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"""Functions to get params dict"""
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import torch.nn as nn
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from rfdetr.models.backbone import Joiner
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def get_vit_lr_decay_rate(name, lr_decay_rate=1.0, num_layers=12):
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"""
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Calculate lr decay rate for different ViT blocks.
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Args:
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name (string): parameter name.
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lr_decay_rate (float): base lr decay rate.
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num_layers (int): number of ViT blocks.
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Returns:
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lr decay rate for the given parameter.
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"""
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layer_id = num_layers + 1
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if name.startswith("backbone"):
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if ".pos_embed" in name or ".patch_embed" in name:
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layer_id = 0
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elif ".blocks." in name and ".residual." not in name:
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layer_id = int(name[name.find(".blocks.") :].split(".")[2]) + 1
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print("name: {}, lr_decay: {}".format(name, lr_decay_rate ** (num_layers + 1 - layer_id)))
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return lr_decay_rate ** (num_layers + 1 - layer_id)
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def get_vit_weight_decay_rate(name, weight_decay_rate=1.0):
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if ('gamma' in name) or ('pos_embed' in name) or ('rel_pos' in name) or ('bias' in name) or ('norm' in name):
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weight_decay_rate = 0.
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print("name: {}, weight_decay rate: {}".format(name, weight_decay_rate))
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return weight_decay_rate
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def get_param_dict(args, model_without_ddp: nn.Module):
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assert isinstance(model_without_ddp.backbone, Joiner)
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backbone = model_without_ddp.backbone[0]
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backbone_named_param_lr_pairs = backbone.get_named_param_lr_pairs(args, prefix="backbone.0")
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backbone_param_lr_pairs = [param_dict for _, param_dict in backbone_named_param_lr_pairs.items()]
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decoder_key = 'transformer.decoder'
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decoder_params = [
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p
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for n, p in model_without_ddp.named_parameters() if decoder_key in n and p.requires_grad
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]
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decoder_param_lr_pairs = [
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{"params": param, "lr": args.lr * args.lr_component_decay}
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for param in decoder_params
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]
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other_params = [
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p
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for n, p in model_without_ddp.named_parameters() if (
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n not in backbone_named_param_lr_pairs and decoder_key not in n and p.requires_grad)
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]
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other_param_dicts = [
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{"params": param, "lr": args.lr}
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for param in other_params
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]
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final_param_dicts = (
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other_param_dicts + backbone_param_lr_pairs + decoder_param_lr_pairs
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)
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return final_param_dicts
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