darkliang
463 lines
17 KiB
Python
463 lines
17 KiB
Python
"""The base model definition.
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This module defines the abstract meta model class and base model class. In the base model,
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we define the basic model functions, like get_loader, build_network, and run_train, etc.
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The api of the base model is run_train and run_test, they are used in `opengait/main.py`.
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Typical usage:
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BaseModel.run_train(model)
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BaseModel.run_test(model)
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"""
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import torch
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import numpy as np
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import os.path as osp
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import torch.nn as nn
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import torch.optim as optim
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import torch.utils.data as tordata
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from tqdm import tqdm
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from torch.cuda.amp import autocast
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from torch.cuda.amp import GradScaler
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from abc import ABCMeta
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from abc import abstractmethod
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from . import backbones
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from .loss_aggregator import LossAggregator
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from data.transform import get_transform
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from data.collate_fn import CollateFn
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from data.dataset import DataSet
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import data.sampler as Samplers
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from utils import Odict, mkdir, ddp_all_gather
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from utils import get_valid_args, is_list, is_dict, np2var, ts2np, list2var, get_attr_from
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from evaluation import evaluator as eval_functions
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from utils import NoOp
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from utils import get_msg_mgr
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__all__ = ['BaseModel']
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class MetaModel(metaclass=ABCMeta):
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"""The necessary functions for the base model.
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This class defines the necessary functions for the base model, in the base model, we have implemented them.
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"""
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@abstractmethod
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def get_loader(self, data_cfg):
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"""Based on the given data_cfg, we get the data loader."""
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raise NotImplementedError
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@abstractmethod
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def build_network(self, model_cfg):
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"""Build your network here."""
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raise NotImplementedError
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@abstractmethod
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def init_parameters(self):
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"""Initialize the parameters of your network."""
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raise NotImplementedError
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@abstractmethod
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def get_optimizer(self, optimizer_cfg):
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"""Based on the given optimizer_cfg, we get the optimizer."""
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raise NotImplementedError
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@abstractmethod
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def get_scheduler(self, scheduler_cfg):
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"""Based on the given scheduler_cfg, we get the scheduler."""
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raise NotImplementedError
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@abstractmethod
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def save_ckpt(self, iteration):
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"""Save the checkpoint, including model parameter, optimizer and scheduler."""
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raise NotImplementedError
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@abstractmethod
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def resume_ckpt(self, restore_hint):
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"""Resume the model from the checkpoint, including model parameter, optimizer and scheduler."""
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raise NotImplementedError
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@abstractmethod
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def inputs_pretreament(self, inputs):
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"""Transform the input data based on transform setting."""
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raise NotImplementedError
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@abstractmethod
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def train_step(self, loss_num) -> bool:
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"""Do one training step."""
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raise NotImplementedError
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@abstractmethod
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def inference(self):
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"""Do inference (calculate features.)."""
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raise NotImplementedError
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@abstractmethod
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def run_train(model):
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"""Run a whole train schedule."""
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raise NotImplementedError
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@abstractmethod
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def run_test(model):
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"""Run a whole test schedule."""
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raise NotImplementedError
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class BaseModel(MetaModel, nn.Module):
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"""Base model.
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This class inherites the MetaModel class, and implements the basic model functions, like get_loader, build_network, etc.
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Attributes:
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msg_mgr: the massage manager.
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cfgs: the configs.
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iteration: the current iteration of the model.
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engine_cfg: the configs of the engine(train or test).
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save_path: the path to save the checkpoints.
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"""
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def __init__(self, cfgs, training):
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"""Initialize the base model.
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Complete the model initialization, including the data loader, the network, the optimizer, the scheduler, the loss.
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Args:
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cfgs:
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All of the configs.
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training:
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Whether the model is in training mode.
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"""
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super(BaseModel, self).__init__()
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self.msg_mgr = get_msg_mgr()
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self.cfgs = cfgs
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self.iteration = 0
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self.engine_cfg = cfgs['trainer_cfg'] if training else cfgs['evaluator_cfg']
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if self.engine_cfg is None:
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raise Exception("Initialize a model without -Engine-Cfgs-")
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if training and self.engine_cfg['enable_float16']:
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self.Scaler = GradScaler()
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self.save_path = osp.join('output/', cfgs['data_cfg']['dataset_name'],
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cfgs['model_cfg']['model'], self.engine_cfg['save_name'])
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self.build_network(cfgs['model_cfg'])
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self.init_parameters()
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self.msg_mgr.log_info(cfgs['data_cfg'])
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if training:
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self.train_loader = self.get_loader(
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cfgs['data_cfg'], train=True)
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if not training or self.engine_cfg['with_test']:
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self.test_loader = self.get_loader(
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cfgs['data_cfg'], train=False)
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self.device = torch.distributed.get_rank()
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torch.cuda.set_device(self.device)
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self.to(device=torch.device(
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"cuda", self.device))
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if training:
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self.loss_aggregator = LossAggregator(cfgs['loss_cfg'])
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self.optimizer = self.get_optimizer(self.cfgs['optimizer_cfg'])
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self.scheduler = self.get_scheduler(cfgs['scheduler_cfg'])
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self.train(training)
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restore_hint = self.engine_cfg['restore_hint']
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if restore_hint != 0:
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self.resume_ckpt(restore_hint)
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def get_backbone(self, backbone_cfg):
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"""Get the backbone of the model."""
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if is_dict(backbone_cfg):
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Backbone = get_attr_from([backbones], backbone_cfg['type'])
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valid_args = get_valid_args(Backbone, backbone_cfg, ['type'])
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return Backbone(**valid_args)
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if is_list(backbone_cfg):
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Backbone = nn.ModuleList([self.get_backbone(cfg)
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for cfg in backbone_cfg])
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return Backbone
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raise ValueError(
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"Error type for -Backbone-Cfg-, supported: (A list of) dict.")
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def build_network(self, model_cfg):
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if 'backbone_cfg' in model_cfg.keys():
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self.Backbone = self.get_backbone(model_cfg['backbone_cfg'])
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def init_parameters(self):
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for m in self.modules():
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if isinstance(m, (nn.Conv3d, nn.Conv2d, nn.Conv1d)):
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nn.init.xavier_uniform_(m.weight.data)
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if m.bias is not None:
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nn.init.constant_(m.bias.data, 0.0)
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elif isinstance(m, nn.Linear):
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nn.init.xavier_uniform_(m.weight.data)
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if m.bias is not None:
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nn.init.constant_(m.bias.data, 0.0)
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elif isinstance(m, (nn.BatchNorm3d, nn.BatchNorm2d, nn.BatchNorm1d)):
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if m.affine:
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nn.init.normal_(m.weight.data, 1.0, 0.02)
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nn.init.constant_(m.bias.data, 0.0)
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def get_loader(self, data_cfg, train=True):
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sampler_cfg = self.cfgs['trainer_cfg']['sampler'] if train else self.cfgs['evaluator_cfg']['sampler']
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dataset = DataSet(data_cfg, train)
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Sampler = get_attr_from([Samplers], sampler_cfg['type'])
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vaild_args = get_valid_args(Sampler, sampler_cfg, free_keys=[
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'sample_type', 'type'])
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sampler = Sampler(dataset, **vaild_args)
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loader = tordata.DataLoader(
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dataset=dataset,
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batch_sampler=sampler,
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collate_fn=CollateFn(dataset.label_set, sampler_cfg),
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num_workers=data_cfg['num_workers'])
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return loader
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def get_optimizer(self, optimizer_cfg):
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self.msg_mgr.log_info(optimizer_cfg)
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optimizer = get_attr_from([optim], optimizer_cfg['solver'])
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valid_arg = get_valid_args(optimizer, optimizer_cfg, ['solver'])
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optimizer = optimizer(
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filter(lambda p: p.requires_grad, self.parameters()), **valid_arg)
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return optimizer
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def get_scheduler(self, scheduler_cfg):
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self.msg_mgr.log_info(scheduler_cfg)
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Scheduler = get_attr_from(
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[optim.lr_scheduler], scheduler_cfg['scheduler'])
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valid_arg = get_valid_args(Scheduler, scheduler_cfg, ['scheduler'])
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scheduler = Scheduler(self.optimizer, **valid_arg)
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return scheduler
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def save_ckpt(self, iteration):
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if torch.distributed.get_rank() == 0:
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mkdir(osp.join(self.save_path, "checkpoints/"))
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save_name = self.engine_cfg['save_name']
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checkpoint = {
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'model': self.state_dict(),
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'optimizer': self.optimizer.state_dict(),
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'scheduler': self.scheduler.state_dict(),
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'iteration': iteration}
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torch.save(checkpoint,
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osp.join(self.save_path, 'checkpoints/{}-{:0>5}.pt'.format(save_name, iteration)))
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def _load_ckpt(self, save_name):
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load_ckpt_strict = self.engine_cfg['restore_ckpt_strict']
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checkpoint = torch.load(save_name, map_location=torch.device(
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"cuda", self.device))
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model_state_dict = checkpoint['model']
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if not load_ckpt_strict:
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self.msg_mgr.log_info("-------- Restored Params List --------")
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self.msg_mgr.log_info(sorted(set(model_state_dict.keys()).intersection(
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set(self.state_dict().keys()))))
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self.load_state_dict(model_state_dict, strict=load_ckpt_strict)
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if self.training:
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if not self.engine_cfg["optimizer_reset"] and 'optimizer' in checkpoint:
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self.optimizer.load_state_dict(checkpoint['optimizer'])
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else:
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self.msg_mgr.log_warning(
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"Restore NO Optimizer from %s !!!" % save_name)
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if not self.engine_cfg["scheduler_reset"] and 'scheduler' in checkpoint:
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self.scheduler.load_state_dict(
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checkpoint['scheduler'])
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else:
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self.msg_mgr.log_warning(
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"Restore NO Scheduler from %s !!!" % save_name)
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self.msg_mgr.log_info("Restore Parameters from %s !!!" % save_name)
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def resume_ckpt(self, restore_hint):
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if isinstance(restore_hint, int):
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save_name = self.engine_cfg['save_name']
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save_name = osp.join(
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self.save_path, 'checkpoints/{}-{:0>5}.pt'.format(save_name, restore_hint))
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self.iteration = restore_hint
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elif isinstance(restore_hint, str):
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save_name = restore_hint
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self.iteration = 0
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else:
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raise ValueError(
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"Error type for -Restore_Hint-, supported: int or string.")
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self._load_ckpt(save_name)
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def fix_BN(self):
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for module in self.modules():
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classname = module.__class__.__name__
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if classname.find('BatchNorm') != -1:
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module.eval()
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def inputs_pretreament(self, inputs):
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"""Conduct transforms on input data.
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Args:
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inputs: the input data.
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Returns:
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tuple: training data including inputs, labels, and some meta data.
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"""
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seqs_batch, labs_batch, typs_batch, vies_batch, seqL_batch = inputs
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trf_cfgs = self.engine_cfg['transform']
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seq_trfs = get_transform(trf_cfgs)
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if len(seqs_batch) != len(seq_trfs):
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raise ValueError(
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"The number of types of input data and transform should be same. But got {} and {}".format(len(seqs_batch), len(seq_trfs)))
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requires_grad = bool(self.training)
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seqs = [np2var(np.asarray([trf(fra) for fra in seq]), requires_grad=requires_grad).float()
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for trf, seq in zip(seq_trfs, seqs_batch)]
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typs = typs_batch
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vies = vies_batch
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labs = list2var(labs_batch).long()
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if seqL_batch is not None:
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seqL_batch = np2var(seqL_batch).int()
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seqL = seqL_batch
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if seqL is not None:
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seqL_sum = int(seqL.sum().data.cpu().numpy())
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ipts = [_[:, :seqL_sum] for _ in seqs]
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else:
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ipts = seqs
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del seqs
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return ipts, labs, typs, vies, seqL
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def train_step(self, loss_sum) -> bool:
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"""Conduct loss_sum.backward(), self.optimizer.step() and self.scheduler.step().
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Args:
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loss_sum:The loss of the current batch.
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Returns:
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bool: True if the training is finished, False otherwise.
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"""
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self.optimizer.zero_grad()
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if loss_sum <= 1e-9:
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self.msg_mgr.log_warning(
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"Find the loss sum less than 1e-9 but the training process will continue!")
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if self.engine_cfg['enable_float16']:
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self.Scaler.scale(loss_sum).backward()
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self.Scaler.step(self.optimizer)
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scale = self.Scaler.get_scale()
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self.Scaler.update()
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# Warning caused by optimizer skip when NaN
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# https://discuss.pytorch.org/t/optimizer-step-before-lr-scheduler-step-error-using-gradscaler/92930/5
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if scale != self.Scaler.get_scale():
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self.msg_mgr.log_debug("Training step skip. Expected the former scale equals to the present, got {} and {}".format(
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scale, self.Scaler.get_scale()))
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return False
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else:
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loss_sum.backward()
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self.optimizer.step()
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self.iteration += 1
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self.scheduler.step()
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return True
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def inference(self, rank):
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"""Inference all the test data.
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Args:
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rank: the rank of the current process.Transform
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Returns:
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Odict: contains the inference results.
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"""
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total_size = len(self.test_loader)
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if rank == 0:
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pbar = tqdm(total=total_size, desc='Transforming')
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else:
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pbar = NoOp()
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batch_size = self.test_loader.batch_sampler.batch_size
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rest_size = total_size
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info_dict = Odict()
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for inputs in self.test_loader:
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ipts = self.inputs_pretreament(inputs)
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with autocast(enabled=self.engine_cfg['enable_float16']):
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retval = self.forward(ipts)
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inference_feat = retval['inference_feat']
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for k, v in inference_feat.items():
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inference_feat[k] = ddp_all_gather(v, requires_grad=False)
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del retval
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for k, v in inference_feat.items():
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inference_feat[k] = ts2np(v)
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info_dict.append(inference_feat)
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rest_size -= batch_size
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if rest_size >= 0:
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update_size = batch_size
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else:
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update_size = total_size % batch_size
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pbar.update(update_size)
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pbar.close()
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for k, v in info_dict.items():
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v = np.concatenate(v)[:total_size]
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info_dict[k] = v
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return info_dict
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@ staticmethod
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def run_train(model):
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"""Accept the instance object(model) here, and then run the train loop."""
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for inputs in model.train_loader:
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ipts = model.inputs_pretreament(inputs)
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with autocast(enabled=model.engine_cfg['enable_float16']):
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retval = model(ipts)
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training_feat, visual_summary = retval['training_feat'], retval['visual_summary']
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del retval
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loss_sum, loss_info = model.loss_aggregator(training_feat)
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ok = model.train_step(loss_sum)
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if not ok:
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continue
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visual_summary.update(loss_info)
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visual_summary['scalar/learning_rate'] = model.optimizer.param_groups[0]['lr']
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model.msg_mgr.train_step(loss_info, visual_summary)
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if model.iteration % model.engine_cfg['save_iter'] == 0:
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# save the checkpoint
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model.save_ckpt(model.iteration)
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# run test if with_test = true
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if model.engine_cfg['with_test']:
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model.msg_mgr.log_info("Running test...")
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model.eval()
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result_dict = BaseModel.run_test(model)
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model.train()
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if model.cfgs['trainer_cfg']['fix_BN']:
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model.fix_BN()
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model.msg_mgr.write_to_tensorboard(result_dict)
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model.msg_mgr.reset_time()
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if model.iteration >= model.engine_cfg['total_iter']:
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break
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@ staticmethod
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def run_test(model):
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"""Accept the instance object(model) here, and then run the test loop."""
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rank = torch.distributed.get_rank()
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with torch.no_grad():
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info_dict = model.inference(rank)
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if rank == 0:
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loader = model.test_loader
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label_list = loader.dataset.label_list
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types_list = loader.dataset.types_list
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views_list = loader.dataset.views_list
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info_dict.update({
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'labels': label_list, 'types': types_list, 'views': views_list})
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if 'eval_func' in model.cfgs["evaluator_cfg"].keys():
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eval_func = model.cfgs['evaluator_cfg']["eval_func"]
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else:
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eval_func = 'identification'
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eval_func = getattr(eval_functions, eval_func)
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valid_args = get_valid_args(
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eval_func, model.cfgs["evaluator_cfg"], ['metric'])
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try:
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dataset_name = model.cfgs['data_cfg']['test_dataset_name']
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except:
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dataset_name = model.cfgs['data_cfg']['dataset_name']
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return eval_func(info_dict, dataset_name, **valid_args)
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