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segmentation/mmcv_custom/layer_decay_vit.py

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+import json
+from mmcv.runner import OPTIMIZER_BUILDERS, DefaultOptimizerConstructor
+from mmcv.runner import get_dist_info
+
+
+def get_num_layer_for_vit(var_name, num_max_layer, layer_sep=None):
+    if var_name in ("backbone.cls_token", "backbone.mask_token", "backbone.pos_embed"):
+        return 0
+    elif var_name.startswith("backbone.patch_embed"):
+        return 0
+    elif var_name.startswith("backbone.blocks"):
+        layer_id = int(var_name.split('.')[2])
+        return layer_id + 1
+    elif var_name.startswith("backbone.layers"):
+        assert layer_sep is not None
+        split = var_name.split('.')
+        start_id = layer_sep[int(split[2])]
+        if split[3] == 'RC':
+            return start_id
+        return start_id + int(split[4]) + 1
+    else:
+        return num_max_layer - 1
+
+
+@OPTIMIZER_BUILDERS.register_module()
+class LayerDecayOptimizerConstructor_vit(DefaultOptimizerConstructor):
+    def add_params(self, params, module, prefix='', is_dcn_module=None):
+        """Add all parameters of module to the params list.
+        The parameters of the given module will be added to the list of param
+        groups, with specific rules defined by paramwise_cfg.
+        Args:
+            params (list[dict]): A list of param groups, it will be modified
+                in place.
+            module (nn.Module): The module to be added.
+            prefix (str): The prefix of the module
+            is_dcn_module (int|float|None): If the current module is a
+                submodule of DCN, `is_dcn_module` will be passed to
+                control conv_offset layer's learning rate. Defaults to None.
+        """
+        # get param-wise options
+
+        parameter_groups = {}
+        print(self.paramwise_cfg)
+        num_layers = self.paramwise_cfg.get('num_layers') + 2
+        layer_sep = self.paramwise_cfg.get('layer_sep', None)
+        layer_decay_rate = self.paramwise_cfg.get('layer_decay_rate')
+        print("Build LayerDecayOptimizerConstructor %f - %d" % (layer_decay_rate, num_layers))
+        weight_decay = self.base_wd
+
+        custom_keys = self.paramwise_cfg.get('custom_keys', {})
+        # first sort with alphabet order and then sort with reversed len of str
+        sorted_keys = sorted(custom_keys.keys())
+
+        for name, param in module.named_parameters():
+
+            if not param.requires_grad:
+                continue  # frozen weights
+
+            if len(param.shape) == 1 or name.endswith(".bias") or ('pos_embed' in name) or ('cls_token' in name) or ('rel_pos_' in name):
+                group_name = "no_decay"
+                this_weight_decay = 0.
+            else:
+                group_name = "decay"
+                this_weight_decay = weight_decay
+
+            layer_id = get_num_layer_for_vit(name, num_layers, layer_sep)
+            group_name = "layer_%d_%s" % (layer_id, group_name)
+
+            # if the parameter match one of the custom keys, ignore other rules
+            this_lr_multi = 1.
+            for key in sorted_keys:
+                if key in f'{name}':
+                    lr_mult = custom_keys[key].get('lr_mult', 1.)
+                    this_lr_multi = lr_mult
+                    group_name = "%s_%s" % (group_name, key)
+                    break
+
+            if group_name not in parameter_groups:
+                scale = layer_decay_rate ** (num_layers - layer_id - 1)
+
+                parameter_groups[group_name] = {
+                    "weight_decay": this_weight_decay,
+                    "params": [],
+                    "param_names": [], 
+                    "lr_scale": scale, 
+                    "group_name": group_name, 
+                    "lr": scale * self.base_lr * this_lr_multi, 
+                }
+
+            parameter_groups[group_name]["params"].append(param)
+            parameter_groups[group_name]["param_names"].append(name)
+
+        rank, _ = get_dist_info()
+        if rank == 0:
+            to_display = {}
+            for key in parameter_groups:
+                to_display[key] = {
+                    "param_names": parameter_groups[key]["param_names"], 
+                    "lr_scale": parameter_groups[key]["lr_scale"], 
+                    "lr": parameter_groups[key]["lr"], 
+                    "weight_decay": parameter_groups[key]["weight_decay"], 
+                }
+            print("Param groups = %s" % json.dumps(to_display, indent=2))
+
+        params.extend(parameter_groups.values())