation
     if args.no_aug or not train_interpolation:
         train_interpolation = data_config['interpolation']
@@ -492,14 +500,6 @@ def main():
         use_multi_epochs_loader=args.use_multi_epochs_loader
     )
 
-    eval_dir = os.path.join(args.data, 'val')
-    if not os.path.isdir(eval_dir):
-        eval_dir = os.path.join(args.data, 'validation')
-        if not os.path.isdir(eval_dir):
-            _logger.error('Validation folder does not exist at: {}'.format(eval_dir))
-            exit(1)
-    dataset_eval = Dataset(eval_dir)
-
     loader_eval = create_loader(
         dataset_eval,
         input_size=data_config['input_size'],
@@ -515,6 +515,7 @@ def main():
         pin_memory=args.pin_mem,
     )
 
+    # setup loss function
     if args.jsd:
         assert num_aug_splits > 1  # JSD only valid with aug splits set
         train_loss_fn = JsdCrossEntropy(num_splits=num_aug_splits, smoothing=args.smoothing).cuda()
@@ -527,6 +528,7 @@ def main():
         train_loss_fn = nn.CrossEntropyLoss().cuda()
     validate_loss_fn = nn.CrossEntropyLoss().cuda()
 
+    # setup checkpoint saver and eval metric tracking
     eval_metric = args.eval_metric
     best_metric = None
     best_epoch = None
@@ -638,11 +640,11 @@ def train_epoch(
                 torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip_grad)
             optimizer.step()
 
-        torch.cuda.synchronize()
         if model_ema is not None:
             model_ema.update(model)
-        num_updates += 1
 
+        torch.cuda.synchronize()
+        num_updates += 1
         batch_time_m.update(time.time() - end)
         if last_batch or batch_idx % args.log_interval == 0:
             lrl = [param_group['lr'] for param_group in optimizer.param_groups]