Fix torchscript use for sequencer, add group_matcher, forward_head support, minor formatting

Author: rwightman

timm/models/sequencer.py

@@ -71,18 +71,19 @@ def _init_weights(module: nn.Module, name: str, head_bias: float = 0., flax=Fals
         module.init_weights()
 
 
-def get_stage(index, layers, patch_sizes, embed_dims, hidden_sizes, mlp_ratios, block_layer, rnn_layer, mlp_layer,
-              norm_layer, act_layer, num_layers, bidirectional, union,
-              with_fc, drop=0., drop_path_rate=0., **kwargs):
+def get_stage(
+        index, layers, patch_sizes, embed_dims, hidden_sizes, mlp_ratios, block_layer, rnn_layer, mlp_layer,
+        norm_layer, act_layer, num_layers, bidirectional, union,
+        with_fc, drop=0., drop_path_rate=0., **kwargs):
     assert len(layers) == len(patch_sizes) == len(embed_dims) == len(hidden_sizes) == len(mlp_ratios)
     blocks = []
     for block_idx in range(layers[index]):
         drop_path = drop_path_rate * (block_idx + sum(layers[:index])) / (sum(layers) - 1)
-        blocks.append(block_layer(embed_dims[index], hidden_sizes[index], mlp_ratio=mlp_ratios[index],
-                                  rnn_layer=rnn_layer, mlp_layer=mlp_layer, norm_layer=norm_layer,
-                                  act_layer=act_layer, num_layers=num_layers,
-                                  bidirectional=bidirectional, union=union, with_fc=with_fc,
-                                  drop=drop, drop_path=drop_path))
+        blocks.append(block_layer(
+            embed_dims[index], hidden_sizes[index], mlp_ratio=mlp_ratios[index],
+            rnn_layer=rnn_layer, mlp_layer=mlp_layer, norm_layer=norm_layer, act_layer=act_layer,
+            num_layers=num_layers, bidirectional=bidirectional, union=union, with_fc=with_fc,
+            drop=drop, drop_path=drop_path))
 
     if index < len(embed_dims) - 1:
         blocks.append(Downsample2D(embed_dims[index], embed_dims[index + 1], patch_sizes[index + 1]))
@@ -101,9 +102,10 @@ def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, None]:
 
 class RNN2DBase(nn.Module):
 
-    def __init__(self, input_size: int, hidden_size: int,
-                 num_layers: int = 1, bias: bool = True, bidirectional: bool = True,
-                 union="cat", with_fc=True):
+    def __init__(
+            self, input_size: int, hidden_size: int,
+            num_layers: int = 1, bias: bool = True, bidirectional: bool = True,
+            union="cat", with_fc=True):
         super().__init__()
 
         self.input_size = input_size
@@ -115,6 +117,7 @@ def __init__(self, input_size: int, hidden_size: int,
         self.with_horizontal = True
         self.with_fc = with_fc
 
+        self.fc = None
         if with_fc:
             if union == "cat":
                 self.fc = nn.Linear(2 * self.output_size, input_size)
@@ -159,33 +162,38 @@ def forward(self, x):
             v, _ = self.rnn_v(v)
             v = v.reshape(B, W, H, -1)
             v = v.permute(0, 2, 1, 3)
+        else:
+            v = None
 
         if self.with_horizontal:
             h = x.reshape(-1, W, C)
             h, _ = self.rnn_h(h)
             h = h.reshape(B, H, W, -1)
+        else:
+            h = None
 
-        if self.with_vertical and self.with_horizontal:
+        if v is not None and h is not None:
             if self.union == "cat":
                 x = torch.cat([v, h], dim=-1)
             else:
                 x = v + h
-        elif self.with_vertical:
+        elif v is not None:
             x = v
-        elif self.with_horizontal:
+        elif h is not None:
             x = h
 
-        if self.with_fc:
+        if self.fc is not None:
             x = self.fc(x)
 
         return x
 
 
 class LSTM2D(RNN2DBase):
 
-    def __init__(self, input_size: int, hidden_size: int,
-                 num_layers: int = 1, bias: bool = True, bidirectional: bool = True,
-                 union="cat", with_fc=True):
+    def __init__(
+            self, input_size: int, hidden_size: int,
+            num_layers: int = 1, bias: bool = True, bidirectional: bool = True,
+            union="cat", with_fc=True):
         super().__init__(input_size, hidden_size, num_layers, bias, bidirectional, union, with_fc)
         if self.with_vertical:
             self.rnn_v = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True, bias=bias, bidirectional=bidirectional)
@@ -194,10 +202,10 @@ def __init__(self, input_size: int, hidden_size: int,
 
 
 class Sequencer2DBlock(nn.Module):
-    def __init__(self, dim, hidden_size, mlp_ratio=3.0, rnn_layer=LSTM2D, mlp_layer=Mlp,
-                 norm_layer=partial(nn.LayerNorm, eps=1e-6), act_layer=nn.GELU,
-                 num_layers=1, bidirectional=True, union="cat", with_fc=True,
-                 drop=0., drop_path=0.):
+    def __init__(
+            self, dim, hidden_size, mlp_ratio=3.0, rnn_layer=LSTM2D, mlp_layer=Mlp,
+            norm_layer=partial(nn.LayerNorm, eps=1e-6), act_layer=nn.GELU,
+            num_layers=1, bidirectional=True, union="cat", with_fc=True, drop=0., drop_path=0.):
         super().__init__()
         channels_dim = int(mlp_ratio * dim)
         self.norm1 = norm_layer(dim)
@@ -255,6 +263,7 @@ def __init__(
             num_classes=1000,
             img_size=224,
             in_chans=3,
+            global_pool='avg',
             layers=[4, 3, 8, 3],
             patch_sizes=[7, 2, 1, 1],
             embed_dims=[192, 384, 384, 384],
@@ -275,7 +284,9 @@ def __init__(
             stem_norm=False,
     ):
         super().__init__()
+        assert global_pool in ('', 'avg')
         self.num_classes = num_classes
+        self.global_pool = global_pool
         self.num_features = embed_dims[-1]  # num_features for consistency with other models
         self.embed_dims = embed_dims
         self.stem = PatchEmbed(
@@ -301,38 +312,54 @@ def init_weights(self, nlhb=False):
         head_bias = -math.log(self.num_classes) if nlhb else 0.
         named_apply(partial(_init_weights, head_bias=head_bias), module=self)  # depth-first
 
+    @torch.jit.ignore
+    def group_matcher(self, coarse=False):
+        return dict(
+            stem=r'^stem',
+            blocks=[
+                (r'^blocks\.(\d+)\..*\.down', (99999,)),
+                (r'^blocks\.(\d+)', None) if coarse else (r'^blocks\.(\d+)\.(\d+)', None),
+                (r'^norm', (99999,))
+            ]
+        )
+
+    @torch.jit.ignore
+    def set_grad_checkpointing(self, enable=True):
+        assert not enable, 'gradient checkpointing not supported'
+
+    @torch.jit.ignore
     def get_classifier(self):
         return self.head
 
-    def reset_classifier(self, num_classes, global_pool=''):
+    def reset_classifier(self, num_classes, global_pool=None):
         self.num_classes = num_classes
+        if self.global_pool is not None:
+            assert global_pool in ('', 'avg')
+            self.global_pool = global_pool
         self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
 
     def forward_features(self, x):
         x = self.stem(x)
         x = self.blocks(x)
         x = self.norm(x)
-        x = x.mean(dim=(1, 2))
         return x
 
+    def forward_head(self, x, pre_logits: bool = False):
+        if self.global_pool == 'avg':
+            x = x.mean(dim=(1, 2))
+        return x if pre_logits else self.head(x)
+
     def forward(self, x):
         x = self.forward_features(x)
-        x = self.head(x)
+        x = self.forward_head(x)
         return x
 
 
-def checkpoint_filter_fn(state_dict, model):
-    return state_dict
-
-
 def _create_sequencer2d(variant, pretrained=False, **kwargs):
     if kwargs.get('features_only', None):
         raise RuntimeError('features_only not implemented for Sequencer2D models.')
 
-    model = build_model_with_cfg(
-        Sequencer2D, variant, pretrained,
-        pretrained_filter_fn=checkpoint_filter_fn,
-        **kwargs)
+    model = build_model_with_cfg(Sequencer2D, variant, pretrained, **kwargs)
     return model