update some model

timm/models/convnext.py

@@ -452,29 +452,29 @@ class ConvNeXt(nn.Module):
         """
         assert output_fmt in ('NCHW',), 'Output shape must be NCHW.'
         intermediates = []
-        take_indices, max_index = feature_take_indices(len(self.stages) + 1, indices)
+        take_indices, max_index = feature_take_indices(len(self.stages), indices)
 
         # forward pass
-        feat_idx = 0  # stem is index 0
         x = self.stem(x)
-        if feat_idx in take_indices:
-            intermediates.append(x)
 
+        last_idx = len(self.stages) - 1
         if torch.jit.is_scripting() or not stop_early:  # can't slice blocks in torchscript
             stages = self.stages
         else:
-            stages = self.stages[:max_index]
+            stages = self.stages[:max_index + 1]
-        for stage in stages:
+        for feat_idx, stage in enumerate(stages):
-            feat_idx += 1
             x = stage(x)
             if feat_idx in take_indices:
-                # NOTE not bothering to apply norm_pre when norm=True as almost no models have it enabled
+                if norm and feat_idx == last_idx:
-                intermediates.append(x)
+                    intermediates.append(self.norm_pre(x))
+                else:
+                    intermediates.append(x)
 
         if intermediates_only:
             return intermediates
 
-        x = self.norm_pre(x)
+        if feat_idx == last_idx:
+            x = self.norm_pre(x)
 
         return x, intermediates
 

timm/models/focalnet.py

@@ -491,7 +491,7 @@ class FocalNet(nn.Module):
         else:
             stages = self.layers[:max_index + 1]
 
-        last_idx = len(self.layers)
+        last_idx = len(self.layers) - 1
         for feat_idx, stage in enumerate(stages):
             x = stage(x)
             if feat_idx in take_indices:

timm/models/mvitv2.py

@@ -870,10 +870,11 @@ class MultiScaleVit(nn.Module):
         if self.pos_embed is not None:
             x = x + self.pos_embed
 
-        for i, stage in enumerate(self.stages):
+        last_idx = len(self.stages) - 1
+        for feat_idx, stage in enumerate(self.stages):
             x, feat_size = stage(x, feat_size)
-            if i in take_indices:
+            if feat_idx in take_indices:
-                if norm and i == (len(self.stages) - 1):
+                if norm and feat_idx == last_idx:
                     x_inter = self.norm(x)  # applying final norm last intermediate
                 else:
                     x_inter = x
@@ -887,7 +888,8 @@ class MultiScaleVit(nn.Module):
         if intermediates_only:
             return intermediates
 
-        x = self.norm(x)
+        if feat_idx == last_idx:
+            x = self.norm(x)
 
         return x, intermediates
 

timm/models/pit.py

@@ -14,7 +14,7 @@ Modifications for timm by / Copyright 2020 Ross Wightman
 import math
 import re
 from functools import partial
-from typing import Optional, Sequence, Tuple
+from typing import List, Optional, Sequence, Tuple, Union
 
 import torch
 from torch import nn
@@ -22,6 +22,7 @@ from torch import nn
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
 from timm.layers import trunc_normal_, to_2tuple
 from ._builder import build_model_with_cfg
+from ._features import feature_take_indices
 from ._registry import register_model, generate_default_cfgs
 from .vision_transformer import Block
 
@@ -254,6 +255,71 @@ class PoolingVisionTransformer(nn.Module):
         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_intermediates(
+            self,
+            x: torch.Tensor,
+            indices: Optional[Union[int, List[int]]] = None,
+            norm: bool = False,
+            stop_early: bool = False,
+            output_fmt: str = 'NCHW',
+            intermediates_only: bool = False,
+    ) -> Union[List[torch.Tensor], Tuple[torch.Tensor, List[torch.Tensor]]]:
+        """ Forward features that returns intermediates.
+
+        Args:
+            x: Input image tensor
+            indices: Take last n blocks if int, all if None, select matching indices if sequence
+            norm: Apply norm layer to compatible intermediates
+            stop_early: Stop iterating over blocks when last desired intermediate hit
+            output_fmt: Shape of intermediate feature outputs
+            intermediates_only: Only return intermediate features
+        Returns:
+
+        """
+        assert output_fmt in ('NCHW',), 'Output shape must be NCHW.'
+        intermediates = []
+        take_indices, max_index = feature_take_indices(len(self.transformers), indices)
+        
+        # forward pass
+        x = self.patch_embed(x)
+        x = self.pos_drop(x + self.pos_embed)
+        cls_tokens = self.cls_token.expand(x.shape[0], -1, -1)
+  
+        last_idx = len(self.transformers) - 1
+        if torch.jit.is_scripting() or not stop_early:  # can't slice blocks in torchscript
+            stages = self.transformers
+        else:
+            stages = self.transformers[:max_index + 1]
+
+        for feat_idx, stage in enumerate(stages):
+            x, cls_tokens = stage((x, cls_tokens))
+            if feat_idx in take_indices:
+                intermediates.append(x) 
+
+        if intermediates_only:
+            return intermediates
+        
+        if feat_idx == last_idx:
+            cls_tokens = self.norm(cls_tokens)
+
+        return cls_tokens, intermediates
+
+    def prune_intermediate_layers(
+            self,
+            indices: Union[int, List[int]] = 1,
+            prune_norm: bool = False,
+            prune_head: bool = True,
+    ):
+        """ Prune layers not required for specified intermediates.
+        """
+        take_indices, max_index = feature_take_indices(len(self.transformers), indices)
+        self.stages = self.stages[:max_index + 1]  # truncate blocks w/ stem as idx 0
+        if prune_norm:
+            self.norm = nn.Identity()
+        if prune_head:
+            self.reset_classifier(0, '')
+        return take_indices
+
     def forward_features(self, x):
         x = self.patch_embed(x)
         x = self.pos_drop(x + self.pos_embed)

timm/models/rdnet.py

@@ -302,20 +302,20 @@ class RDNet(nn.Module):
         """
         assert output_fmt in ('NCHW',), 'Output shape must be NCHW.'
         intermediates = []
-        take_indices, max_index = feature_take_indices(len(self.dense_stages) + 1, indices)
+        stage_ends = [int(info['module'].split('.')[-1]) for info in self.feature_info]
+        take_indices, max_index = feature_take_indices(len(stage_ends), indices)
+        take_indices = [stage_ends[i] for i in take_indices]
+        max_index = stage_ends[max_index]
 
         # forward pass
-        feat_idx = 0  # stem is index 0
         x = self.stem(x)
-        if feat_idx in take_indices:
+
-            intermediates.append(x)
+        last_idx = len(self.dense_stages) - 1
-        last_idx = len(self.dense_stages)
         if torch.jit.is_scripting() or not stop_early:  # can't slice blocks in torchscript
             dense_stages = self.dense_stages
         else:
-            dense_stages = self.dense_stages[:max_index]
+            dense_stages = self.dense_stages[:max_index + 1]
-        for stage in dense_stages:
+        for feat_idx, stage in enumerate(dense_stages):
-            feat_idx += 1
             x = stage(x)
             if feat_idx in take_indices:
                 if norm and feat_idx == last_idx:
@@ -340,8 +340,10 @@ class RDNet(nn.Module):
     ):
         """ Prune layers not required for specified intermediates.
         """
-        take_indices, max_index = feature_take_indices(len(self.dense_stages) + 1, indices)
+        stage_ends = [int(info['module'].split('.')[-1]) for info in self.feature_info]
-        self.dense_stages = self.dense_stages[:max_index]  # truncate blocks w/ stem as idx 0
+        take_indices, max_index = feature_take_indices(len(stage_ends), indices)
+        max_index = stage_ends[max_index]
+        self.dense_stages = self.dense_stages[:max_index + 1]  # truncate blocks w/ stem as idx 0
         if prune_norm:
             self.norm_pre = nn.Identity()
         if prune_head:

timm/models/resnetv2.py

@@ -571,9 +571,13 @@ class ResNetV2(nn.Module):
         
         # forward pass
         feat_idx = 0
-        x = self.stem(x)
+        H, W = x.shape[-2:]
+        for stem in self.stem:
+            x = stem(x)
+            if x.shape[-2:] == (H //2, W //2):
+                x_down = x
         if feat_idx in take_indices:
-            intermediates.append(x)
+            intermediates.append(x_down)
         last_idx = len(self.stages)
         if torch.jit.is_scripting() or not stop_early:  # can't slice blocks in torchscript
             stages = self.stages

timm/models/xcit.py

@@ -494,7 +494,8 @@ class Xcit(nn.Module):
         # NOTE not supporting return of class tokens
         x = torch.cat((self.cls_token.expand(B, -1, -1), x), dim=1)
         for blk in self.cls_attn_blocks:
-                x = blk(x)
+            x = blk(x)
+
         x = self.norm(x)
 
         return x, intermediates