wehub-resource-sync
129 lines
4.6 KiB
Python
129 lines
4.6 KiB
Python
# Copyright 2024 MIT Han Lab
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# SPDX-License-Identifier: Apache-2.0
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import json
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from typing import Any
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import numpy as np
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import torch.nn as nn
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from ...apps.utils import CosineLRwithWarmup, build_optimizer
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__all__ = ["Scheduler", "RunConfig"]
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class Scheduler:
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PROGRESS = 0
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class RunConfig:
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n_epochs: int
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init_lr: float
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warmup_epochs: int
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warmup_lr: float
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lr_schedule_name: str
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lr_schedule_param: dict
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optimizer_name: str
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optimizer_params: dict
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weight_decay: float
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no_wd_keys: list
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grad_clip: float # allow none to turn off grad clipping
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reset_bn: bool
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reset_bn_size: int
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reset_bn_batch_size: int
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eval_image_size: list # allow none to use image_size in data_provider
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@property
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def none_allowed(self):
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return ["grad_clip", "eval_image_size"]
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def __init__(self, **kwargs): # arguments must be passed as kwargs
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for k, val in kwargs.items():
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setattr(self, k, val)
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# check that all relevant configs are there
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annotations = {}
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for clas in type(self).mro():
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if hasattr(clas, "__annotations__"):
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annotations.update(clas.__annotations__)
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for k, k_type in annotations.items():
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assert hasattr(self, k), f"Key {k} with type {k_type} required for initialization."
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attr = getattr(self, k)
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if k in self.none_allowed:
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k_type = (k_type, type(None))
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assert isinstance(attr, k_type), f"Key {k} must be type {k_type}, provided={attr}."
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self.global_step = 0
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self.batch_per_epoch = 1
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def build_optimizer(self, network: nn.Module) -> tuple[Any, Any]:
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r"""require setting 'batch_per_epoch' before building optimizer & lr_scheduler"""
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param_dict = {}
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for name, param in network.named_parameters():
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if param.requires_grad:
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opt_config = [self.weight_decay, self.init_lr]
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if self.no_wd_keys is not None and len(self.no_wd_keys) > 0:
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if np.any([key in name for key in self.no_wd_keys]):
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opt_config[0] = 0
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opt_key = json.dumps(opt_config)
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param_dict[opt_key] = param_dict.get(opt_key, []) + [param]
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net_params = []
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for opt_key, param_list in param_dict.items():
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wd, lr = json.loads(opt_key)
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net_params.append({"params": param_list, "weight_decay": wd, "lr": lr})
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optimizer = build_optimizer(net_params, self.optimizer_name, self.optimizer_params, self.init_lr)
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# build lr scheduler
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if self.lr_schedule_name == "cosine":
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decay_steps = []
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for epoch in self.lr_schedule_param.get("step", []):
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decay_steps.append(epoch * self.batch_per_epoch)
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decay_steps.append(self.n_epochs * self.batch_per_epoch)
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decay_steps.sort()
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lr_scheduler = CosineLRwithWarmup(
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optimizer,
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self.warmup_epochs * self.batch_per_epoch,
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self.warmup_lr,
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decay_steps,
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)
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else:
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raise NotImplementedError
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return optimizer, lr_scheduler
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def update_global_step(self, epoch, batch_id=0) -> None:
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self.global_step = epoch * self.batch_per_epoch + batch_id
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Scheduler.PROGRESS = self.progress
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@property
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def progress(self) -> float:
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warmup_steps = self.warmup_epochs * self.batch_per_epoch
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steps = max(0, self.global_step - warmup_steps)
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return steps / (self.n_epochs * self.batch_per_epoch)
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def step(self) -> None:
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self.global_step += 1
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Scheduler.PROGRESS = self.progress
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def get_remaining_epoch(self, epoch, post=True) -> int:
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return self.n_epochs + self.warmup_epochs - epoch - int(post)
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def epoch_format(self, epoch: int) -> str:
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epoch_format = f"%.{len(str(self.n_epochs))}d"
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epoch_format = f"[{epoch_format}/{epoch_format}]"
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epoch_format = epoch_format % (epoch + 1 - self.warmup_epochs, self.n_epochs)
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return epoch_format
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