# 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 argparse import paddle import paddle.nn as nn import paddle.distributed as dist from ppcls.utils.check import check_gpu from ppcls.utils.misc import AverageMeter from ppcls.utils import logger from ppcls.data import build_dataloader from ppcls.arch import build_model from ppcls.loss import build_loss from ppcls.arch.loss_metrics import build_metrics from ppcls.optimizer import build_optimizer from ppcls.utils.save_load import load_dygraph_pretrain 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.reader 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'] # 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.config["Arch"]["Head"]["class_num"] = self.config["Global"][ "class_num"] self.is_rec = True else: self.is_rec = False self.model = build_model(self.config["Arch"]) if self.config["Global"]["pretrained_model"] is not None: 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']: from visualdl import LogWriter 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.train_loss_func = None self.eval_loss_func = None self.train_metric_func = None self.eval_metric_func = None def _build_metric_info(self, metric_config, mode="train"): """ _build_metric_info: build metrics according to current mode Return: metric: dict of the metrics info """ metric = None mode = mode.capitalize() if mode in metric_config and metric_config[mode] is not None: metric = build_metrics(metric_config[mode]) return metric def _build_loss_info(self, loss_config, mode="train"): """ _build_loss_info: build loss according to current mode Return: loss_dict: dict of the loss info """ loss = None mode = mode.capitalize() if mode in loss_config and loss_config[mode] is not None: loss = build_loss(loss_config[mode]) return loss def train(self): # build train loss and metric info if self.train_loss_func is None: self.train_loss_func = self._build_loss_info(self.config["Loss"]) if "Metric" in self.config and self.train_metric_func is None: self.train_metric_func = self._build_metric_info(self.config[ "Metric"]) if self.train_dataloader is None: self.train_dataloader = build_dataloader(self.config["DataLoader"], "Train", self.device) 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() # 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 epoch_id in range(best_metric["epoch"] + 1, self.config["Global"]["epochs"] + 1): acc = 0.0 self.model.train() for iter_id, batch in enumerate(self.train_dataloader()): batch_size = batch[0].shape[0] batch[1] = paddle.to_tensor(batch[1].numpy().astype("int64") .reshape([-1, 1])) global_step += 1 # image input if not self.is_rec: out = self.model(batch[0]) else: out = self.model(batch[0], batch[1]) # calc loss 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) 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 ]) logger.info("[Train][Epoch {}][Iter: {}/{}]{}, {}".format( epoch_id, iter_id, len(self.train_dataloader), lr_msg, metric_msg)) # step opt and lr loss_dict["loss"].backward() optimizer.step() optimizer.clear_grad() lr_sch.step() metric_msg = ", ".join([ "{}: {:.5f}".format(key, output_info[key].avg) for key in output_info ]) logger.info("[Train][Epoch {}][Avg]{}".format(epoch_id, 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_during_train"] == 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") 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="ppcls_epoch_{}".format(epoch_id)) 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): if self.eval_dataloader is None: self.eval_dataloader = build_dataloader(self.config["DataLoader"], "Eval", self.device) if self.gallery_dataloader is None: self.gallery_dataloader = build_dataloader( self.config["DataLoader"], "Gallery", self.device) if self.query_dataloader is None: self.query_dataloader = build_dataloader(self.config["DataLoader"], "Query", self.device) # build train loss and metric info if self.eval_loss_func is None: self.eval_loss_func = self._build_loss_info(self.config["Loss"], "eval") if self.eval_metric_func is None: self.eval_metric_func = self._build_metric_info( self.config["Metric"], "eval") self.model.eval() if self.eval_mode == "classification": self.eval_cls(epoch_id) elif self.eval_mode == "retrieval": self.eval_retrieval(epoch_id) else: logger.warning("Invalid eval mode: {}".format(self.eval_mode)) def eval_cls(self, epoch_id=0): output_info = dict() print_batch_step = self.config["Global"]["print_batch_step"] metric_key = None for iter_id, batch in enumerate(self.eval_dataloader()): batch_size = batch[0].shape[0] batch[0] = paddle.to_tensor(batch[0]).astype("float32") batch[1] = paddle.to_tensor(batch[1]).reshape([-1, 1]) # image input if self.is_rec: out = self.model(batch[0], batch[1]) else: out = self.model(batch[0]) # 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) if iter_id % print_batch_step == 0: 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)) 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): output_info = dict() self.model.eval() # step1. build gallery gallery_feas, gallery_img_id, gallery_camera_id = self._cal_feature( name='gallery') query_feas, query_img_id, query_camera_id = self._cal_feature( name='query') gallery_img_id = paddle.to_tensor([gallery_img_id]).t() if gallery_camera_id is not None: gallery_camera_id = paddle.to_tensor(gallery_camera_id).t() query_img_id = paddle.to_tensor(query_img_id) if query_camera_id is not None: query_camera_id = paddle.to_tensor(query_camera_id) # step2. do evaluation sim_block_size = self.config["Global"].get("sim_block_size", 1) sections = [sim_block_size] * (len(query_feas) // sim_block_size) if not len(query_feas) % sim_block_size: sections.append(len(query_feas) % sim_block_size) fea_blocks = paddle.split(query_feas, num_or_sections=sections) camera_id_blocks = paddle.split( query_camera_id, num_or_sections=sections) image_id_blocks = paddle.split(query_img_id, num_or_sections=sections) metric_key = None for block_idx, block_fea in enumerate(fea_blocks): similarities_matrix = paddle.matmul( block_fea, gallery_feas, transpose_y=True) image_id_block = image_id_blocks[block_idx] image_id_mask = (image_id_block == gallery_img_id) similarities_matrix = similarities_matrix.masked_select( image_id_mask) camera_id_block = camera_id_blocks[block_idx] camera_id_mask = (camera_id_block == gallery_camera_id) similarities_matrix = similarities_matrix.masked_select( camera_id_mask) # calc metric if self.eval_metric_func is not None: metric_dict = self.eval_metric_func(similarities_matrix, image_id_block) 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], len(image_id_block)) def _cal_feature(self, name='gallery'): all_feas = None all_image_id = None all_camera_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_cam_id = False for idx, batch in enumerate(dataloader( )): # 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_cam_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_cam_id: all_camera_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_cam_id: all_camera_id = paddle.concat([all_camera_id, batch[2]]) if paddle.distributed.get_world_size() > 1: feat_list = [] img_id_list = [] cam_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_cam_id: paddle.distributed.all_gather(cam_id_list, all_camera_id) all_camera_id = paddle.concat(cam_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_camera_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()