Fix a few more hiera API issues

Author: rwightman

timm/models/hiera.py

@@ -24,11 +24,12 @@
 # --------------------------------------------------------
 import math
 from functools import partial
-from typing import List, Tuple, Type, Callable, Optional, Union
+from typing import Callable, Dict, List, Optional, Tuple, Type, Union
 
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
+from torch.utils.checkpoint import checkpoint
 
 
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
@@ -480,14 +481,14 @@ def __init__(
     ):
         super().__init__()
         self.num_classes = num_classes
+        self.grad_checkpointing = False
         norm_layer = get_norm_layer(norm_layer)
-        depth = sum(stages)
+
         self.patch_stride = patch_stride
         self.tokens_spatial_shape = [i // s for i, s in zip(img_size, patch_stride)]
         num_tokens = math.prod(self.tokens_spatial_shape)
         flat_mu_size = math.prod(mask_unit_size)
         flat_q_stride = math.prod(q_stride)
-
         assert q_pool < len(stages)
         self.q_pool, self.q_stride = q_pool, q_stride
         self.mu_size, self.mask_unit_size = flat_mu_size, mask_unit_size
@@ -532,11 +533,10 @@ def __init__(
         # q_pool locations
         q_pool_blocks = [x + 1 for x in self.stage_ends[:q_pool]]
 
-        # stochastic depth decay rule
-        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]
-
         # Transformer blocks
         cur_stage = 0
+        depth = sum(stages)
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
         self.blocks = nn.ModuleList()
         self.feature_info = []
         for i in range(depth):
@@ -586,8 +586,9 @@ def __init__(
         else:
             nn.init.trunc_normal_(self.pos_embed, std=0.02)
         self.apply(partial(self._init_weights))
-        self.head.fc.weight.data.mul_(head_init_scale)
-        self.head.fc.bias.data.mul_(head_init_scale)
+        if isinstance(self.head.fc, nn.Linear):
+            self.head.fc.weight.data.mul_(head_init_scale)
+            self.head.fc.bias.data.mul_(head_init_scale)
 
     def _init_weights(self, m, init_bias=0.02):
         if isinstance(m, (nn.Linear, nn.Conv1d, nn.Conv2d, nn.Conv3d)):
@@ -605,6 +606,25 @@ def no_weight_decay(self):
         else:
             return ["pos_embed_spatial", "pos_embed_temporal"]
 
+    @torch.jit.ignore
+    def group_matcher(self, coarse: bool = False) -> Dict:
+        return dict(
+            stem=r'^pos_embed|pos_embed_spatial|pos_embed_temporal|patch_embed',  # stem and embed
+            blocks=[(r'^blocks\.(\d+)', None), (r'^norm', (99999,))]
+        )
+
+    @torch.jit.ignore
+    def set_grad_checkpointing(self, enable: bool = True) -> None:
+        self.grad_checkpointing = enable
+
+    @torch.jit.ignore
+    def get_classifier(self):
+        return self.head.fc
+
+    def reset_classifier(self, num_classes: int, global_pool: Optional[str] = None, other: bool = False):
+        self.num_classes = num_classes
+        self.head.reset(num_classes, global_pool, other=other)
+
     def get_random_mask(self, x: torch.Tensor, mask_ratio: float) -> torch.Tensor:
         """
         Generates a random mask, mask_ratio fraction are dropped.
@@ -740,7 +760,10 @@ def forward_features(
 
         intermediates = []
         for i, blk in enumerate(self.blocks):
-            x = blk(x)
+            if self.grad_checkpointing and not torch.jit.is_scripting():
+                x = checkpoint(blk, x)
+            else:
+                x = blk(x)
             if return_intermediates and i in self.stage_ends:
                 intermediates.append(self.reroll(x, i, mask=mask))