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124 lines
4.8 KiB
124 lines
4.8 KiB
2 years ago
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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import json
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from mmcv.runner import OPTIMIZER_BUILDERS, DefaultOptimizerConstructor
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from mmcv.runner import get_dist_info
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def get_num_layer_layer_wise(var_name, num_max_layer=12):
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if var_name in ("backbone.cls_token", "backbone.mask_token", "backbone.pos_embed"):
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return 0
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elif var_name.startswith("backbone.downsample_layers"):
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stage_id = int(var_name.split('.')[2])
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if stage_id == 0:
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layer_id = 0
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elif stage_id == 1:
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layer_id = 2
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elif stage_id == 2:
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layer_id = 3
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elif stage_id == 3:
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layer_id = num_max_layer
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return layer_id
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elif var_name.startswith("backbone.stages"):
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stage_id = int(var_name.split('.')[2])
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block_id = int(var_name.split('.')[3])
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if stage_id == 0:
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layer_id = 1
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elif stage_id == 1:
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layer_id = 2
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elif stage_id == 2:
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layer_id = 3 + block_id // 3
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elif stage_id == 3:
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layer_id = num_max_layer
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return layer_id
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else:
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return num_max_layer + 1
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def get_num_layer_stage_wise(var_name, num_max_layer):
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if var_name in ("backbone.cls_token", "backbone.mask_token", "backbone.pos_embed"):
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return 0
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elif var_name.startswith("backbone.downsample_layers"):
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return 0
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elif var_name.startswith("backbone.stages"):
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stage_id = int(var_name.split('.')[2])
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return stage_id + 1
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else:
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return num_max_layer - 1
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@OPTIMIZER_BUILDERS.register_module()
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class LearningRateDecayOptimizerConstructor(DefaultOptimizerConstructor):
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def add_params(self, params, module, prefix='', is_dcn_module=None):
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"""Add all parameters of module to the params list.
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The parameters of the given module will be added to the list of param
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groups, with specific rules defined by paramwise_cfg.
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Args:
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params (list[dict]): A list of param groups, it will be modified
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in place.
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module (nn.Module): The module to be added.
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prefix (str): The prefix of the module
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is_dcn_module (int|float|None): If the current module is a
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submodule of DCN, `is_dcn_module` will be passed to
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control conv_offset layer's learning rate. Defaults to None.
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"""
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parameter_groups = {}
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print(self.paramwise_cfg)
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num_layers = self.paramwise_cfg.get('num_layers') + 2
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decay_rate = self.paramwise_cfg.get('decay_rate')
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decay_type = self.paramwise_cfg.get('decay_type', "layer_wise")
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print("Build LearningRateDecayOptimizerConstructor %s %f - %d" % (decay_type, decay_rate, num_layers))
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weight_decay = self.base_wd
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for name, param in module.named_parameters():
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if not param.requires_grad:
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continue # frozen weights
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if len(param.shape) == 1 or name.endswith(".bias") or name in ('pos_embed', 'cls_token'):
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group_name = "no_decay"
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this_weight_decay = 0.
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else:
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group_name = "decay"
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this_weight_decay = weight_decay
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if decay_type == "layer_wise":
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layer_id = get_num_layer_layer_wise(name, self.paramwise_cfg.get('num_layers'))
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elif decay_type == "stage_wise":
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layer_id = get_num_layer_stage_wise(name, num_layers)
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group_name = "layer_%d_%s" % (layer_id, group_name)
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if group_name not in parameter_groups:
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scale = decay_rate ** (num_layers - layer_id - 1)
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parameter_groups[group_name] = {
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"weight_decay": this_weight_decay,
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"params": [],
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"param_names": [],
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"lr_scale": scale,
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"group_name": group_name,
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"lr": scale * self.base_lr,
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}
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parameter_groups[group_name]["params"].append(param)
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parameter_groups[group_name]["param_names"].append(name)
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rank, _ = get_dist_info()
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if rank == 0:
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to_display = {}
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for key in parameter_groups:
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to_display[key] = {
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"param_names": parameter_groups[key]["param_names"],
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"lr_scale": parameter_groups[key]["lr_scale"],
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"lr": parameter_groups[key]["lr"],
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"weight_decay": parameter_groups[key]["weight_decay"],
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}
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print("Param groups = %s" % json.dumps(to_display, indent=2))
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params.extend(parameter_groups.values())
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