Update benchmark script to add precision arg. Fix some downstream (DeiT) compat issues with latest changes. Bump version to 0.4.7

benchmark.py

@@ -19,7 +19,7 @@ from contextlib import suppress
 from functools import partial
 
 from timm.models import create_model, is_model, list_models
-from timm.optim import create_optimizer
+from timm.optim import create_optimizer_v2
 from timm.data import resolve_data_config
 from timm.utils import AverageMeter, setup_default_logging
 
@@ -53,6 +53,10 @@ parser.add_argument('--detail', action='store_true', default=False,
                     help='Provide train fwd/bwd/opt breakdown detail if True. Defaults to False')
 parser.add_argument('--results-file', default='', type=str, metavar='FILENAME',
                     help='Output csv file for validation results (summary)')
+parser.add_argument('--num-warm-iter', default=10, type=int,
+                    metavar='N', help='Number of warmup iterations (default: 10)')
+parser.add_argument('--num-bench-iter', default=40, type=int,
+                    metavar='N', help='Number of benchmark iterations (default: 40)')
 
 # common inference / train args
 parser.add_argument('--model', '-m', metavar='NAME', default='resnet50',
@@ -70,11 +74,9 @@ parser.add_argument('--gp', default=None, type=str, metavar='POOL',
 parser.add_argument('--channels-last', action='store_true', default=False,
                     help='Use channels_last memory layout')
 parser.add_argument('--amp', action='store_true', default=False,
-                    help='Use AMP mixed precision. Defaults to Apex, fallback to native Torch AMP.')
-parser.add_argument('--apex-amp', action='store_true', default=False,
-                    help='Use NVIDIA Apex AMP mixed precision')
-parser.add_argument('--native-amp', action='store_true', default=False,
-                    help='Use Native Torch AMP mixed precision')
+                    help='use PyTorch Native AMP for mixed precision training. Overrides --precision arg.')
+parser.add_argument('--precision', default='float32', type=str,
+                    help='Numeric precision. One of (amp, float32, float16, bfloat16, tf32)')
 parser.add_argument('--torchscript', dest='torchscript', action='store_true',
                     help='convert model torchscript for inference')
 
@@ -117,28 +119,50 @@ def cuda_timestamp(sync=False, device=None):
     return time.perf_counter()
 
 
-def count_params(model):
+def count_params(model: nn.Module):
     return sum([m.numel() for m in model.parameters()])
 
 
+def resolve_precision(precision: str):
+    assert precision in ('amp', 'float16', 'bfloat16', 'float32')
+    use_amp = False
+    model_dtype = torch.float32
+    data_dtype = torch.float32
+    if precision == 'amp':
+        use_amp = True
+    elif precision == 'float16':
+        model_dtype = torch.float16
+        data_dtype = torch.float16
+    elif precision == 'bfloat16':
+        model_dtype = torch.bfloat16
+        data_dtype = torch.bfloat16
+    return use_amp, model_dtype, data_dtype
+
+
 class BenchmarkRunner:
-    def __init__(self, model_name, detail=False, device='cuda', torchscript=False, **kwargs):
+    def __init__(
+            self, model_name, detail=False, device='cuda', torchscript=False, precision='float32',
+            num_warm_iter=10, num_bench_iter=50, **kwargs):
         self.model_name = model_name
         self.detail = detail
         self.device = device
+        self.use_amp, self.model_dtype, self.data_dtype = resolve_precision(precision)
+        self.channels_last = kwargs.pop('channels_last', False)
+        self.amp_autocast = torch.cuda.amp.autocast if self.use_amp else suppress
+
         self.model = create_model(
             model_name,
             num_classes=kwargs.pop('num_classes', None),
             in_chans=3,
             global_pool=kwargs.pop('gp', 'fast'),
-            scriptable=torchscript).to(device=self.device)
+            scriptable=torchscript)
+        self.model.to(
+            device=self.device,
+            dtype=self.model_dtype,
+            memory_format=torch.channels_last if self.channels_last else None)
         self.num_classes = self.model.num_classes
         self.param_count = count_params(self.model)
         _logger.info('Model %s created, param count: %d' % (model_name, self.param_count))
-
-        self.channels_last = kwargs.pop('channels_last', False)
-        self.use_amp = kwargs.pop('use_amp', '')
-        self.amp_autocast = torch.cuda.amp.autocast if self.use_amp == 'native' else suppress
         if torchscript:
             self.model = torch.jit.script(self.model)
 
@@ -147,16 +171,17 @@ class BenchmarkRunner:
         self.batch_size = kwargs.pop('batch_size', 256)
 
         self.example_inputs = None
-        self.num_warm_iter = 10
-        self.num_bench_iter = 50
-        self.log_freq = 10
+        self.num_warm_iter = num_warm_iter
+        self.num_bench_iter = num_bench_iter
+        self.log_freq = num_bench_iter // 5
         if 'cuda' in self.device:
             self.time_fn = partial(cuda_timestamp, device=self.device)
         else:
             self.time_fn = timestamp
 
     def _init_input(self):
-        self.example_inputs = torch.randn((self.batch_size,) + self.input_size, device=self.device)
+        self.example_inputs = torch.randn(
+            (self.batch_size,) + self.input_size, device=self.device, dtype=self.data_dtype)
         if self.channels_last:
             self.example_inputs = self.example_inputs.contiguous(memory_format=torch.channels_last)
 
@@ -166,10 +191,6 @@ class InferenceBenchmarkRunner(BenchmarkRunner):
     def __init__(self, model_name, device='cuda', torchscript=False, **kwargs):
         super().__init__(model_name=model_name, device=device, torchscript=torchscript, **kwargs)
         self.model.eval()
-        if self.use_amp == 'apex':
-            self.model = amp.initialize(self.model, opt_level='O1')
-        if self.channels_last:
-            self.model = self.model.to(memory_format=torch.channels_last)
 
     def run(self):
         def _step():
@@ -231,16 +252,11 @@ class TrainBenchmarkRunner(BenchmarkRunner):
             self.loss = nn.CrossEntropyLoss().to(self.device)
         self.target_shape = tuple()
 
-        self.optimizer = create_optimizer(
+        self.optimizer = create_optimizer_v2(
             self.model,
             opt_name=kwargs.pop('opt', 'sgd'),
             lr=kwargs.pop('lr', 1e-4))
 
-        if self.use_amp == 'apex':
-            self.model, self.optimizer = amp.initialize(self.model, self.optimizer, opt_level='O1')
-        if self.channels_last:
-            self.model = self.model.to(memory_format=torch.channels_last)
-
     def _gen_target(self, batch_size):
         return torch.empty(
             (batch_size,) + self.target_shape, device=self.device, dtype=torch.long).random_(self.num_classes)
@@ -331,6 +347,7 @@ class TrainBenchmarkRunner(BenchmarkRunner):
                 samples_per_sec=round(num_samples / t_run_elapsed, 2),
                 step_time=round(1000 * total_step / num_samples, 3),
                 batch_size=self.batch_size,
+                img_size=self.input_size[-1],
                 param_count=round(self.param_count / 1e6, 2),
             )
 
@@ -367,23 +384,14 @@ def _try_run(model_name, bench_fn, initial_batch_size, bench_kwargs):
 
 def benchmark(args):
     if args.amp:
-        if has_native_amp:
-            args.native_amp = True
-        elif has_apex:
-            args.apex_amp = True
-        else:
-            _logger.warning("Neither APEX or Native Torch AMP is available.")
-    if args.native_amp:
-        args.use_amp = 'native'
-        _logger.info('Benchmarking in mixed precision with native PyTorch AMP.')
-    elif args.apex_amp:
-        args.use_amp = 'apex'
-        _logger.info('Benchmarking in mixed precision with NVIDIA APEX AMP.')
-    else:
-        args.use_amp = ''
-        _logger.info('Benchmarking in float32. AMP not enabled.')
+        _logger.warning("Overriding precision to 'amp' since --amp flag set.")
+        args.precision = 'amp'
+    _logger.info(f'Benchmarking in {args.precision} precision. '
+                 f'{"NHWC" if args.channels_last else "NCHW"} layout. '
+                 f'torchscript {"enabled" if args.torchscript else "disabled"}')
 
     bench_kwargs = vars(args).copy()
+    bench_kwargs.pop('amp')
     model = bench_kwargs.pop('model')
     batch_size = bench_kwargs.pop('batch_size')
 

timm/models/regnet.py

@@ -89,7 +89,7 @@ default_cfgs = dict(
     regnety_064=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_064-0a48325c.pth'),
     regnety_080=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_080-e7f3eb93.pth'),
     regnety_120=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_120-721ba79a.pth'),
-    regnety_160=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_160-d64013cd.pth'),
+    regnety_160=_cfg(url='https://dl.fbaipublicfiles.com/deit/regnety_160-a5fe301d.pth'),
     regnety_320=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_320-ba464b29.pth'),
 )
 

timm/models/vision_transformer.py

@@ -281,8 +281,9 @@ class VisionTransformer(nn.Module):
 
         # Classifier head(s)
         self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
-        self.head_dist = nn.Linear(self.embed_dim, self.num_classes) \
-            if num_classes > 0 and distilled else nn.Identity()
+        self.head_dist = None
+        if distilled:
+            self.head_dist = nn.Linear(self.embed_dim, self.num_classes) if num_classes > 0 else nn.Identity()
 
         # Weight init
         assert weight_init in ('jax', 'jax_nlhb', 'nlhb', '')
@@ -336,8 +337,8 @@ class VisionTransformer(nn.Module):
     def reset_classifier(self, num_classes, global_pool=''):
         self.num_classes = num_classes
         self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
-        self.head_dist = nn.Linear(self.embed_dim, self.num_classes) \
-            if num_classes > 0 and self.dist_token is not None else nn.Identity()
+        if self.head_dist is not None:
+            self.head_dist = nn.Linear(self.embed_dim, self.num_classes) if num_classes > 0 else nn.Identity()
 
     def forward_features(self, x):
         x = self.patch_embed(x)
@@ -356,8 +357,8 @@ class VisionTransformer(nn.Module):
 
     def forward(self, x):
         x = self.forward_features(x)
-        if isinstance(x, tuple):
-            x, x_dist = self.head(x[0]), self.head_dist(x[1])
+        if self.head_dist is not None:
+            x, x_dist = self.head(x[0]), self.head_dist(x[1])  # x must be a tuple
             if self.training and not torch.jit.is_scripting():
                 # during inference, return the average of both classifier predictions
                 return x, x_dist

timm/models/vision_transformer_hybrid.py

@@ -145,6 +145,12 @@ def vit_base_r50_s16_224_in21k(pretrained=False, **kwargs):
     return model
 
 
+@register_model
+def vit_base_resnet50_224_in21k(pretrained=False, **kwargs):
+    # NOTE this is forwarding to model def above for backwards compatibility
+    return vit_base_r50_s16_224_in21k(pretrained=pretrained, **kwargs)
+
+
 @register_model
 def vit_base_r50_s16_384(pretrained=False, **kwargs):
     """ R50+ViT-B/16 hybrid from original paper (https://arxiv.org/abs/2010.11929).
@@ -157,6 +163,12 @@ def vit_base_r50_s16_384(pretrained=False, **kwargs):
     return model
 
 
+@register_model
+def vit_base_resnet50_384(pretrained=False, **kwargs):
+    # NOTE this is forwarding to model def above for backwards compatibility
+    return vit_base_r50_s16_384(pretrained=pretrained, **kwargs)
+
+
 @register_model
 def vit_tiny_r_s16_p8_224(pretrained=False, **kwargs):
     """ R+ViT-Ti/S16 w/ 8x8 patch hybrid @ 224 x 224.

timm/optim/__init__.py

@@ -10,4 +10,4 @@ from .radam import RAdam
 from .rmsprop_tf import RMSpropTF
 from .sgdp import SGDP
 
-from .optim_factory import create_optimizer, optimizer_kwargs
+from .optim_factory import create_optimizer, create_optimizer_v2, optimizer_kwargs

timm/optim/optim_factory.py

@@ -55,7 +55,21 @@ def optimizer_kwargs(cfg):
     return kwargs
 
 
-def create_optimizer(
+def create_optimizer(args, model, filter_bias_and_bn=True):
+    """ Legacy optimizer factory for backwards compatibility.
+    NOTE: Use create_optimizer_v2 for new code.
+    """
+    opt_args = dict(lr=args.lr, weight_decay=args.weight_decay, momentum=args.momentum)
+    if hasattr(args, 'opt_eps') and args.opt_eps is not None:
+        opt_args['eps'] = args.opt_eps
+    if hasattr(args, 'opt_betas') and args.opt_betas is not None:
+        opt_args['betas'] = args.opt_betas
+    if hasattr(args, 'opt_args') and args.opt_args is not None:
+        opt_args.update(args.opt_args)
+    return create_optimizer_v2(model, opt_name=args.opt, filter_bias_and_bn=filter_bias_and_bn, **opt_args)
+
+
+def create_optimizer_v2(
         model: nn.Module,
         opt_name: str = 'sgd',
         lr: Optional[float] = None,

timm/version.py

@@ -1 +1 @@
-__version__ = '0.4.6'
+__version__ = '0.4.7'

train.py

@@ -33,7 +33,7 @@ from timm.models import create_model, safe_model_name, resume_checkpoint, load_c
     convert_splitbn_model, model_parameters
 from timm.utils import *
 from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy, JsdCrossEntropy
-from timm.optim import create_optimizer, optimizer_kwargs
+from timm.optim import create_optimizer_v2, optimizer_kwargs
 from timm.scheduler import create_scheduler
 from timm.utils import ApexScaler, NativeScaler
 
@@ -389,7 +389,7 @@ def main():
         assert not args.sync_bn, 'Cannot use SyncBatchNorm with torchscripted model'
         model = torch.jit.script(model)
 
-    optimizer = create_optimizer(model, **optimizer_kwargs(cfg=args))
+    optimizer = create_optimizer_v2(model, **optimizer_kwargs(cfg=args))
 
     # setup automatic mixed-precision (AMP) loss scaling and op casting
     amp_autocast = suppress  # do nothing