refactor trainer
dongshuilong
parent
765f15bc60
commit
ebde0e13cb
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# 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 sys
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import numpy as np
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__dir__ = os.path.dirname(os.path.abspath(__file__))
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sys.path.append(os.path.abspath(os.path.join(__dir__, '../../')))
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import time
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import platform
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import datetime
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import argparse
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import paddle
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import paddle.nn as nn
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import paddle.distributed as dist
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from visualdl import LogWriter
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from ppcls.utils.check import check_gpu
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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
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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.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.train import classification_train, retrieval_train
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from ppcls.engine.eval import classification_eval, retrieval_eval
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from ppcls.arch.gears.identity_head import IdentityHead
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class Core(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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self.eval_mode = self.config["Global"].get("eval_mode",
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"classification")
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# init logger
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self.output_dir = self.config['Global']['output_dir']
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log_file = os.path.join(self.output_dir, self.config["Arch"]["name"],
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f"{mode}.log")
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init_logger(name='root', log_file=log_file)
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print_config(config)
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# init train_func and eval_func
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if self.eval_mode == "classification":
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self.evaler = classification_eval
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self.trainer = classification_train
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elif self.eval_mode == "retrieval":
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self.trainer = retrieval_train
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self.evaler = retrieval_eval
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else:
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logger.warning("Invalid eval mode: {}".format(self.eval_mode))
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self.use_dali = self.config['Global'].get("use_dali", False)
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# for visualdl
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self.vdl_writer = None
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if self.config['Global']['use_visualdl'] and mode == "train":
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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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self.vdl_writer = LogWriter(logdir=vdl_writer_path)
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# set device
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assert self.config["Global"]["device"] in ["cpu", "gpu", "xpu"]
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self.device = paddle.set_device(self.config["Global"]["device"])
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logger.info('train with paddle {} and device {}'.format(
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paddle.__version__, self.device))
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# AMP training
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self.amp = True if "AMP" in self.config else False
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if self.amp and self.config["AMP"] is not None:
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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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else:
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self.scale_loss = 1.0
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self.use_dynamic_loss_scaling = False
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if self.amp:
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AMP_RELATED_FLAGS_SETTING = {
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'FLAGS_cudnn_batchnorm_spatial_persistent': 1,
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'FLAGS_max_inplace_grad_add': 8,
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}
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paddle.fluid.set_flags(AMP_RELATED_FLAGS_SETTING)
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# build dataloader
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if self.mode == 'train':
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self.train_dataloader = build_dataloader(
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self.config["DataLoader"], "Train", self.device, self.use_dali)
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if self.mode in ["train", "eval"]:
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if self.eval_mode == "classification":
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self.eval_dataloader = build_dataloader(
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self.config["DataLoader"], "Eval", self.device,
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self.use_dali)
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elif self.eval_mode == "retrieval":
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self.gallery_dataloader = build_dataloader(
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self.config["DataLoader"]["Eval"], "Gallery", self.device,
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self.use_dali)
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self.query_dataloader = build_dataloader(
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self.config["DataLoader"]["Eval"], "Query", self.device,
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self.use_dali)
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# build loss
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if self.mode == "train":
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loss_info = self.config["Loss"]["Train"]
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self.train_loss_func = build_loss(loss_info)
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if self.mode in ["train", "eval"]:
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loss_config = self.config.get("Loss", None)
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if loss_config is not None:
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loss_config = loss_config.get("Eval")
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if loss_config is not None:
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self.eval_loss_func = build_loss(loss_config)
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else:
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self.eval_loss_func = None
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else:
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self.eval_loss_func = None
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# build metric
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if self.mode == 'train':
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metric_config = self.config.get("Metric")
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if metric_config is not None:
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metric_config = metric_config.get("Train")
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if metric_config is not None:
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self.train_metric_func = build_metrics(metric_config)
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else:
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self.train_metric_func = None
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else:
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self.train_metric_func = None
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if self.mode in ["train", "eval"]:
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metric_config = self.config.get("Metric")
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if self.eval_mode == "classification":
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if metric_config is not None:
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metric_config = metric_config.get("Eval")
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if metric_config is not None:
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self.eval_metric_func = build_metrics(metric_config)
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elif self.eval_mode == "retrieval":
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if metric_config is None:
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metric_config = [{"name": "Recallk", "topk": (1, 5)}]
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else:
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metric_config = metric_config["Eval"]
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self.eval_metric_func = build_metrics(metric_config)
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else:
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self.eval_metric_func = None
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# build model
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self.model = build_model(self.config["Arch"])
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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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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, self.config["Global"]["pretrained_model"])
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else:
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load_dygraph_pretrain(
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self.model, self.config["Global"]["pretrained_model"])
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# for slim
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# build optimizer
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if self.mode == 'train':
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self.optimizer, self.lr_sch = build_optimizer(
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self.config["Optimizer"], self.config["Global"]["epochs"],
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len(self.train_dataloader), self.model.parameters())
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# for distributed
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self.config["Global"][
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"distributed"] = paddle.distributed.get_world_size() != 1
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if self.config["Global"]["distributed"]:
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dist.init_parallel_env()
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if self.config["Global"]["distributed"]:
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self.model = paddle.DataParallel(self.model)
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# build postprocess for infer
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if self.mode == 'infer':
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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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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": 0.0,
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"epoch": 0,
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}
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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)
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if metric_info is not None:
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best_metric.update(metric_info)
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# for amp training
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if self.amp:
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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.max_iter = len(self.train_dataloader) - 1 if platform.system(
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) == "Windows" else len(self.train_dataloader)
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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.trainer(self, epoch_id, print_batch_step)
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if self.use_dali:
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self.train_dataloader.reset()
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metric_msg = ", ".join([
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"{}: {:.5f}".format(key, self.output_info[key].avg)
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for key in self.output_info
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])
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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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if self.config["Global"][
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"eval_during_train"] and epoch_id % self.config["Global"][
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"eval_interval"] == 0:
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acc = self.eval(epoch_id)
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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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model_name=self.config["Arch"]["name"],
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prefix="best_model")
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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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# save model
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if 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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model_name=self.config["Arch"]["name"],
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prefix="epoch_{}".format(epoch_id))
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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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model_name=self.config["Arch"]["name"],
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prefix="latest")
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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.evaler(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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total_trainer = paddle.distributed.get_world_size()
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local_rank = paddle.distributed.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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out = self.model(batch_tensor)
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if isinstance(out, list):
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out = out[0]
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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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model = ExportModel(self.config["Arch"], self.model)
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if self.config["Global"]["pretrained_model"] is not None:
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load_dygraph_pretrain(model.base_model,
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self.config["Global"]["pretrained_model"])
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model.eval()
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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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paddle.jit.save(
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model,
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os.path.join(self.config["Global"]["save_inference_dir"],
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"inference"))
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class ExportModel(nn.Layer):
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"""
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ExportModel: add softmax onto the model
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"""
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def __init__(self, config, model):
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super().__init__()
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self.base_model = model
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# we should choose a final model to export
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if isinstance(self.base_model, DistillationModel):
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self.infer_model_name = config["infer_model_name"]
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else:
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self.infer_model_name = None
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self.infer_output_key = config.get("infer_output_key", None)
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if self.infer_output_key == "features" and isinstance(self.base_model,
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RecModel):
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self.base_model.head = IdentityHead()
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if config.get("infer_add_softmax", True):
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self.softmax = nn.Softmax(axis=-1)
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else:
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self.softmax = None
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def eval(self):
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self.training = False
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for layer in self.sublayers():
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layer.training = False
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layer.eval()
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def forward(self, x):
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x = self.base_model(x)
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if isinstance(x, list):
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x = x[0]
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if self.infer_model_name is not None:
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x = x[self.infer_model_name]
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if self.infer_output_key is not None:
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x = x[self.infer_output_key]
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if self.softmax is not None:
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x = self.softmax(x)
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return x
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@ -0,0 +1,16 @@
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# 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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#
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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 ppcls.engine.eval.classification import classification_eval
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from ppcls.engine.eval.retrieval import retrieval_eval
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@ -0,0 +1,118 @@
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# 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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#
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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 sys
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import time
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import platform
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import paddle
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__dir__ = os.path.dirname(os.path.abspath(__file__))
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sys.path.append(os.path.abspath(os.path.join(__dir__, '../../../')))
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from ppcls.utils.misc import AverageMeter
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from ppcls.utils import logger
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def classification_eval(evaler, epoch_id=0):
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output_info = dict()
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time_info = {
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"batch_cost": AverageMeter(
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"batch_cost", '.5f', postfix=" s,"),
|
||||
"reader_cost": AverageMeter(
|
||||
"reader_cost", ".5f", postfix=" s,"),
|
||||
}
|
||||
print_batch_step = evaler.config["Global"]["print_batch_step"]
|
||||
|
||||
metric_key = None
|
||||
tic = time.time()
|
||||
eval_dataloader = evaler.eval_dataloader if evaler.use_dali else evaler.eval_dataloader(
|
||||
)
|
||||
max_iter = len(evaler.eval_dataloader) - 1 if platform.system(
|
||||
) == "Windows" else len(evaler.eval_dataloader)
|
||||
for iter_id, batch in enumerate(eval_dataloader):
|
||||
if iter_id >= max_iter:
|
||||
break
|
||||
if iter_id == 5:
|
||||
for key in time_info:
|
||||
time_info[key].reset()
|
||||
if evaler.use_dali:
|
||||
batch = [
|
||||
paddle.to_tensor(batch[0]['data']),
|
||||
paddle.to_tensor(batch[0]['label'])
|
||||
]
|
||||
time_info["reader_cost"].update(time.time() - tic)
|
||||
batch_size = batch[0].shape[0]
|
||||
batch[0] = paddle.to_tensor(batch[0]).astype("float32")
|
||||
batch[1] = batch[1].reshape([-1, 1]).astype("int64")
|
||||
# image input
|
||||
out = evaler.model(batch[0])
|
||||
# calc loss
|
||||
if evaler.eval_loss_func is not None:
|
||||
loss_dict = evaler.eval_loss_func(out, batch[-1])
|
||||
for key in loss_dict:
|
||||
if key not in output_info:
|
||||
output_info[key] = AverageMeter(key, '7.5f')
|
||||
output_info[key].update(loss_dict[key].numpy()[0], batch_size)
|
||||
# calc metric
|
||||
if evaler.eval_metric_func is not None:
|
||||
metric_dict = evaler.eval_metric_func(out, batch[-1])
|
||||
if paddle.distributed.get_world_size() > 1:
|
||||
for key in metric_dict:
|
||||
paddle.distributed.all_reduce(
|
||||
metric_dict[key], op=paddle.distributed.ReduceOp.SUM)
|
||||
metric_dict[key] = metric_dict[
|
||||
key] / paddle.distributed.get_world_size()
|
||||
for key in metric_dict:
|
||||
if metric_key is None:
|
||||
metric_key = key
|
||||
if key not in output_info:
|
||||
output_info[key] = AverageMeter(key, '7.5f')
|
||||
|
||||
output_info[key].update(metric_dict[key].numpy()[0],
|
||||
batch_size)
|
||||
|
||||
time_info["batch_cost"].update(time.time() - tic)
|
||||
|
||||
if iter_id % print_batch_step == 0:
|
||||
time_msg = "s, ".join([
|
||||
"{}: {:.5f}".format(key, time_info[key].avg)
|
||||
for key in time_info
|
||||
])
|
||||
|
||||
ips_msg = "ips: {:.5f} images/sec".format(
|
||||
batch_size / time_info["batch_cost"].avg)
|
||||
|
||||
metric_msg = ", ".join([
|
||||
"{}: {:.5f}".format(key, output_info[key].val)
|
||||
for key in output_info
|
||||
])
|
||||
logger.info("[Eval][Epoch {}][Iter: {}/{}]{}, {}, {}".format(
|
||||
epoch_id, iter_id,
|
||||
len(evaler.eval_dataloader), metric_msg, time_msg, ips_msg))
|
||||
|
||||
tic = time.time()
|
||||
if evaler.use_dali:
|
||||
evaler.eval_dataloader.reset()
|
||||
metric_msg = ", ".join([
|
||||
"{}: {:.5f}".format(key, output_info[key].avg) for key in output_info
|
||||
])
|
||||
logger.info("[Eval][Epoch {}][Avg]{}".format(epoch_id, metric_msg))
|
||||
|
||||
# do not try to save best eval.model
|
||||
if evaler.eval_metric_func is None:
|
||||
return -1
|
||||
# return 1st metric in the dict
|
||||
return output_info[metric_key].avg
|
||||
|
|
@ -0,0 +1,160 @@
|
|||
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
from __future__ import absolute_import
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
import os
|
||||
import sys
|
||||
import time
|
||||
import platform
|
||||
import paddle
|
||||
|
||||
__dir__ = os.path.dirname(os.path.abspath(__file__))
|
||||
sys.path.append(os.path.abspath(os.path.join(__dir__, '../../../')))
|
||||
from ppcls.utils.misc import AverageMeter
|
||||
from ppcls.utils import logger
|
||||
|
||||
|
||||
def retrieval_eval(evaler, epoch_id=0):
|
||||
evaler.model.eval()
|
||||
# step1. build gallery
|
||||
gallery_feas, gallery_img_id, gallery_unique_id = cal_feature(
|
||||
evaler, name='gallery')
|
||||
query_feas, query_img_id, query_query_id = cal_feature(
|
||||
evaler, name='query')
|
||||
|
||||
# step2. do evaluation
|
||||
sim_block_size = evaler.config["Global"].get("sim_block_size", 64)
|
||||
sections = [sim_block_size] * (len(query_feas) // sim_block_size)
|
||||
if len(query_feas) % sim_block_size:
|
||||
sections.append(len(query_feas) % sim_block_size)
|
||||
fea_blocks = paddle.split(query_feas, num_or_sections=sections)
|
||||
if query_query_id is not None:
|
||||
query_id_blocks = paddle.split(
|
||||
query_query_id, num_or_sections=sections)
|
||||
image_id_blocks = paddle.split(query_img_id, num_or_sections=sections)
|
||||
metric_key = None
|
||||
|
||||
if evaler.eval_loss_func is None:
|
||||
metric_dict = {metric_key: 0.}
|
||||
else:
|
||||
metric_dict = dict()
|
||||
for block_idx, block_fea in enumerate(fea_blocks):
|
||||
similarity_matrix = paddle.matmul(
|
||||
block_fea, gallery_feas, transpose_y=True)
|
||||
if query_query_id is not None:
|
||||
query_id_block = query_id_blocks[block_idx]
|
||||
query_id_mask = (query_id_block != gallery_unique_id.t())
|
||||
|
||||
image_id_block = image_id_blocks[block_idx]
|
||||
image_id_mask = (image_id_block != gallery_img_id.t())
|
||||
|
||||
keep_mask = paddle.logical_or(query_id_mask, image_id_mask)
|
||||
similarity_matrix = similarity_matrix * keep_mask.astype(
|
||||
"float32")
|
||||
else:
|
||||
keep_mask = None
|
||||
|
||||
metric_tmp = evaler.eval_metric_func(similarity_matrix,
|
||||
image_id_blocks[block_idx],
|
||||
gallery_img_id, keep_mask)
|
||||
|
||||
for key in metric_tmp:
|
||||
if key not in metric_dict:
|
||||
metric_dict[key] = metric_tmp[key] * block_fea.shape[
|
||||
0] / len(query_feas)
|
||||
else:
|
||||
metric_dict[key] += metric_tmp[key] * block_fea.shape[
|
||||
0] / len(query_feas)
|
||||
|
||||
metric_info_list = []
|
||||
for key in metric_dict:
|
||||
if metric_key is None:
|
||||
metric_key = key
|
||||
metric_info_list.append("{}: {:.5f}".format(key, metric_dict[key]))
|
||||
metric_msg = ", ".join(metric_info_list)
|
||||
logger.info("[Eval][Epoch {}][Avg]{}".format(epoch_id, metric_msg))
|
||||
|
||||
return metric_dict[metric_key]
|
||||
|
||||
|
||||
def cal_feature(evaler, name='gallery'):
|
||||
all_feas = None
|
||||
all_image_id = None
|
||||
all_unique_id = None
|
||||
has_unique_id = False
|
||||
|
||||
if name == 'gallery':
|
||||
dataloader = evaler.gallery_dataloader
|
||||
elif name == 'query':
|
||||
dataloader = evaler.query_dataloader
|
||||
else:
|
||||
raise RuntimeError("Only support gallery or query dataset")
|
||||
|
||||
max_iter = len(dataloader) - 1 if platform.system() == "Windows" else len(
|
||||
dataloader)
|
||||
dataloader_tmp = dataloader if evaler.use_dali else dataloader()
|
||||
for idx, batch in enumerate(dataloader_tmp): # load is very time-consuming
|
||||
if idx >= max_iter:
|
||||
break
|
||||
if idx % evaler.config["Global"]["print_batch_step"] == 0:
|
||||
logger.info(
|
||||
f"{name} feature calculation process: [{idx}/{len(dataloader)}]"
|
||||
)
|
||||
if evaler.use_dali:
|
||||
batch = [
|
||||
paddle.to_tensor(batch[0]['data']),
|
||||
paddle.to_tensor(batch[0]['label'])
|
||||
]
|
||||
batch = [paddle.to_tensor(x) for x in batch]
|
||||
batch[1] = batch[1].reshape([-1, 1]).astype("int64")
|
||||
if len(batch) == 3:
|
||||
has_unique_id = True
|
||||
batch[2] = batch[2].reshape([-1, 1]).astype("int64")
|
||||
out = evaler.model(batch[0], batch[1])
|
||||
batch_feas = out["features"]
|
||||
|
||||
# do norm
|
||||
if evaler.config["Global"].get("feature_normalize", True):
|
||||
feas_norm = paddle.sqrt(
|
||||
paddle.sum(paddle.square(batch_feas), axis=1, keepdim=True))
|
||||
batch_feas = paddle.divide(batch_feas, feas_norm)
|
||||
|
||||
if all_feas is None:
|
||||
all_feas = batch_feas
|
||||
if has_unique_id:
|
||||
all_unique_id = batch[2]
|
||||
all_image_id = batch[1]
|
||||
else:
|
||||
all_feas = paddle.concat([all_feas, batch_feas])
|
||||
all_image_id = paddle.concat([all_image_id, batch[1]])
|
||||
if has_unique_id:
|
||||
all_unique_id = paddle.concat([all_unique_id, batch[2]])
|
||||
if evaler.use_dali:
|
||||
dataloader_tmp.reset()
|
||||
if paddle.distributed.get_world_size() > 1:
|
||||
feat_list = []
|
||||
img_id_list = []
|
||||
unique_id_list = []
|
||||
paddle.distributed.all_gather(feat_list, all_feas)
|
||||
paddle.distributed.all_gather(img_id_list, all_image_id)
|
||||
all_feas = paddle.concat(feat_list, axis=0)
|
||||
all_image_id = paddle.concat(img_id_list, axis=0)
|
||||
if has_unique_id:
|
||||
paddle.distributed.all_gather(unique_id_list, all_unique_id)
|
||||
all_unique_id = paddle.concat(unique_id_list, axis=0)
|
||||
|
||||
logger.info("Build {} done, all feat shape: {}, begin to eval..".format(
|
||||
name, all_feas.shape))
|
||||
return all_feas, all_image_id, all_unique_id
|
||||
|
|
@ -0,0 +1,15 @@
|
|||
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
from ppcls.engine.train.classification import classification_train
|
||||
from ppcls.engine.train.retrieval import retrieval_train
|
||||
|
|
@ -0,0 +1,89 @@
|
|||
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
from __future__ import absolute_import, division, print_function
|
||||
|
||||
import datetime
|
||||
import os
|
||||
import platform
|
||||
import sys
|
||||
import time
|
||||
|
||||
import numpy as np
|
||||
import paddle
|
||||
|
||||
__dir__ = os.path.dirname(os.path.abspath(__file__))
|
||||
sys.path.append(os.path.abspath(os.path.join(__dir__, '../../../')))
|
||||
from ppcls.utils import logger
|
||||
from ppcls.utils.misc import AverageMeter
|
||||
from ppcls.engine.train.utils import update_loss, update_metric, log_info
|
||||
|
||||
|
||||
def classification_train(trainer, epoch_id, print_batch_step):
|
||||
tic = time.time()
|
||||
|
||||
train_dataloader = trainer.train_dataloader if trainer.use_dali else trainer.train_dataloader(
|
||||
)
|
||||
for iter_id, batch in enumerate(train_dataloader):
|
||||
if iter_id >= trainer.max_iter:
|
||||
break
|
||||
if iter_id == 5:
|
||||
for key in trainer.time_info:
|
||||
trainer.time_info[key].reset()
|
||||
trainer.time_info["reader_cost"].update(time.time() - tic)
|
||||
if trainer.use_dali:
|
||||
batch = [
|
||||
paddle.to_tensor(batch[0]['data']),
|
||||
paddle.to_tensor(batch[0]['label'])
|
||||
]
|
||||
batch_size = batch[0].shape[0]
|
||||
batch[1] = batch[1].reshape([-1, 1]).astype("int64")
|
||||
|
||||
trainer.global_step += 1
|
||||
# image input
|
||||
if trainer.amp:
|
||||
with paddle.amp.auto_cast(custom_black_list={
|
||||
"flatten_contiguous_range", "greater_than"
|
||||
}):
|
||||
out = trainer.model(batch[0])
|
||||
loss_dict = trainer.train_loss_func(out, batch[1])
|
||||
else:
|
||||
out = trainer.model(batch[0])
|
||||
|
||||
# calc loss
|
||||
if trainer.config["DataLoader"]["Train"]["dataset"].get(
|
||||
"batch_transform_ops", None):
|
||||
loss_dict = trainer.train_loss_func(out, batch[1:])
|
||||
else:
|
||||
loss_dict = trainer.train_loss_func(out, batch[1])
|
||||
|
||||
# step opt and lr
|
||||
if trainer.amp:
|
||||
scaled = trainer.scaler.scale(loss_dict["loss"])
|
||||
scaled.backward()
|
||||
trainer.scaler.minimize(trainer.optimizer, scaled)
|
||||
else:
|
||||
loss_dict["loss"].backward()
|
||||
trainer.optimizer.step()
|
||||
trainer.optimizer.clear_grad()
|
||||
trainer.lr_sch.step()
|
||||
|
||||
# below code just for logging
|
||||
# update metric_for_logger
|
||||
update_metric(trainer, out, batch, batch_size)
|
||||
# update_loss_for_logger
|
||||
update_loss(trainer, loss_dict, batch_size)
|
||||
trainer.time_info["batch_cost"].update(time.time() - tic)
|
||||
if iter_id % print_batch_step == 0:
|
||||
log_info(trainer, batch_size, epoch_id, iter_id)
|
||||
tic = time.time()
|
||||
|
|
@ -0,0 +1,90 @@
|
|||
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
from __future__ import absolute_import, division, print_function
|
||||
|
||||
import datetime
|
||||
import os
|
||||
import platform
|
||||
import sys
|
||||
import time
|
||||
|
||||
import numpy as np
|
||||
import paddle
|
||||
|
||||
__dir__ = os.path.dirname(os.path.abspath(__file__))
|
||||
sys.path.append(os.path.abspath(os.path.join(__dir__, '../../../')))
|
||||
from ppcls.utils import logger
|
||||
from ppcls.utils.misc import AverageMeter
|
||||
from ppcls.engine.train.utils import update_loss, update_metric, log_info
|
||||
|
||||
|
||||
def retrieval_train(trainer, epoch_id, print_batch_step):
|
||||
tic = time.time()
|
||||
|
||||
train_dataloader = trainer.train_dataloader if trainer.use_dali else trainer.train_dataloader(
|
||||
)
|
||||
for iter_id, batch in enumerate(train_dataloader):
|
||||
if iter_id >= trainer.max_iter:
|
||||
break
|
||||
if iter_id == 5:
|
||||
for key in trainer.time_info:
|
||||
trainer.time_info[key].reset()
|
||||
trainer.time_info["reader_cost"].update(time.time() - tic)
|
||||
if trainer.use_dali:
|
||||
batch = [
|
||||
paddle.to_tensor(batch[0]['data']),
|
||||
paddle.to_tensor(batch[0]['label'])
|
||||
]
|
||||
batch_size = batch[0].shape[0]
|
||||
batch[1] = batch[1].reshape([-1, 1]).astype("int64")
|
||||
|
||||
trainer.global_step += 1
|
||||
# image input
|
||||
if trainer.amp:
|
||||
with paddle.amp.auto_cast(custom_black_list={
|
||||
"flatten_contiguous_range", "greater_than"
|
||||
}):
|
||||
out = trainer.model(batch[0], batch[1])
|
||||
loss_dict = trainer.train_loss_func(out, batch[1])
|
||||
else:
|
||||
out = trainer.model(batch[0], batch[1])
|
||||
|
||||
# calc loss
|
||||
if trainer.config["DataLoader"]["Train"]["dataset"].get(
|
||||
"batch_transform_ops", None):
|
||||
loss_dict = trainer.train_loss_func(out, batch[1:])
|
||||
else:
|
||||
loss_dict = trainer.train_loss_func(out, batch[1])
|
||||
|
||||
# step opt and lr
|
||||
if trainer.amp:
|
||||
scaled = trainer.scaler.scale(loss_dict["loss"])
|
||||
scaled.backward()
|
||||
trainer.scaler.minimize(trainer.optimizer, scaled)
|
||||
else:
|
||||
loss_dict["loss"].backward()
|
||||
trainer.optimizer.step()
|
||||
trainer.optimizer.clear_grad()
|
||||
trainer.lr_sch.step()
|
||||
|
||||
# below code just for logging
|
||||
# update metric_for_logger
|
||||
update_metric(trainer, out, batch, batch_size)
|
||||
|
||||
# update_loss_for_logger
|
||||
update_loss(trainer, loss_dict, batch_size)
|
||||
trainer.time_info["batch_cost"].update(time.time() - tic)
|
||||
if iter_id % print_batch_step == 0:
|
||||
log_info(trainer, batch_size, epoch_id, iter_id)
|
||||
tic = time.time()
|
||||
|
|
@ -0,0 +1,82 @@
|
|||
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
from __future__ import absolute_import, division, print_function
|
||||
|
||||
import datetime
|
||||
import os
|
||||
import platform
|
||||
import sys
|
||||
import time
|
||||
|
||||
import numpy as np
|
||||
import paddle
|
||||
|
||||
__dir__ = os.path.dirname(os.path.abspath(__file__))
|
||||
sys.path.append(os.path.abspath(os.path.join(__dir__, '../../../')))
|
||||
from ppcls.utils import logger
|
||||
from ppcls.utils.misc import AverageMeter
|
||||
|
||||
|
||||
def update_metric(trainer, out, batch, batch_size):
|
||||
# calc metric
|
||||
if trainer.train_metric_func is not None:
|
||||
metric_dict = trainer.train_metric_func(out, batch[-1])
|
||||
for key in metric_dict:
|
||||
if key not in trainer.output_info:
|
||||
trainer.output_info[key] = AverageMeter(key, '7.5f')
|
||||
trainer.output_info[key].update(metric_dict[key].numpy()[0],
|
||||
batch_size)
|
||||
|
||||
|
||||
def update_loss(trainer, loss_dict, batch_size):
|
||||
# update_output_info
|
||||
for key in loss_dict:
|
||||
if key not in trainer.output_info:
|
||||
trainer.output_info[key] = AverageMeter(key, '7.5f')
|
||||
trainer.output_info[key].update(loss_dict[key].numpy()[0], batch_size)
|
||||
|
||||
|
||||
def log_info(trainer, batch_size, epoch_id, iter_id):
|
||||
lr_msg = "lr: {:.5f}".format(trainer.lr_sch.get_lr())
|
||||
metric_msg = ", ".join([
|
||||
"{}: {:.5f}".format(key, trainer.output_info[key].avg)
|
||||
for key in trainer.output_info
|
||||
])
|
||||
time_msg = "s, ".join([
|
||||
"{}: {:.5f}".format(key, trainer.time_info[key].avg)
|
||||
for key in trainer.time_info
|
||||
])
|
||||
|
||||
ips_msg = "ips: {:.5f} images/sec".format(
|
||||
batch_size / trainer.time_info["batch_cost"].avg)
|
||||
eta_sec = ((trainer.config["Global"]["epochs"] - epoch_id + 1
|
||||
) * len(trainer.train_dataloader) - iter_id
|
||||
) * trainer.time_info["batch_cost"].avg
|
||||
eta_msg = "eta: {:s}".format(str(datetime.timedelta(seconds=int(eta_sec))))
|
||||
logger.info("[Train][Epoch {}/{}][Iter: {}/{}]{}, {}, {}, {}, {}".format(
|
||||
epoch_id, trainer.config["Global"]["epochs"], iter_id,
|
||||
len(trainer.train_dataloader), lr_msg, metric_msg, time_msg, ips_msg,
|
||||
eta_msg))
|
||||
|
||||
logger.scaler(
|
||||
name="lr",
|
||||
value=trainer.lr_sch.get_lr(),
|
||||
step=trainer.global_step,
|
||||
writer=trainer.vdl_writer)
|
||||
for key in trainer.output_info:
|
||||
logger.scaler(
|
||||
name="train_{}".format(key),
|
||||
value=trainer.output_info[key].avg,
|
||||
step=trainer.global_step,
|
||||
writer=trainer.vdl_writer)
|
||||
|
|
@ -1,662 +0,0 @@
|
|||
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
from __future__ import absolute_import
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
import os
|
||||
import sys
|
||||
import numpy as np
|
||||
|
||||
__dir__ = os.path.dirname(os.path.abspath(__file__))
|
||||
sys.path.append(os.path.abspath(os.path.join(__dir__, '../../')))
|
||||
|
||||
import time
|
||||
import platform
|
||||
import datetime
|
||||
import argparse
|
||||
import paddle
|
||||
import paddle.nn as nn
|
||||
import paddle.distributed as dist
|
||||
from visualdl import LogWriter
|
||||
|
||||
from ppcls.utils.check import check_gpu
|
||||
from ppcls.utils.misc import AverageMeter
|
||||
from ppcls.utils import logger
|
||||
from ppcls.utils.logger import init_logger
|
||||
from ppcls.utils.config import print_config
|
||||
from ppcls.data import build_dataloader
|
||||
from ppcls.arch import build_model
|
||||
from ppcls.arch import apply_to_static
|
||||
from ppcls.loss import build_loss
|
||||
from ppcls.metric import build_metrics
|
||||
from ppcls.optimizer import build_optimizer
|
||||
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
|
||||
from ppcls.utils.save_load import init_model
|
||||
from ppcls.utils import save_load
|
||||
|
||||
from ppcls.data.utils.get_image_list import get_image_list
|
||||
from ppcls.data.postprocess import build_postprocess
|
||||
from ppcls.data import create_operators
|
||||
|
||||
|
||||
class Trainer(object):
|
||||
def __init__(self, config, mode="train"):
|
||||
self.mode = mode
|
||||
self.config = config
|
||||
self.output_dir = self.config['Global']['output_dir']
|
||||
|
||||
log_file = os.path.join(self.output_dir, self.config["Arch"]["name"],
|
||||
f"{mode}.log")
|
||||
init_logger(name='root', log_file=log_file)
|
||||
print_config(config)
|
||||
# set device
|
||||
assert self.config["Global"]["device"] in ["cpu", "gpu", "xpu"]
|
||||
self.device = paddle.set_device(self.config["Global"]["device"])
|
||||
# set dist
|
||||
self.config["Global"][
|
||||
"distributed"] = paddle.distributed.get_world_size() != 1
|
||||
if self.config["Global"]["distributed"]:
|
||||
dist.init_parallel_env()
|
||||
|
||||
if "Head" in self.config["Arch"]:
|
||||
self.is_rec = True
|
||||
else:
|
||||
self.is_rec = False
|
||||
|
||||
self.model = build_model(self.config["Arch"])
|
||||
# set @to_static for benchmark, skip this by default.
|
||||
apply_to_static(self.config, self.model)
|
||||
|
||||
if self.config["Global"]["pretrained_model"] is not None:
|
||||
if self.config["Global"]["pretrained_model"].startswith("http"):
|
||||
load_dygraph_pretrain_from_url(
|
||||
self.model, self.config["Global"]["pretrained_model"])
|
||||
else:
|
||||
load_dygraph_pretrain(
|
||||
self.model, self.config["Global"]["pretrained_model"])
|
||||
|
||||
if self.config["Global"]["distributed"]:
|
||||
self.model = paddle.DataParallel(self.model)
|
||||
|
||||
self.vdl_writer = None
|
||||
if self.config['Global']['use_visualdl'] and mode == "train":
|
||||
vdl_writer_path = os.path.join(self.output_dir, "vdl")
|
||||
if not os.path.exists(vdl_writer_path):
|
||||
os.makedirs(vdl_writer_path)
|
||||
self.vdl_writer = LogWriter(logdir=vdl_writer_path)
|
||||
logger.info('train with paddle {} and device {}'.format(
|
||||
paddle.__version__, self.device))
|
||||
# init members
|
||||
self.train_dataloader = None
|
||||
self.eval_dataloader = None
|
||||
self.gallery_dataloader = None
|
||||
self.query_dataloader = None
|
||||
self.eval_mode = self.config["Global"].get("eval_mode",
|
||||
"classification")
|
||||
self.amp = True if "AMP" in self.config else False
|
||||
if self.amp and self.config["AMP"] is not None:
|
||||
self.scale_loss = self.config["AMP"].get("scale_loss", 1.0)
|
||||
self.use_dynamic_loss_scaling = self.config["AMP"].get(
|
||||
"use_dynamic_loss_scaling", False)
|
||||
else:
|
||||
self.scale_loss = 1.0
|
||||
self.use_dynamic_loss_scaling = False
|
||||
if self.amp:
|
||||
AMP_RELATED_FLAGS_SETTING = {
|
||||
'FLAGS_cudnn_batchnorm_spatial_persistent': 1,
|
||||
'FLAGS_max_inplace_grad_add': 8,
|
||||
}
|
||||
paddle.fluid.set_flags(AMP_RELATED_FLAGS_SETTING)
|
||||
self.train_loss_func = None
|
||||
self.eval_loss_func = None
|
||||
self.train_metric_func = None
|
||||
self.eval_metric_func = None
|
||||
self.use_dali = self.config['Global'].get("use_dali", False)
|
||||
|
||||
def train(self):
|
||||
# build train loss and metric info
|
||||
if self.train_loss_func is None:
|
||||
loss_info = self.config["Loss"]["Train"]
|
||||
self.train_loss_func = build_loss(loss_info)
|
||||
if self.train_metric_func is None:
|
||||
metric_config = self.config.get("Metric")
|
||||
if metric_config is not None:
|
||||
metric_config = metric_config.get("Train")
|
||||
if metric_config is not None:
|
||||
self.train_metric_func = build_metrics(metric_config)
|
||||
|
||||
if self.train_dataloader is None:
|
||||
self.train_dataloader = build_dataloader(
|
||||
self.config["DataLoader"], "Train", self.device, self.use_dali)
|
||||
|
||||
step_each_epoch = len(self.train_dataloader)
|
||||
|
||||
optimizer, lr_sch = build_optimizer(self.config["Optimizer"],
|
||||
self.config["Global"]["epochs"],
|
||||
step_each_epoch,
|
||||
self.model.parameters())
|
||||
|
||||
print_batch_step = self.config['Global']['print_batch_step']
|
||||
save_interval = self.config["Global"]["save_interval"]
|
||||
|
||||
best_metric = {
|
||||
"metric": 0.0,
|
||||
"epoch": 0,
|
||||
}
|
||||
# key:
|
||||
# val: metrics list word
|
||||
output_info = dict()
|
||||
time_info = {
|
||||
"batch_cost": AverageMeter(
|
||||
"batch_cost", '.5f', postfix=" s,"),
|
||||
"reader_cost": AverageMeter(
|
||||
"reader_cost", ".5f", postfix=" s,"),
|
||||
}
|
||||
# global iter counter
|
||||
global_step = 0
|
||||
|
||||
if self.config["Global"]["checkpoints"] is not None:
|
||||
metric_info = init_model(self.config["Global"], self.model,
|
||||
optimizer)
|
||||
if metric_info is not None:
|
||||
best_metric.update(metric_info)
|
||||
|
||||
# for amp training
|
||||
if self.amp:
|
||||
scaler = paddle.amp.GradScaler(
|
||||
init_loss_scaling=self.scale_loss,
|
||||
use_dynamic_loss_scaling=self.use_dynamic_loss_scaling)
|
||||
|
||||
tic = time.time()
|
||||
max_iter = len(self.train_dataloader) - 1 if platform.system(
|
||||
) == "Windows" else len(self.train_dataloader)
|
||||
for epoch_id in range(best_metric["epoch"] + 1,
|
||||
self.config["Global"]["epochs"] + 1):
|
||||
acc = 0.0
|
||||
train_dataloader = self.train_dataloader if self.use_dali else self.train_dataloader(
|
||||
)
|
||||
for iter_id, batch in enumerate(train_dataloader):
|
||||
if iter_id >= max_iter:
|
||||
break
|
||||
if iter_id == 5:
|
||||
for key in time_info:
|
||||
time_info[key].reset()
|
||||
time_info["reader_cost"].update(time.time() - tic)
|
||||
if self.use_dali:
|
||||
batch = [
|
||||
paddle.to_tensor(batch[0]['data']),
|
||||
paddle.to_tensor(batch[0]['label'])
|
||||
]
|
||||
batch_size = batch[0].shape[0]
|
||||
batch[1] = batch[1].reshape([-1, 1]).astype("int64")
|
||||
|
||||
global_step += 1
|
||||
# image input
|
||||
if self.amp:
|
||||
with paddle.amp.auto_cast(custom_black_list={
|
||||
"flatten_contiguous_range", "greater_than"
|
||||
}):
|
||||
out = self.forward(batch)
|
||||
loss_dict = self.train_loss_func(out, batch[1])
|
||||
else:
|
||||
out = self.forward(batch)
|
||||
|
||||
# calc loss
|
||||
if self.config["DataLoader"]["Train"]["dataset"].get(
|
||||
"batch_transform_ops", None):
|
||||
loss_dict = self.train_loss_func(out, batch[1:])
|
||||
else:
|
||||
loss_dict = self.train_loss_func(out, batch[1])
|
||||
|
||||
for key in loss_dict:
|
||||
if not key in output_info:
|
||||
output_info[key] = AverageMeter(key, '7.5f')
|
||||
output_info[key].update(loss_dict[key].numpy()[0],
|
||||
batch_size)
|
||||
# calc metric
|
||||
if self.train_metric_func is not None:
|
||||
metric_dict = self.train_metric_func(out, batch[-1])
|
||||
for key in metric_dict:
|
||||
if not key in output_info:
|
||||
output_info[key] = AverageMeter(key, '7.5f')
|
||||
output_info[key].update(metric_dict[key].numpy()[0],
|
||||
batch_size)
|
||||
|
||||
# step opt and lr
|
||||
if self.amp:
|
||||
scaled = scaler.scale(loss_dict["loss"])
|
||||
scaled.backward()
|
||||
scaler.minimize(optimizer, scaled)
|
||||
else:
|
||||
loss_dict["loss"].backward()
|
||||
optimizer.step()
|
||||
optimizer.clear_grad()
|
||||
lr_sch.step()
|
||||
|
||||
time_info["batch_cost"].update(time.time() - tic)
|
||||
|
||||
if iter_id % print_batch_step == 0:
|
||||
lr_msg = "lr: {:.5f}".format(lr_sch.get_lr())
|
||||
metric_msg = ", ".join([
|
||||
"{}: {:.5f}".format(key, output_info[key].avg)
|
||||
for key in output_info
|
||||
])
|
||||
time_msg = "s, ".join([
|
||||
"{}: {:.5f}".format(key, time_info[key].avg)
|
||||
for key in time_info
|
||||
])
|
||||
|
||||
ips_msg = "ips: {:.5f} images/sec".format(
|
||||
batch_size / time_info["batch_cost"].avg)
|
||||
eta_sec = ((self.config["Global"]["epochs"] - epoch_id + 1
|
||||
) * len(self.train_dataloader) - iter_id
|
||||
) * time_info["batch_cost"].avg
|
||||
eta_msg = "eta: {:s}".format(
|
||||
str(datetime.timedelta(seconds=int(eta_sec))))
|
||||
logger.info(
|
||||
"[Train][Epoch {}/{}][Iter: {}/{}]{}, {}, {}, {}, {}".
|
||||
format(epoch_id, self.config["Global"][
|
||||
"epochs"], iter_id,
|
||||
len(self.train_dataloader), lr_msg, metric_msg,
|
||||
time_msg, ips_msg, eta_msg))
|
||||
|
||||
logger.scaler(
|
||||
name="lr",
|
||||
value=lr_sch.get_lr(),
|
||||
step=global_step,
|
||||
writer=self.vdl_writer)
|
||||
for key in output_info:
|
||||
logger.scaler(
|
||||
name="train_{}".format(key),
|
||||
value=output_info[key].avg,
|
||||
step=global_step,
|
||||
writer=self.vdl_writer)
|
||||
tic = time.time()
|
||||
if self.use_dali:
|
||||
self.train_dataloader.reset()
|
||||
metric_msg = ", ".join([
|
||||
"{}: {:.5f}".format(key, output_info[key].avg)
|
||||
for key in output_info
|
||||
])
|
||||
logger.info("[Train][Epoch {}/{}][Avg]{}".format(
|
||||
epoch_id, self.config["Global"]["epochs"], metric_msg))
|
||||
output_info.clear()
|
||||
|
||||
# eval model and save model if possible
|
||||
if self.config["Global"][
|
||||
"eval_during_train"] and epoch_id % self.config["Global"][
|
||||
"eval_interval"] == 0:
|
||||
acc = self.eval(epoch_id)
|
||||
if acc > best_metric["metric"]:
|
||||
best_metric["metric"] = acc
|
||||
best_metric["epoch"] = epoch_id
|
||||
save_load.save_model(
|
||||
self.model,
|
||||
optimizer,
|
||||
best_metric,
|
||||
self.output_dir,
|
||||
model_name=self.config["Arch"]["name"],
|
||||
prefix="best_model")
|
||||
logger.info("[Eval][Epoch {}][best metric: {}]".format(
|
||||
epoch_id, best_metric["metric"]))
|
||||
logger.scaler(
|
||||
name="eval_acc",
|
||||
value=acc,
|
||||
step=epoch_id,
|
||||
writer=self.vdl_writer)
|
||||
|
||||
self.model.train()
|
||||
|
||||
# save model
|
||||
if epoch_id % save_interval == 0:
|
||||
save_load.save_model(
|
||||
self.model,
|
||||
optimizer, {"metric": acc,
|
||||
"epoch": epoch_id},
|
||||
self.output_dir,
|
||||
model_name=self.config["Arch"]["name"],
|
||||
prefix="epoch_{}".format(epoch_id))
|
||||
# save the latest model
|
||||
save_load.save_model(
|
||||
self.model,
|
||||
optimizer, {"metric": acc,
|
||||
"epoch": epoch_id},
|
||||
self.output_dir,
|
||||
model_name=self.config["Arch"]["name"],
|
||||
prefix="latest")
|
||||
|
||||
if self.vdl_writer is not None:
|
||||
self.vdl_writer.close()
|
||||
|
||||
def build_avg_metrics(self, info_dict):
|
||||
return {key: AverageMeter(key, '7.5f') for key in info_dict}
|
||||
|
||||
@paddle.no_grad()
|
||||
def eval(self, epoch_id=0):
|
||||
self.model.eval()
|
||||
if self.eval_loss_func is None:
|
||||
loss_config = self.config.get("Loss", None)
|
||||
if loss_config is not None:
|
||||
loss_config = loss_config.get("Eval")
|
||||
if loss_config is not None:
|
||||
self.eval_loss_func = build_loss(loss_config)
|
||||
if self.eval_mode == "classification":
|
||||
if self.eval_dataloader is None:
|
||||
self.eval_dataloader = build_dataloader(
|
||||
self.config["DataLoader"], "Eval", self.device,
|
||||
self.use_dali)
|
||||
|
||||
if self.eval_metric_func is None:
|
||||
metric_config = self.config.get("Metric")
|
||||
if metric_config is not None:
|
||||
metric_config = metric_config.get("Eval")
|
||||
if metric_config is not None:
|
||||
self.eval_metric_func = build_metrics(metric_config)
|
||||
|
||||
eval_result = self.eval_cls(epoch_id)
|
||||
|
||||
elif self.eval_mode == "retrieval":
|
||||
if self.gallery_dataloader is None:
|
||||
self.gallery_dataloader = build_dataloader(
|
||||
self.config["DataLoader"]["Eval"], "Gallery", self.device,
|
||||
self.use_dali)
|
||||
|
||||
if self.query_dataloader is None:
|
||||
self.query_dataloader = build_dataloader(
|
||||
self.config["DataLoader"]["Eval"], "Query", self.device,
|
||||
self.use_dali)
|
||||
# build metric info
|
||||
if self.eval_metric_func is None:
|
||||
metric_config = self.config.get("Metric", None)
|
||||
if metric_config is None:
|
||||
metric_config = [{"name": "Recallk", "topk": (1, 5)}]
|
||||
else:
|
||||
metric_config = metric_config["Eval"]
|
||||
self.eval_metric_func = build_metrics(metric_config)
|
||||
eval_result = self.eval_retrieval(epoch_id)
|
||||
else:
|
||||
logger.warning("Invalid eval mode: {}".format(self.eval_mode))
|
||||
eval_result = None
|
||||
self.model.train()
|
||||
return eval_result
|
||||
|
||||
def forward(self, batch):
|
||||
if not self.is_rec:
|
||||
out = self.model(batch[0])
|
||||
else:
|
||||
out = self.model(batch[0], batch[1])
|
||||
return out
|
||||
|
||||
@paddle.no_grad()
|
||||
def eval_cls(self, epoch_id=0):
|
||||
output_info = dict()
|
||||
time_info = {
|
||||
"batch_cost": AverageMeter(
|
||||
"batch_cost", '.5f', postfix=" s,"),
|
||||
"reader_cost": AverageMeter(
|
||||
"reader_cost", ".5f", postfix=" s,"),
|
||||
}
|
||||
print_batch_step = self.config["Global"]["print_batch_step"]
|
||||
|
||||
metric_key = None
|
||||
tic = time.time()
|
||||
eval_dataloader = self.eval_dataloader if self.use_dali else self.eval_dataloader(
|
||||
)
|
||||
max_iter = len(self.eval_dataloader) - 1 if platform.system(
|
||||
) == "Windows" else len(self.eval_dataloader)
|
||||
for iter_id, batch in enumerate(eval_dataloader):
|
||||
if iter_id >= max_iter:
|
||||
break
|
||||
if iter_id == 5:
|
||||
for key in time_info:
|
||||
time_info[key].reset()
|
||||
if self.use_dali:
|
||||
batch = [
|
||||
paddle.to_tensor(batch[0]['data']),
|
||||
paddle.to_tensor(batch[0]['label'])
|
||||
]
|
||||
time_info["reader_cost"].update(time.time() - tic)
|
||||
batch_size = batch[0].shape[0]
|
||||
batch[0] = paddle.to_tensor(batch[0]).astype("float32")
|
||||
batch[1] = batch[1].reshape([-1, 1]).astype("int64")
|
||||
# image input
|
||||
out = self.forward(batch)
|
||||
# calc loss
|
||||
if self.eval_loss_func is not None:
|
||||
loss_dict = self.eval_loss_func(out, batch[-1])
|
||||
for key in loss_dict:
|
||||
if not key in output_info:
|
||||
output_info[key] = AverageMeter(key, '7.5f')
|
||||
output_info[key].update(loss_dict[key].numpy()[0],
|
||||
batch_size)
|
||||
# calc metric
|
||||
if self.eval_metric_func is not None:
|
||||
metric_dict = self.eval_metric_func(out, batch[-1])
|
||||
if paddle.distributed.get_world_size() > 1:
|
||||
for key in metric_dict:
|
||||
paddle.distributed.all_reduce(
|
||||
metric_dict[key],
|
||||
op=paddle.distributed.ReduceOp.SUM)
|
||||
metric_dict[key] = metric_dict[
|
||||
key] / paddle.distributed.get_world_size()
|
||||
for key in metric_dict:
|
||||
if metric_key is None:
|
||||
metric_key = key
|
||||
if not key in output_info:
|
||||
output_info[key] = AverageMeter(key, '7.5f')
|
||||
|
||||
output_info[key].update(metric_dict[key].numpy()[0],
|
||||
batch_size)
|
||||
|
||||
time_info["batch_cost"].update(time.time() - tic)
|
||||
|
||||
if iter_id % print_batch_step == 0:
|
||||
time_msg = "s, ".join([
|
||||
"{}: {:.5f}".format(key, time_info[key].avg)
|
||||
for key in time_info
|
||||
])
|
||||
|
||||
ips_msg = "ips: {:.5f} images/sec".format(
|
||||
batch_size / time_info["batch_cost"].avg)
|
||||
|
||||
metric_msg = ", ".join([
|
||||
"{}: {:.5f}".format(key, output_info[key].val)
|
||||
for key in output_info
|
||||
])
|
||||
logger.info("[Eval][Epoch {}][Iter: {}/{}]{}, {}, {}".format(
|
||||
epoch_id, iter_id,
|
||||
len(self.eval_dataloader), metric_msg, time_msg, ips_msg))
|
||||
|
||||
tic = time.time()
|
||||
if self.use_dali:
|
||||
self.eval_dataloader.reset()
|
||||
metric_msg = ", ".join([
|
||||
"{}: {:.5f}".format(key, output_info[key].avg)
|
||||
for key in output_info
|
||||
])
|
||||
logger.info("[Eval][Epoch {}][Avg]{}".format(epoch_id, metric_msg))
|
||||
|
||||
# do not try to save best model
|
||||
if self.eval_metric_func is None:
|
||||
return -1
|
||||
# return 1st metric in the dict
|
||||
return output_info[metric_key].avg
|
||||
|
||||
def eval_retrieval(self, epoch_id=0):
|
||||
self.model.eval()
|
||||
# step1. build gallery
|
||||
gallery_feas, gallery_img_id, gallery_unique_id = self._cal_feature(
|
||||
name='gallery')
|
||||
query_feas, query_img_id, query_query_id = self._cal_feature(
|
||||
name='query')
|
||||
|
||||
# step2. do evaluation
|
||||
sim_block_size = self.config["Global"].get("sim_block_size", 64)
|
||||
sections = [sim_block_size] * (len(query_feas) // sim_block_size)
|
||||
if len(query_feas) % sim_block_size:
|
||||
sections.append(len(query_feas) % sim_block_size)
|
||||
fea_blocks = paddle.split(query_feas, num_or_sections=sections)
|
||||
if query_query_id is not None:
|
||||
query_id_blocks = paddle.split(
|
||||
query_query_id, num_or_sections=sections)
|
||||
image_id_blocks = paddle.split(query_img_id, num_or_sections=sections)
|
||||
metric_key = None
|
||||
|
||||
if self.eval_metric_func is None:
|
||||
metric_dict = {metric_key: 0.}
|
||||
else:
|
||||
metric_dict = dict()
|
||||
for block_idx, block_fea in enumerate(fea_blocks):
|
||||
similarity_matrix = paddle.matmul(
|
||||
block_fea, gallery_feas, transpose_y=True)
|
||||
if query_query_id is not None:
|
||||
query_id_block = query_id_blocks[block_idx]
|
||||
query_id_mask = (query_id_block != gallery_unique_id.t())
|
||||
|
||||
image_id_block = image_id_blocks[block_idx]
|
||||
image_id_mask = (image_id_block != gallery_img_id.t())
|
||||
|
||||
keep_mask = paddle.logical_or(query_id_mask, image_id_mask)
|
||||
similarity_matrix = similarity_matrix * keep_mask.astype(
|
||||
"float32")
|
||||
else:
|
||||
keep_mask = None
|
||||
|
||||
metric_tmp = self.eval_metric_func(similarity_matrix,
|
||||
image_id_blocks[block_idx],
|
||||
gallery_img_id, keep_mask)
|
||||
|
||||
for key in metric_tmp:
|
||||
if key not in metric_dict:
|
||||
metric_dict[key] = metric_tmp[key] * block_fea.shape[
|
||||
0] / len(query_feas)
|
||||
else:
|
||||
metric_dict[key] += metric_tmp[key] * block_fea.shape[
|
||||
0] / len(query_feas)
|
||||
|
||||
metric_info_list = []
|
||||
for key in metric_dict:
|
||||
if metric_key is None:
|
||||
metric_key = key
|
||||
metric_info_list.append("{}: {:.5f}".format(key, metric_dict[key]))
|
||||
metric_msg = ", ".join(metric_info_list)
|
||||
logger.info("[Eval][Epoch {}][Avg]{}".format(epoch_id, metric_msg))
|
||||
|
||||
return metric_dict[metric_key]
|
||||
|
||||
def _cal_feature(self, name='gallery'):
|
||||
all_feas = None
|
||||
all_image_id = None
|
||||
all_unique_id = None
|
||||
if name == 'gallery':
|
||||
dataloader = self.gallery_dataloader
|
||||
elif name == 'query':
|
||||
dataloader = self.query_dataloader
|
||||
else:
|
||||
raise RuntimeError("Only support gallery or query dataset")
|
||||
|
||||
has_unique_id = False
|
||||
max_iter = len(dataloader) - 1 if platform.system(
|
||||
) == "Windows" else len(dataloader)
|
||||
dataloader_tmp = dataloader if self.use_dali else dataloader()
|
||||
for idx, batch in enumerate(
|
||||
dataloader_tmp): # load is very time-consuming
|
||||
if idx >= max_iter:
|
||||
break
|
||||
if idx % self.config["Global"]["print_batch_step"] == 0:
|
||||
logger.info(
|
||||
f"{name} feature calculation process: [{idx}/{len(dataloader)}]"
|
||||
)
|
||||
if self.use_dali:
|
||||
batch = [
|
||||
paddle.to_tensor(batch[0]['data']),
|
||||
paddle.to_tensor(batch[0]['label'])
|
||||
]
|
||||
batch = [paddle.to_tensor(x) for x in batch]
|
||||
batch[1] = batch[1].reshape([-1, 1]).astype("int64")
|
||||
if len(batch) == 3:
|
||||
has_unique_id = True
|
||||
batch[2] = batch[2].reshape([-1, 1]).astype("int64")
|
||||
out = self.forward(batch)
|
||||
batch_feas = out["features"]
|
||||
|
||||
# do norm
|
||||
if self.config["Global"].get("feature_normalize", True):
|
||||
feas_norm = paddle.sqrt(
|
||||
paddle.sum(paddle.square(batch_feas), axis=1,
|
||||
keepdim=True))
|
||||
batch_feas = paddle.divide(batch_feas, feas_norm)
|
||||
|
||||
if all_feas is None:
|
||||
all_feas = batch_feas
|
||||
if has_unique_id:
|
||||
all_unique_id = batch[2]
|
||||
all_image_id = batch[1]
|
||||
else:
|
||||
all_feas = paddle.concat([all_feas, batch_feas])
|
||||
all_image_id = paddle.concat([all_image_id, batch[1]])
|
||||
if has_unique_id:
|
||||
all_unique_id = paddle.concat([all_unique_id, batch[2]])
|
||||
if self.use_dali:
|
||||
dataloader_tmp.reset()
|
||||
if paddle.distributed.get_world_size() > 1:
|
||||
feat_list = []
|
||||
img_id_list = []
|
||||
unique_id_list = []
|
||||
paddle.distributed.all_gather(feat_list, all_feas)
|
||||
paddle.distributed.all_gather(img_id_list, all_image_id)
|
||||
all_feas = paddle.concat(feat_list, axis=0)
|
||||
all_image_id = paddle.concat(img_id_list, axis=0)
|
||||
if has_unique_id:
|
||||
paddle.distributed.all_gather(unique_id_list, all_unique_id)
|
||||
all_unique_id = paddle.concat(unique_id_list, axis=0)
|
||||
|
||||
logger.info("Build {} done, all feat shape: {}, begin to eval..".
|
||||
format(name, all_feas.shape))
|
||||
return all_feas, all_image_id, all_unique_id
|
||||
|
||||
@paddle.no_grad()
|
||||
def infer(self, ):
|
||||
total_trainer = paddle.distributed.get_world_size()
|
||||
local_rank = paddle.distributed.get_rank()
|
||||
image_list = get_image_list(self.config["Infer"]["infer_imgs"])
|
||||
# data split
|
||||
image_list = image_list[local_rank::total_trainer]
|
||||
|
||||
preprocess_func = create_operators(self.config["Infer"]["transforms"])
|
||||
postprocess_func = build_postprocess(self.config["Infer"][
|
||||
"PostProcess"])
|
||||
|
||||
batch_size = self.config["Infer"]["batch_size"]
|
||||
|
||||
self.model.eval()
|
||||
|
||||
batch_data = []
|
||||
image_file_list = []
|
||||
for idx, image_file in enumerate(image_list):
|
||||
with open(image_file, 'rb') as f:
|
||||
x = f.read()
|
||||
for process in preprocess_func:
|
||||
x = process(x)
|
||||
batch_data.append(x)
|
||||
image_file_list.append(image_file)
|
||||
if len(batch_data) >= batch_size or idx == len(image_list) - 1:
|
||||
batch_tensor = paddle.to_tensor(batch_data)
|
||||
out = self.forward([batch_tensor])
|
||||
if isinstance(out, list):
|
||||
out = out[0]
|
||||
result = postprocess_func(out, image_file_list)
|
||||
print(result)
|
||||
batch_data.clear()
|
||||
image_file_list.clear()
|
||||
|
|
@ -21,11 +21,11 @@ __dir__ = os.path.dirname(os.path.abspath(__file__))
|
|||
sys.path.append(os.path.abspath(os.path.join(__dir__, '../')))
|
||||
|
||||
from ppcls.utils import config
|
||||
from ppcls.engine.trainer import Trainer
|
||||
from ppcls.engine.core import Core
|
||||
|
||||
if __name__ == "__main__":
|
||||
args = config.parse_args()
|
||||
config = config.get_config(
|
||||
args.config, overrides=args.override, show=False)
|
||||
trainer = Trainer(config, mode="eval")
|
||||
trainer.eval()
|
||||
evaler = Core(config, mode="eval")
|
||||
evaler.eval()
|
||||
|
|
|
|||
|
|
@ -24,82 +24,11 @@ import paddle
|
|||
import paddle.nn as nn
|
||||
|
||||
from ppcls.utils import config
|
||||
from ppcls.utils.logger import init_logger
|
||||
from ppcls.utils.config import print_config
|
||||
from ppcls.arch import build_model, RecModel, DistillationModel
|
||||
from ppcls.utils.save_load import load_dygraph_pretrain
|
||||
from ppcls.arch.gears.identity_head import IdentityHead
|
||||
|
||||
|
||||
class ExportModel(nn.Layer):
|
||||
"""
|
||||
ExportModel: add softmax onto the model
|
||||
"""
|
||||
|
||||
def __init__(self, config):
|
||||
super().__init__()
|
||||
self.base_model = build_model(config)
|
||||
|
||||
# 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 config.get("infer_add_softmax", True):
|
||||
self.softmax = nn.Softmax(axis=-1)
|
||||
else:
|
||||
self.softmax = 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.softmax is not None:
|
||||
x = self.softmax(x)
|
||||
return x
|
||||
|
||||
from ppcls.engine.core import Core
|
||||
|
||||
if __name__ == "__main__":
|
||||
args = config.parse_args()
|
||||
config = config.get_config(
|
||||
args.config, overrides=args.override, show=False)
|
||||
log_file = os.path.join(config['Global']['output_dir'],
|
||||
config["Arch"]["name"], "export.log")
|
||||
init_logger(name='root', log_file=log_file)
|
||||
print_config(config)
|
||||
|
||||
# set device
|
||||
assert config["Global"]["device"] in ["cpu", "gpu", "xpu"]
|
||||
device = paddle.set_device(config["Global"]["device"])
|
||||
model = ExportModel(config["Arch"])
|
||||
if config["Global"]["pretrained_model"] is not None:
|
||||
load_dygraph_pretrain(model.base_model,
|
||||
config["Global"]["pretrained_model"])
|
||||
|
||||
model.eval()
|
||||
|
||||
model = paddle.jit.to_static(
|
||||
model,
|
||||
input_spec=[
|
||||
paddle.static.InputSpec(
|
||||
shape=[None] + config["Global"]["image_shape"],
|
||||
dtype='float32')
|
||||
])
|
||||
paddle.jit.save(model,
|
||||
os.path.join(config["Global"]["save_inference_dir"],
|
||||
"inference"))
|
||||
exporter = Core(config, mode="export")
|
||||
exporter.export()
|
||||
|
|
|
|||
|
|
@ -21,12 +21,11 @@ __dir__ = os.path.dirname(os.path.abspath(__file__))
|
|||
sys.path.append(os.path.abspath(os.path.join(__dir__, '../')))
|
||||
|
||||
from ppcls.utils import config
|
||||
from ppcls.engine.trainer import Trainer
|
||||
from ppcls.engine.core import Core
|
||||
|
||||
if __name__ == "__main__":
|
||||
args = config.parse_args()
|
||||
config = config.get_config(
|
||||
args.config, overrides=args.override, show=False)
|
||||
trainer = Trainer(config, mode="infer")
|
||||
|
||||
trainer.infer()
|
||||
inferer = Core(config, mode="infer")
|
||||
inferer.infer()
|
||||
|
|
|
|||
|
|
@ -21,11 +21,11 @@ __dir__ = os.path.dirname(os.path.abspath(__file__))
|
|||
sys.path.append(os.path.abspath(os.path.join(__dir__, '../')))
|
||||
|
||||
from ppcls.utils import config
|
||||
from ppcls.engine.trainer import Trainer
|
||||
from ppcls.engine.core import Core
|
||||
|
||||
if __name__ == "__main__":
|
||||
args = config.parse_args()
|
||||
config = config.get_config(
|
||||
args.config, overrides=args.override, show=False)
|
||||
trainer = Trainer(config, mode="train")
|
||||
trainer = Core(config, mode="train")
|
||||
trainer.train()
|
||||
|
|
|
|||
Loading…
Reference in New Issue