548 lines
22 KiB
Python
548 lines
22 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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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 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 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.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
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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.reader import create_operators
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class Trainer(object):
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def __init__(self, config, mode="train"):
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self.mode = mode
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self.config = config
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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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if "Head" in self.config["Arch"]:
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self.config["Arch"]["Head"]["class_num"] = self.config["Global"][
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"class_num"]
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self.is_rec = True
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else:
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self.is_rec = False
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self.model = build_model(self.config["Arch"])
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if self.config["Global"]["pretrained_model"] is not None:
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load_dygraph_pretrain(self.model,
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self.config["Global"]["pretrained_model"])
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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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# init members
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self.train_dataloader = None
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self.eval_dataloader = None
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self.gallery_dataloader = None
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self.query_dataloader = None
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self.eval_mode = self.config["Global"].get("eval_mode",
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"classification")
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self.train_loss_func = None
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self.eval_loss_func = None
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self.train_metric_func = None
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self.eval_metric_func = None
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def train(self):
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# build train loss and metric info
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if self.train_loss_func is None:
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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.train_metric_func is None:
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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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if self.train_dataloader is None:
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self.train_dataloader = build_dataloader(self.config["DataLoader"],
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"Train", self.device)
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step_each_epoch = len(self.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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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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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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optimizer)
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if metric_info is not None:
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best_metric.update(metric_info)
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tic = time.time()
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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 iter_id, batch in enumerate(self.train_dataloader()):
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if iter_id == 5:
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for key in time_info:
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time_info[key].reset()
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time_info["reader_cost"].update(time.time() - tic)
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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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if not self.is_rec:
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out = self.model(batch[0])
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else:
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out = self.model(batch[0], batch[1])
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# calc loss
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loss_dict = self.train_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 self.train_metric_func is not None:
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metric_dict = self.train_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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# 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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time_info["batch_cost"].update(time.time() - tic)
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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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time_msg = "s, ".join([
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"{}: {:.5f}".format(key, time_info[key].avg)
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for key in time_info
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])
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ips_msg = "ips: {:.5f} images/sec".format(
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batch_size / time_info["batch_cost"].avg)
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eta_sec = ((self.config["Global"]["epochs"] - epoch_id + 1
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) * len(self.train_dataloader) - iter_id
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) * time_info["batch_cost"].avg
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eta_msg = "eta: {:s}".format(
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str(datetime.timedelta(seconds=int(eta_sec))))
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logger.info(
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"[Train][Epoch {}][Iter: {}/{}]{}, {}, {}, {}, {}".
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format(epoch_id, iter_id,
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len(self.train_dataloader), lr_msg, metric_msg,
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time_msg, ips_msg, eta_msg))
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tic = time.time()
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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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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, acc))
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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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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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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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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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self.model.eval()
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if self.eval_loss_func is None:
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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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if self.eval_mode == "classification":
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if self.eval_dataloader is None:
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self.eval_dataloader = build_dataloader(
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self.config["DataLoader"], "Eval", self.device)
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if self.eval_metric_func is None:
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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("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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eval_result = self.eval_cls(epoch_id)
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elif self.eval_mode == "retrieval":
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if self.gallery_dataloader is None:
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self.gallery_dataloader = build_dataloader(
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self.config["DataLoader"]["Eval"], "Gallery", self.device)
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if self.query_dataloader is None:
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self.query_dataloader = build_dataloader(
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self.config["DataLoader"]["Eval"], "Query", self.device)
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# build metric info
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if self.eval_metric_func is None:
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metric_config = self.config.get("Metric", None)
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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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eval_result = self.eval_retrieval(epoch_id)
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else:
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logger.warning("Invalid eval mode: {}".format(self.eval_mode))
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eval_result = None
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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 eval_cls(self, 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,"),
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"reader_cost": AverageMeter(
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"reader_cost", ".5f", postfix=" s,"),
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}
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print_batch_step = self.config["Global"]["print_batch_step"]
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metric_key = None
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tic = time.time()
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for iter_id, batch in enumerate(self.eval_dataloader()):
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if iter_id == 5:
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for key in time_info:
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time_info[key].reset()
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time_info["reader_cost"].update(time.time() - tic)
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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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if self.is_rec:
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out = self.model(batch[0], batch[1])
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else:
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out = self.model(batch[0])
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# calc loss
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if self.eval_loss_func is not None:
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loss_dict = self.eval_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 self.eval_metric_func is not None:
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metric_dict = self.eval_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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time_info["batch_cost"].update(time.time() - tic)
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if iter_id % print_batch_step == 0:
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time_msg = "s, ".join([
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"{}: {:.5f}".format(key, time_info[key].avg)
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for key in time_info
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])
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ips_msg = "ips: {:.5f} images/sec".format(
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batch_size / time_info["batch_cost"].avg)
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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,
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len(self.eval_dataloader), metric_msg, time_msg, ips_msg))
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tic = time.time()
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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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# do not try to save best model
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if self.eval_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 eval_retrieval(self, epoch_id=0):
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self.model.eval()
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cum_similarity_matrix = None
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# step1. build gallery
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gallery_feas, gallery_img_id, gallery_unique_id = self._cal_feature(
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name='gallery')
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query_feas, query_img_id, query_query_id = self._cal_feature(
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name='query')
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gallery_img_id = gallery_img_id
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# if gallery_unique_id is not None:
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# gallery_unique_id = gallery_unique_id
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# step2. do evaluation
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sim_block_size = self.config["Global"].get("sim_block_size", 64)
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sections = [sim_block_size] * (len(query_feas) // sim_block_size)
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if len(query_feas) % sim_block_size:
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sections.append(len(query_feas) % sim_block_size)
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fea_blocks = paddle.split(query_feas, num_or_sections=sections)
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if query_query_id is not None:
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query_id_blocks = paddle.split(
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query_query_id, num_or_sections=sections)
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image_id_blocks = paddle.split(
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query_img_id, num_or_sections=sections)
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metric_key = None
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if self.eval_metric_func is None:
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metric_dict = {metric_key: 0.}
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else:
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metric_dict = dict()
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for block_idx, block_fea in enumerate(fea_blocks):
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similarity_matrix = paddle.matmul(
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block_fea, gallery_feas, transpose_y=True)
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if query_query_id is not None:
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query_id_block = query_id_blocks[block_idx]
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query_id_mask = (query_id_block != gallery_unique_id.t())
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image_id_block = image_id_blocks[block_idx]
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image_id_mask = (image_id_block != gallery_img_id.t())
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keep_mask = paddle.logical_or(query_id_mask, image_id_mask)
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similarity_matrix = similarity_matrix * keep_mask.astype("float32")
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metric_tmp = self.eval_metric_func(similarity_matrix,image_id_blocks[block_idx], gallery_img_id)
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for key in metric_tmp:
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if key not in metric_dict:
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metric_dict[key] = metric_tmp[key]
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else:
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metric_dict[key] += metric_tmp[key]
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num_sections = len(fea_blocks)
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for key in metric_dict:
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metric_dict[key] = metric_dict[key]/num_sections
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metric_info_list = []
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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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metric_info_list.append("{}: {:.5f}".format(key, metric_dict[key]))
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metric_msg = ", ".join(metric_info_list)
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logger.info("[Eval][Epoch {}][Avg]{}".format(epoch_id, metric_msg))
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return metric_dict[metric_key]
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def _cal_feature(self, name='gallery'):
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all_feas = None
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all_image_id = None
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all_unique_id = None
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if name == 'gallery':
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dataloader = self.gallery_dataloader
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elif name == 'query':
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dataloader = self.query_dataloader
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else:
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raise RuntimeError("Only support gallery or query dataset")
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has_unique_id = False
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for idx, batch in enumerate(dataloader(
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)): # load is very time-consuming
|
|
batch = [paddle.to_tensor(x) for x in batch]
|
|
batch[1] = batch[1].reshape([-1, 1])
|
|
if len(batch) == 3:
|
|
has_unique_id = True
|
|
batch[2] = batch[2].reshape([-1, 1])
|
|
out = self.model(batch[0], batch[1])
|
|
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 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.model(batch_tensor)
|
|
result = postprocess_func(out, image_file_list)
|
|
print(result)
|
|
batch_data.clear()
|
|
image_file_list.clear()
|