558 lines
22 KiB
Python
558 lines
22 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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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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# 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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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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import paddle
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import paddle.distributed as dist
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from visualdl import LogWriter
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from paddle import nn
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import numpy as np
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import random
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from ppcls.utils.misc import AverageMeter
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from ppcls.utils import logger
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from ppcls.utils.logger import init_logger
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from ppcls.utils.config import print_config
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from ppcls.data import build_dataloader
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from ppcls.arch import build_model, RecModel, DistillationModel, TheseusLayer
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from ppcls.arch import apply_to_static
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from ppcls.loss import build_loss
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from ppcls.metric import build_metrics
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from ppcls.optimizer import build_optimizer
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from ppcls.utils.ema import ExponentialMovingAverage
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from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
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from ppcls.utils.save_load import init_model
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from ppcls.utils import save_load
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from ppcls.data.utils.get_image_list import get_image_list
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from ppcls.data.postprocess import build_postprocess
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from ppcls.data import create_operators
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from ppcls.engine import train as train_method
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from ppcls.engine.train.utils import type_name
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from ppcls.engine import evaluation
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from ppcls.arch.gears.identity_head import IdentityHead
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class Engine(object):
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def __init__(self, config, mode="train"):
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assert mode in ["train", "eval", "infer", "export"]
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self.mode = mode
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self.config = config
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# set seed
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self._init_seed()
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# init logger
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init_logger(self.config, mode=mode)
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print_config(config)
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# for visualdl
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self.vdl_writer = self._init_vdl()
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# is_rec
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if "Head" in self.config["Arch"] or self.config["Arch"].get("is_rec",
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False):
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self.is_rec = True
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else:
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self.is_rec = False
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# init train_func and eval_func
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self.train_mode = self.config["Global"].get("train_mode", None)
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if self.train_mode is None:
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self.train_epoch_func = train_method.train_epoch
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else:
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self.train_epoch_func = getattr(train_method,
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"train_epoch_" + self.train_mode)
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self.eval_mode = self.config["Global"].get("eval_mode",
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"classification")
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assert self.eval_mode in [
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"classification", "retrieval", "adaface"
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], logger.error("Invalid eval mode: {}".format(self.eval_mode))
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self.eval_func = getattr(evaluation, self.eval_mode + "_eval")
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# set device
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self.device = self._init_device()
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# gradient accumulation
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self.update_freq = self.config["Global"].get("update_freq", 1)
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# build dataloader
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self.dataloader_dict = build_dataloader(self)
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self.train_dataloader, self.unlabel_train_dataloader, self.eval_dataloader = self.dataloader_dict[
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"Train"], self.dataloader_dict[
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"UnLabelTrain"], self.dataloader_dict["Eval"]
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self.gallery_query_dataloader, self.gallery_dataloader, self.query_dataloader = self.dataloader_dict[
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"GalleryQuery"], self.dataloader_dict[
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"Gallery"], self.dataloader_dict["Query"]
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# build loss
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self.train_loss_func, self.unlabel_train_loss_func, self.eval_loss_func = build_loss(
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self.config, self.mode)
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# build metric
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self.train_metric_func, self.eval_metric_func = build_metrics(self)
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# build model
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self.model = build_model(self.config, self.mode)
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# set @to_static for benchmark, skip this by default.
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apply_to_static(self.config, self.model)
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# load_pretrain
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self._init_pretrained()
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# build optimizer
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self.optimizer, self.lr_sch = build_optimizer(
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self.config, self.train_dataloader,
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[self.model, self.train_loss_func])
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# AMP training and evaluating
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self._init_amp()
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# for distributed
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self._init_dist()
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def train(self):
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assert self.mode == "train"
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print_batch_step = self.config['Global']['print_batch_step']
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save_interval = self.config["Global"]["save_interval"]
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best_metric = {
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"metric": -1.0,
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"epoch": 0,
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}
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# build EMA model
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self.ema = "EMA" in self.config and self.mode == "train"
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if self.ema:
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self.model_ema = ExponentialMovingAverage(
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self.model, self.config['EMA'].get("decay", 0.9999))
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best_metric_ema = 0.0
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ema_module = self.model_ema.module
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else:
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ema_module = None
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# key:
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# val: metrics list word
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self.output_info = dict()
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self.time_info = {
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"batch_cost": AverageMeter(
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"batch_cost", '.5f', postfix=" s,"),
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"reader_cost": AverageMeter(
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"reader_cost", ".5f", postfix=" s,"),
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}
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# global iter counter
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self.global_step = 0
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if self.config.Global.checkpoints is not None:
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metric_info = init_model(self.config.Global, self.model,
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self.optimizer, self.train_loss_func,
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ema_module)
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if metric_info is not None:
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best_metric.update(metric_info)
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for epoch_id in range(best_metric["epoch"] + 1,
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self.config["Global"]["epochs"] + 1):
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acc = 0.0
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# for one epoch train
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self.train_epoch_func(self, epoch_id, print_batch_step)
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metric_msg = ", ".join(
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[self.output_info[key].avg_info for key in self.output_info])
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logger.info("[Train][Epoch {}/{}][Avg]{}".format(
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epoch_id, self.config["Global"]["epochs"], metric_msg))
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self.output_info.clear()
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# eval model and save model if possible
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start_eval_epoch = self.config["Global"].get("start_eval_epoch",
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0) - 1
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if self.config["Global"][
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"eval_during_train"] and epoch_id % self.config["Global"][
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"eval_interval"] == 0 and epoch_id > start_eval_epoch:
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acc = self.eval(epoch_id)
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# step lr (by epoch) according to given metric, such as acc
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for i in range(len(self.lr_sch)):
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if getattr(self.lr_sch[i], "by_epoch", False) and \
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type_name(self.lr_sch[i]) == "ReduceOnPlateau":
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self.lr_sch[i].step(acc)
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if acc > best_metric["metric"]:
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best_metric["metric"] = acc
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best_metric["epoch"] = epoch_id
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save_load.save_model(
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self.model,
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self.optimizer,
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best_metric,
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self.output_dir,
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ema=ema_module,
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model_name=self.config["Arch"]["name"],
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prefix="best_model",
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loss=self.train_loss_func,
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save_student_model=True)
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logger.info("[Eval][Epoch {}][best metric: {}]".format(
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epoch_id, best_metric["metric"]))
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logger.scaler(
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name="eval_acc",
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value=acc,
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step=epoch_id,
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writer=self.vdl_writer)
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self.model.train()
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if self.ema:
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ori_model, self.model = self.model, ema_module
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acc_ema = self.eval(epoch_id)
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self.model = ori_model
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ema_module.eval()
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if acc_ema > best_metric_ema:
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best_metric_ema = acc_ema
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save_load.save_model(
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self.model,
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self.optimizer,
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{"metric": acc_ema,
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"epoch": epoch_id},
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self.output_dir,
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ema=ema_module,
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model_name=self.config["Arch"]["name"],
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prefix="best_model_ema",
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loss=self.train_loss_func)
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logger.info("[Eval][Epoch {}][best metric ema: {}]".format(
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epoch_id, best_metric_ema))
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logger.scaler(
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name="eval_acc_ema",
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value=acc_ema,
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step=epoch_id,
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writer=self.vdl_writer)
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# save model
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if save_interval > 0 and epoch_id % save_interval == 0:
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save_load.save_model(
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self.model,
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self.optimizer, {"metric": acc,
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"epoch": epoch_id},
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self.output_dir,
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ema=ema_module,
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model_name=self.config["Arch"]["name"],
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prefix="epoch_{}".format(epoch_id),
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loss=self.train_loss_func)
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# save the latest model
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save_load.save_model(
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self.model,
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self.optimizer, {"metric": acc,
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"epoch": epoch_id},
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self.output_dir,
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ema=ema_module,
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model_name=self.config["Arch"]["name"],
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prefix="latest",
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loss=self.train_loss_func)
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if self.vdl_writer is not None:
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self.vdl_writer.close()
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@paddle.no_grad()
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def eval(self, epoch_id=0):
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assert self.mode in ["train", "eval"]
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self.model.eval()
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eval_result = self.eval_func(self, epoch_id)
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self.model.train()
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return eval_result
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@paddle.no_grad()
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def infer(self):
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assert self.mode == "infer" and self.eval_mode == "classification"
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self.preprocess_func = create_operators(self.config["Infer"][
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"transforms"])
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self.postprocess_func = build_postprocess(self.config["Infer"][
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"PostProcess"])
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total_trainer = dist.get_world_size()
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local_rank = dist.get_rank()
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image_list = get_image_list(self.config["Infer"]["infer_imgs"])
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# data split
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image_list = image_list[local_rank::total_trainer]
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batch_size = self.config["Infer"]["batch_size"]
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self.model.eval()
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batch_data = []
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image_file_list = []
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for idx, image_file in enumerate(image_list):
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with open(image_file, 'rb') as f:
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x = f.read()
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for process in self.preprocess_func:
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x = process(x)
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batch_data.append(x)
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image_file_list.append(image_file)
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if len(batch_data) >= batch_size or idx == len(image_list) - 1:
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batch_tensor = paddle.to_tensor(batch_data)
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if self.amp and self.amp_eval:
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with paddle.amp.auto_cast(
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custom_black_list={
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"flatten_contiguous_range", "greater_than"
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},
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level=self.amp_level):
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out = self.model(batch_tensor)
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else:
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out = self.model(batch_tensor)
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if isinstance(out, list):
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out = out[0]
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if isinstance(out, dict) and "Student" in out:
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out = out["Student"]
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if isinstance(out, dict) and "logits" in out:
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out = out["logits"]
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if isinstance(out, dict) and "output" in out:
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out = out["output"]
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result = self.postprocess_func(out, image_file_list)
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print(result)
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batch_data.clear()
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image_file_list.clear()
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def export(self):
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assert self.mode == "export"
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use_multilabel = self.config["Global"].get(
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"use_multilabel",
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False) or "ATTRMetric" in self.config["Metric"]["Eval"][0]
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model = ExportModel(self.config["Arch"], self.model, use_multilabel)
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if self.config["Global"]["pretrained_model"] is not None:
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if self.config["Global"]["pretrained_model"].startswith("http"):
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load_dygraph_pretrain_from_url(
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model.base_model,
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self.config["Global"]["pretrained_model"])
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else:
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load_dygraph_pretrain(
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model.base_model,
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self.config["Global"]["pretrained_model"])
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model.eval()
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# for re-parameterization nets
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for layer in self.model.sublayers():
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if hasattr(layer, "re_parameterize") and not getattr(layer,
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"is_repped"):
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layer.re_parameterize()
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save_path = os.path.join(self.config["Global"]["save_inference_dir"],
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"inference")
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model = paddle.jit.to_static(
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model,
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input_spec=[
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paddle.static.InputSpec(
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shape=[None] + self.config["Global"]["image_shape"],
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dtype='float32')
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])
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if hasattr(model.base_model,
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"quanter") and model.base_model.quanter is not None:
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model.base_model.quanter.save_quantized_model(model,
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save_path + "_int8")
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else:
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paddle.jit.save(model, save_path)
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logger.info(
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f"Export succeeded! The inference model exported has been saved in \"{self.config['Global']['save_inference_dir']}\"."
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)
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def _init_vdl(self):
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if self.config['Global'][
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'use_visualdl'] and mode == "train" and dist.get_rank() == 0:
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vdl_writer_path = os.path.join(self.output_dir, "vdl")
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if not os.path.exists(vdl_writer_path):
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os.makedirs(vdl_writer_path)
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return LogWriter(logdir=vdl_writer_path)
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return None
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def _init_seed(self):
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seed = self.config["Global"].get("seed", False)
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if dist.get_world_size() != 1:
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# if self.config["Global"]["distributed"]:
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# set different seed in different GPU manually in distributed environment
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if not seed:
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logger.warning(
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"The random seed cannot be None in a distributed environment. Global.seed has been set to 42 by default"
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)
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self.config["Global"]["seed"] = seed = 42
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logger.info(
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f"Set random seed to ({int(seed)} + $PADDLE_TRAINER_ID) for different trainer"
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)
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dist_seed = int(seed) + dist.get_rank()
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paddle.seed(dist_seed)
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np.random.seed(dist_seed)
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random.seed(dist_seed)
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elif seed or seed == 0:
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assert isinstance(seed, int), "The 'seed' must be a integer!"
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paddle.seed(seed)
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np.random.seed(seed)
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random.seed(seed)
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def _init_device(self):
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device = self.config["Global"]["device"]
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assert device in ["cpu", "gpu", "xpu", "npu", "mlu", "ascend"]
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logger.info('train with paddle {} and device {}'.format(
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paddle.__version__, device))
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return paddle.set_device(device)
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def _init_pretrained(self):
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if self.config["Global"]["pretrained_model"] is not None:
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if self.config["Global"]["pretrained_model"].startswith("http"):
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load_dygraph_pretrain_from_url(
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[self.model, getattr(self, 'train_loss_func', None)],
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self.config["Global"]["pretrained_model"])
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else:
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load_dygraph_pretrain(
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[self.model, getattr(self, 'train_loss_func', None)],
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self.config["Global"]["pretrained_model"])
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def _init_amp(self):
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self.amp = "AMP" in self.config and self.config["AMP"] is not None
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self.amp_eval = False
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# for amp
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if self.amp:
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AMP_RELATED_FLAGS_SETTING = {'FLAGS_max_inplace_grad_add': 8, }
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if paddle.is_compiled_with_cuda():
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AMP_RELATED_FLAGS_SETTING.update({
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'FLAGS_cudnn_batchnorm_spatial_persistent': 1
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})
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paddle.set_flags(AMP_RELATED_FLAGS_SETTING)
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self.scale_loss = self.config["AMP"].get("scale_loss", 1.0)
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self.use_dynamic_loss_scaling = self.config["AMP"].get(
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"use_dynamic_loss_scaling", False)
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self.scaler = paddle.amp.GradScaler(
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init_loss_scaling=self.scale_loss,
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use_dynamic_loss_scaling=self.use_dynamic_loss_scaling)
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self.amp_level = self.config['AMP'].get("level", "O1")
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if self.amp_level not in ["O1", "O2"]:
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msg = "[Parameter Error]: The optimize level of AMP only support 'O1' and 'O2'. The level has been set 'O1'."
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logger.warning(msg)
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self.config['AMP']["level"] = "O1"
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self.amp_level = "O1"
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self.amp_eval = self.config["AMP"].get("use_fp16_test", False)
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# TODO(gaotingquan): Paddle not yet support FP32 evaluation when training with AMPO2
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if self.mode == "train" and self.config["Global"].get(
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"eval_during_train",
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True) and self.amp_level == "O2" and self.amp_eval == False:
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msg = "PaddlePaddle only support FP16 evaluation when training with AMP O2 now. "
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logger.warning(msg)
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self.config["AMP"]["use_fp16_test"] = True
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self.amp_eval = True
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# TODO(gaotingquan): to compatible with different versions of Paddle
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paddle_version = paddle.__version__[:3]
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# paddle version < 2.3.0 and not develop
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if paddle_version not in ["2.3", "0.0"]:
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if self.mode == "train":
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self.model, self.optimizer = paddle.amp.decorate(
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models=self.model,
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optimizers=self.optimizer,
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level=self.amp_level,
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save_dtype='float32')
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elif self.amp_eval:
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if self.amp_level == "O2":
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msg = "The PaddlePaddle that installed not support FP16 evaluation in AMP O2. Please use PaddlePaddle version >= 2.3.0. Use FP32 evaluation instead and please notice the Eval Dataset output_fp16 should be 'False'."
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logger.warning(msg)
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self.amp_eval = False
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else:
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self.model, self.optimizer = paddle.amp.decorate(
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models=self.model,
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level=self.amp_level,
|
|
save_dtype='float32')
|
|
# paddle version >= 2.3.0 or develop
|
|
else:
|
|
if self.mode == "train" or self.amp_eval:
|
|
self.model = paddle.amp.decorate(
|
|
models=self.model,
|
|
level=self.amp_level,
|
|
save_dtype='float32')
|
|
|
|
if self.mode == "train" and len(self.train_loss_func.parameters(
|
|
)) > 0:
|
|
self.train_loss_func = paddle.amp.decorate(
|
|
models=self.train_loss_func,
|
|
level=self.amp_level,
|
|
save_dtype='float32')
|
|
|
|
def _init_dist(self):
|
|
# check the gpu num
|
|
world_size = dist.get_world_size()
|
|
self.config["Global"]["distributed"] = world_size != 1
|
|
# TODO(gaotingquan):
|
|
if self.mode == "train":
|
|
std_gpu_num = 8 if isinstance(
|
|
self.config["Optimizer"],
|
|
dict) and self.config["Optimizer"]["name"] == "AdamW" else 4
|
|
if world_size != std_gpu_num:
|
|
msg = f"The training strategy provided by PaddleClas is based on {std_gpu_num} gpus. But the number of gpu is {world_size} in current training. Please modify the stategy (learning rate, batch size and so on) if use this config to train."
|
|
logger.warning(msg)
|
|
|
|
if self.config["Global"]["distributed"]:
|
|
dist.init_parallel_env()
|
|
self.model = paddle.DataParallel(self.model)
|
|
if self.mode == 'train' and len(self.train_loss_func.parameters(
|
|
)) > 0:
|
|
self.train_loss_func = paddle.DataParallel(
|
|
self.train_loss_func)
|
|
|
|
|
|
class ExportModel(TheseusLayer):
|
|
"""
|
|
ExportModel: add softmax onto the model
|
|
"""
|
|
|
|
def __init__(self, config, model, use_multilabel):
|
|
super().__init__()
|
|
self.base_model = model
|
|
# we should choose a final model to export
|
|
if isinstance(self.base_model, DistillationModel):
|
|
self.infer_model_name = config["infer_model_name"]
|
|
else:
|
|
self.infer_model_name = None
|
|
|
|
self.infer_output_key = config.get("infer_output_key", None)
|
|
if self.infer_output_key == "features" and isinstance(self.base_model,
|
|
RecModel):
|
|
self.base_model.head = IdentityHead()
|
|
if use_multilabel:
|
|
self.out_act = nn.Sigmoid()
|
|
else:
|
|
if config.get("infer_add_softmax", True):
|
|
self.out_act = nn.Softmax(axis=-1)
|
|
else:
|
|
self.out_act = None
|
|
|
|
def eval(self):
|
|
self.training = False
|
|
for layer in self.sublayers():
|
|
layer.training = False
|
|
layer.eval()
|
|
|
|
def forward(self, x):
|
|
x = self.base_model(x)
|
|
if isinstance(x, list):
|
|
x = x[0]
|
|
if self.infer_model_name is not None:
|
|
x = x[self.infer_model_name]
|
|
if self.infer_output_key is not None:
|
|
x = x[self.infer_output_key]
|
|
if self.out_act is not None:
|
|
if isinstance(x, dict):
|
|
x = x["logits"]
|
|
x = self.out_act(x)
|
|
return x
|