s/messy/partial_edge_blocks/g

Author: crutcher

tests/layers/test_drop.py

@@ -79,7 +79,7 @@ def test_drop_filter(self):
         result = drop.drop_block_2d_drop_filter_(
             selection=selection,
             kernel=(2, 3),
-            messy=False
+            partial_edge_blocks=False
         ).squeeze()
         print(result)
         assert result.device == torch.device(torch_device)
@@ -92,7 +92,7 @@ def test_drop_filter(self):
                 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
             ]
 
-    def test_drop_filter_messy(self):
+    def test_drop_filter_partial_edge_blocks(self):
         selection = torch.tensor(
             [
                 [0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0],
@@ -108,7 +108,7 @@ def test_drop_filter_messy(self):
         result = drop.drop_block_2d_drop_filter_(
             selection=selection,
             kernel=(2, 3),
-            messy=True
+            partial_edge_blocks=True
         ).squeeze()
         print(result)
         assert result.device == torch.device(torch_device)

timm/layers/drop.py

@@ -64,7 +64,7 @@ def drop_block_2d_drop_filter_(
         *,
         selection,
         kernel: Tuple[int, int],
-        messy: bool
+        partial_edge_blocks: bool
 ):
     """Convert drop block gamma noise to a drop filter.
 
@@ -75,13 +75,13 @@ def drop_block_2d_drop_filter_(
           `1.0` at the midpoints of selected blocks to drop,
           `0.0` everywhere else. Expected to be gamma noise.
         kernel: the shape of the 2d kernel.
-        messy: permit partial blocks at the edges, faster.
+        partial_edge_blocks: permit partial blocks at the edges, faster.
 
     Returns:
         A drop filter, `1.0` at points to drop, `0.0` at points to keep.
     """
 
-    if not messy:
+    if not partial_edge_blocks:
         selection = selection * conv2d_kernel_midpoint_mask(
             shape=selection.shape[-2:],
             kernel=kernel,
@@ -111,7 +111,7 @@ def drop_block_2d(
         with_noise: bool = False,
         inplace: bool = False,
         batchwise: bool = False,
-        messy: bool = False,
+        partial_edge_blocks: bool = False,
 ):
     """DropBlock. See https://arxiv.org/pdf/1810.12890.pdf
 
@@ -125,7 +125,7 @@ def drop_block_2d(
         with_noise: should normal noise be added to the dropped region?
         inplace: if the drop should be applied in-place on the input tensor.
         batchwise: should the entire batch use the same drop mask?
-        messy: partial-blocks at the edges, faster.
+        partial_edge_blocks: partial-blocks at the edges, faster.
 
     Returns:
         If inplace, the modified `x`; otherwise, the dropped copy of `x`, on the same device.
@@ -156,7 +156,7 @@ def drop_block_2d(
     drop_filter = drop_block_2d_drop_filter_(
         selection=selection,
         kernel=kernel,
-        messy=messy,
+        partial_edge_blocks=partial_edge_blocks,
     )
     keep_filter = 1.0 - drop_filter
 
@@ -210,7 +210,7 @@ def drop_block_fast_2d(
         with_noise=with_noise,
         inplace=inplace,
         batchwise=True,
-        messy=True,
+        partial_edge_blocks=True,
     )
 
 
@@ -224,15 +224,15 @@ class DropBlock2d(nn.Module):
         with_noise: should normal noise be added to the dropped region?
         inplace: if the drop should be applied in-place on the input tensor.
         batchwise: should the entire batch use the same drop mask?
-        messy: partial-blocks at the edges, faster.
+        partial_edge_blocks: partial-blocks at the edges, faster.
     """
     drop_prob: float
     block_size: int
     gamma_scale: float
     with_noise: bool
     inplace: bool
     batchwise: bool
-    messy: bool
+    partial_edge_blocks: bool
 
     def __init__(
             self,
@@ -242,7 +242,7 @@ def __init__(
             with_noise: bool = False,
             inplace: bool = False,
             batchwise: bool = False,
-            messy: bool = True,
+            partial_edge_blocks: bool = True,
     ):
         super(DropBlock2d, self).__init__()
         self.drop_prob = drop_prob
@@ -251,7 +251,7 @@ def __init__(
         self.with_noise = with_noise
         self.inplace = inplace
         self.batchwise = batchwise
-        self.messy = messy
+        self.partial_edge_blocks = partial_edge_blocks
 
     def forward(self, x):
         if not self.training or not self.drop_prob:
@@ -265,7 +265,7 @@ def forward(self, x):
             with_noise=self.with_noise,
             inplace=self.inplace,
             batchwise=self.batchwise,
-            messy=self.messy)
+            partial_edge_blocks=self.partial_edge_blocks)
 
 
 def drop_path(x, drop_prob: float = 0., training: bool = False, scale_by_keep: bool = True):

timm/models/resnet.py

@@ -328,7 +328,7 @@ def make_blocks(
         drop_block_rate: float = 0.,
         drop_path_rate: float = 0.,
         drop_block_batchwise: bool = False,
-        drop_block_messy: bool = True,
+        drop_block_partial_edge_blocks: bool = True,
         **kwargs,
 ) -> Tuple[List[Tuple[str, nn.Module]], List[Dict[str, Any]]]:
     """Create ResNet stages with specified block configurations.
@@ -344,7 +344,7 @@ def make_blocks(
         avg_down: Use average pooling for downsample.
         drop_block_rate: DropBlock drop rate.
         drop_block_batchwise: Batchwise block dropping, faster.
-        drop_block_messy: dropping produces partial blocks on the edge, faster.
+        drop_block_partial_edge_blocks: dropping produces partial blocks on the edge, faster.
         drop_path_rate: Drop path rate for stochastic depth.
         **kwargs: Additional arguments passed to block constructors.
 
@@ -364,7 +364,7 @@ def make_blocks(
             drop_blocks(
                 drop_prob=drop_block_rate,
                 batchwise=drop_block_batchwise,
-                messy=drop_block_messy,
+                partial_edge_blocks=drop_block_partial_edge_blocks,
             ))):
         stage_name = f'layer{stage_idx + 1}'  # never liked this name, but weight compat requires it
         stride = 1 if stage_idx == 0 else 2
@@ -468,7 +468,7 @@ def __init__(
             drop_path_rate: float = 0.,
             drop_block_rate: float = 0.,
             drop_block_batchwise: bool = True,
-            drop_block_messy: bool = True,
+            drop_block_partial_edge_blocks: bool = True,
             zero_init_last: bool = True,
             block_args: Optional[Dict[str, Any]] = None,
     ):
@@ -500,7 +500,7 @@ def __init__(
             drop_path_rate (float): Stochastic depth drop-path rate (default 0.)
             drop_block_rate (float): Drop block rate (default 0.)
             drop_block_batchwise (bool): Sample blocks batchwise, faster.
-            drop_block_messy (bool): Partial block dropping at the edges, faster.
+            drop_block_partial_edge_blocks (bool): Partial block dropping at the edges, faster.
             zero_init_last (bool): zero-init the last weight in residual path (usually last BN affine weight)
             block_args (dict): Extra kwargs to pass through to block module
         """
@@ -572,7 +572,7 @@ def __init__(
             aa_layer=aa_layer,
             drop_block_rate=drop_block_rate,
             drop_block_batchwise=drop_block_batchwise,
-            drop_block_messy=drop_block_messy,
+            drop_block_partial_edge_blocks=drop_block_partial_edge_blocks,
             drop_path_rate=drop_path_rate,
             **block_args,
         )
@@ -1470,7 +1470,7 @@ def resnet10t_dropblock_correct(pretrained: bool = False, **kwargs) -> ResNet:
         avg_down=True,
         drop_block_rate=0.05,
         drop_block_batchwise=True,
-        drop_block_messy=True,
+        drop_block_partial_edge_blocks=True,
     )
     return _create_resnet('resnet10t', pretrained, **dict(model_args, **kwargs))
 
@@ -1486,7 +1486,7 @@ def resnet10t_dropblock_fast(pretrained: bool = False, **kwargs) -> ResNet:
         avg_down=True,
         drop_block_rate=0.05,
         drop_block_batchwise=False,
-        drop_block_messy=False,
+        drop_block_partial_edge_blocks=False,
     )
     return _create_resnet('resnet10t', pretrained, **dict(model_args, **kwargs))