Fix memory_efficient mode for DenseNets. Add AntiAliasing (Blur) support for DenseNets and create one test model. Add lr cycle/mul params to train args.

timm/models/densenet.py

@@ -14,7 +14,7 @@ from torch.jit.annotations import List
 
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
 from .helpers import load_pretrained
-from .layers import SelectAdaptivePool2d, BatchNormAct2d, create_norm_act
+from .layers import SelectAdaptivePool2d, BatchNormAct2d, create_norm_act, BlurPool2d
 from .registry import register_model
 
 __all__ = ['DenseNet']
@@ -71,9 +71,9 @@ class DenseLayer(nn.Module):
     def call_checkpoint_bottleneck(self, x):
         # type: (List[torch.Tensor]) -> torch.Tensor
         def closure(*xs):
-            return self.bottleneck_fn(*xs)
+            return self.bottleneck_fn(xs)
 
-        return cp.checkpoint(closure, x)
+        return cp.checkpoint(closure, *x)
 
     @torch.jit._overload_method  # noqa: F811
     def forward(self, x):
@@ -132,12 +132,15 @@ class DenseBlock(nn.ModuleDict):
 
 
 class DenseTransition(nn.Sequential):
-    def __init__(self, num_input_features, num_output_features, norm_act_layer=nn.BatchNorm2d):
+    def __init__(self, num_input_features, num_output_features, norm_act_layer=nn.BatchNorm2d, aa_layer=None):
         super(DenseTransition, self).__init__()
         self.add_module('norm', norm_act_layer(num_input_features))
         self.add_module('conv', nn.Conv2d(
             num_input_features, num_output_features, kernel_size=1, stride=1, bias=False))
-        self.add_module('pool', nn.AvgPool2d(kernel_size=2, stride=2))
+        if aa_layer is not None:
+            self.add_module('pool', aa_layer(num_output_features, stride=2))
+        else:
+            self.add_module('pool', nn.AvgPool2d(kernel_size=2, stride=2))
 
 
 class DenseNet(nn.Module):
@@ -301,6 +304,17 @@ def densenet121(pretrained=False, **kwargs):
     return model
 
 
+@register_model
+def densenetblur121d(pretrained=False, **kwargs):
+    r"""Densenet-121 model from
+    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
+    """
+    model = _densenet(
+        'densenet121', growth_rate=32, block_config=(6, 12, 24, 16), pretrained=pretrained, stem_type='deep',
+        aa_layer=BlurPool2d, **kwargs)
+    return model
+
+
 @register_model
 def densenet121d(pretrained=False, **kwargs):
     r"""Densenet-121 model from

timm/scheduler/scheduler_factory.py

@@ -23,12 +23,12 @@ def create_scheduler(args, optimizer):
         lr_scheduler = CosineLRScheduler(
             optimizer,
             t_initial=num_epochs,
-            t_mul=1.0,
+            t_mul=args.lr_cycle_mul,
             lr_min=args.min_lr,
             decay_rate=args.decay_rate,
             warmup_lr_init=args.warmup_lr,
             warmup_t=args.warmup_epochs,
-            cycle_limit=1,
+            cycle_limit=args.lr_cycle_limit,
             t_in_epochs=True,
             noise_range_t=noise_range,
             noise_pct=args.lr_noise_pct,
@@ -40,11 +40,11 @@ def create_scheduler(args, optimizer):
         lr_scheduler = TanhLRScheduler(
             optimizer,
             t_initial=num_epochs,
-            t_mul=1.0,
+            t_mul=args.lr_cycle_mul,
             lr_min=args.min_lr,
             warmup_lr_init=args.warmup_lr,
             warmup_t=args.warmup_epochs,
-            cycle_limit=1,
+            cycle_limit=args.lr_cycle_limit,
             t_in_epochs=True,
             noise_range_t=noise_range,
             noise_pct=args.lr_noise_pct,

train.py

@@ -111,6 +111,10 @@ parser.add_argument('--lr-noise-pct', type=float, default=0.67, metavar='PERCENT
                     help='learning rate noise limit percent (default: 0.67)')
 parser.add_argument('--lr-noise-std', type=float, default=1.0, metavar='STDDEV',
                     help='learning rate noise std-dev (default: 1.0)')
+parser.add_argument('--lr-cycle-mul', type=float, default=1.0, metavar='MULT',
+                    help='learning rate cycle len multiplier (default: 1.0)')
+parser.add_argument('--lr-cycle-limit', type=int, default=1, metavar='N',
+                    help='learning rate cycle limit')
 parser.add_argument('--warmup-lr', type=float, default=0.0001, metavar='LR',
                     help='warmup learning rate (default: 0.0001)')
 parser.add_argument('--min-lr', type=float, default=1e-5, metavar='LR',