278 lines
11 KiB
Python
278 lines
11 KiB
Python
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# Copyright (c) 2020 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 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 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 ppcls.utils import config
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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.data import build_dataloader
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from ppcls.arch import build_model
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from ppcls.arch.loss_metrics import build_loss
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from ppcls.arch.loss_metrics import build_metrics
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from ppcls.optimizer import build_optimizer
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from ppcls.utils import save_load
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class Trainer(object):
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def __init__(self, mode="train"):
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args = config.parse_args()
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self.config = config.get_config(
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args.config, overrides=args.override, show=True)
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self.mode = mode
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self.output_dir = self.config['Global']['output_dir']
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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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# set dist
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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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self.model = build_model(self.config["Arch"])
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if self.config["Global"]["distributed"]:
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self.model = paddle.DataParallel(self.model)
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self.vdl_writer = None
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if self.config['Global']['use_visualdl']:
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from visualdl import LogWriter
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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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logger.info('train with paddle {} and device {}'.format(
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paddle.__version__, self.device))
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def _build_metric_info(self, metric_config, mode="train"):
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"""
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_build_metric_info: build metrics according to current mode
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Return:
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metric: dict of the metrics info
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"""
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metric = None
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mode = mode.capitalize()
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if mode in metric_config and metric_config[mode] is not None:
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metric = build_metrics(metric_config[mode])
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return metric
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def _build_loss_info(self, loss_config, mode="train"):
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"""
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_build_loss_info: build loss according to current mode
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Return:
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loss_dict: dict of the loss info
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"""
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loss = None
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mode = mode.capitalize()
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if mode in loss_config and loss_config[mode] is not None:
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loss = build_loss(loss_config[mode])
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return loss
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def train(self):
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# build train loss and metric info
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loss_func = self._build_loss_info(self.config["Loss"])
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metric_func = self._build_metric_info(self.config["Metric"])
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train_dataloader = build_dataloader(self.config["DataLoader"], "train",
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self.device)
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step_each_epoch = len(train_dataloader)
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optimizer, lr_sch = build_optimizer(self.config["Optimizer"],
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self.config["Global"]["epochs"],
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step_each_epoch,
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self.model.parameters())
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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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output_info = dict()
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# global iter counter
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global_step = 0
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for epoch_id in range(1, self.config["Global"]["epochs"] + 1):
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self.model.train()
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for iter_id, batch in enumerate(train_dataloader()):
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batch_size = batch[0].shape[0]
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batch[1] = paddle.to_tensor(batch[1].numpy().astype("int64")
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.reshape([-1, 1]))
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global_step += 1
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# image input
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out = self.model(batch[0])
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# calc loss
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loss_dict = loss_func(out, batch[-1])
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for key in loss_dict:
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if not key in output_info:
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output_info[key] = AverageMeter(key, '7.5f')
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output_info[key].update(loss_dict[key].numpy()[0],
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batch_size)
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# calc metric
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if metric_func is not None:
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metric_dict = metric_func(out, batch[-1])
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for key in metric_dict:
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if not key in output_info:
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output_info[key] = AverageMeter(key, '7.5f')
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output_info[key].update(metric_dict[key].numpy()[0],
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batch_size)
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if iter_id % print_batch_step == 0:
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lr_msg = "lr: {:.5f}".format(lr_sch.get_lr())
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metric_msg = ", ".join([
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"{}: {:.5f}".format(key, output_info[key].avg)
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for key in output_info
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])
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logger.info("[Train][Epoch {}][Iter: {}/{}]{}, {}".format(
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epoch_id, iter_id,
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len(train_dataloader), lr_msg, metric_msg))
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# step opt and lr
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loss_dict["loss"].backward()
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optimizer.step()
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optimizer.clear_grad()
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lr_sch.step()
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metric_msg = ", ".join([
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"{}: {:.5f}".format(key, output_info[key].avg)
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for key in output_info
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])
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logger.info("[Train][Epoch {}][Avg]{}".format(epoch_id,
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metric_msg))
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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_during_train"] == 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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optimizer,
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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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# 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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optimizer,
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self.output_dir,
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model_name=self.config["Arch"]["name"],
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prefix="ppcls_epoch_{}".format(epoch_id))
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def build_avg_metrics(self, info_dict):
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return {key: AverageMeter(key, '7.5f') for key in info_dict}
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@paddle.no_grad()
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def eval(self, epoch_id=0):
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output_info = dict()
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eval_dataloader = build_dataloader(self.config["DataLoader"], "eval",
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self.device)
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self.model.eval()
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print_batch_step = self.config["Global"]["print_batch_step"]
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# build train loss and metric info
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loss_func = self._build_loss_info(self.config["Loss"], "eval")
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metric_func = self._build_metric_info(self.config["Metric"], "eval")
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metric_key = None
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for iter_id, batch in enumerate(eval_dataloader()):
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batch_size = batch[0].shape[0]
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batch[0] = paddle.to_tensor(batch[0]).astype("float32")
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batch[1] = paddle.to_tensor(batch[1]).reshape([-1, 1])
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# image input
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out = self.model(batch[0])
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# calc build
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if loss_func is not None:
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loss_dict = loss_func(out, batch[-1])
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for key in loss_dict:
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if not key in output_info:
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output_info[key] = AverageMeter(key, '7.5f')
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output_info[key].update(loss_dict[key].numpy()[0],
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batch_size)
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# calc metric
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if metric_func is not None:
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metric_dict = metric_func(out, batch[-1])
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if paddle.distributed.get_world_size() > 1:
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for key in metric_dict:
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paddle.distributed.all_reduce(
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metric_dict[key],
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op=paddle.distributed.ReduceOp.SUM)
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metric_dict[key] = metric_dict[
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key] / paddle.distributed.get_world_size()
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for key in metric_dict:
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if metric_key is None:
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metric_key = key
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if not key in output_info:
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output_info[key] = AverageMeter(key, '7.5f')
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output_info[key].update(metric_dict[key].numpy()[0],
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batch_size)
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if iter_id % print_batch_step == 0:
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metric_msg = ", ".join([
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"{}: {:.5f}".format(key, output_info[key].val)
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for key in output_info
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])
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logger.info("[Eval][Epoch {}][Iter: {}/{}]{}".format(
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epoch_id, iter_id, len(eval_dataloader), metric_msg))
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metric_msg = ", ".join([
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"{}: {:.5f}".format(key, output_info[key].avg)
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for key in output_info
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])
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logger.info("[Eval][Epoch {}][Avg]{}".format(epoch_id, metric_msg))
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self.model.train()
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# do not try to save best model
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if metric_func is None:
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return -1
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# return 1st metric in the dict
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return output_info[metric_key].avg
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def main():
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trainer = Trainer()
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trainer.train()
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if __name__ == "__main__":
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main()
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