import torch import torch.distributed as dist import time import os import numpy as np import random # ----------------- Extra Components ----------------- from utils import distributed_utils from utils.misc import ModelEMA, CollateFunc, build_dataloader from utils.vis_tools import vis_data # ----------------- Evaluator Components ----------------- from evaluator.build import build_evluator # ----------------- Optimizer & LrScheduler Components ----------------- from utils.solver.optimizer import build_yolo_optimizer, build_detr_optimizer from utils.solver.lr_scheduler import build_lr_scheduler # ----------------- Dataset Components ----------------- from dataset.build import build_dataset, build_transform # Trainer refered to YOLOv8 class YoloTrainer(object): def __init__(self, args, data_cfg, model_cfg, trans_cfg, device, model, criterion, world_size): # ------------------- basic parameters ------------------- self.args = args self.epoch = 0 self.best_map = -1. self.last_opt_step = 0 self.no_aug_epoch = args.no_aug_epoch self.clip_grad = 10 self.device = device self.criterion = criterion self.world_size = world_size self.heavy_eval = False self.optimizer_dict = {'optimizer': 'sgd', 'momentum': 0.937, 'weight_decay': 5e-4, 'lr0': 0.01} self.ema_dict = {'ema_decay': 0.9999, 'ema_tau': 2000} self.lr_schedule_dict = {'scheduler': 'linear', 'lrf': 0.01} self.warmup_dict = {'warmup_momentum': 0.8, 'warmup_bias_lr': 0.1} # ---------------------------- Build Dataset & Model & Trans. Config ---------------------------- self.data_cfg = data_cfg self.model_cfg = model_cfg self.trans_cfg = trans_cfg # ---------------------------- Build Transform ---------------------------- self.train_transform, self.trans_cfg = build_transform( args=args, trans_config=self.trans_cfg, max_stride=model_cfg['max_stride'], is_train=True) self.val_transform, _ = build_transform( args=args, trans_config=self.trans_cfg, max_stride=model_cfg['max_stride'], is_train=False) # ---------------------------- Build Dataset & Dataloader ---------------------------- self.dataset, self.dataset_info = build_dataset(self.args, self.data_cfg, self.trans_cfg, self.train_transform, is_train=True) self.train_loader = build_dataloader(self.args, self.dataset, self.args.batch_size // self.world_size, CollateFunc()) # ---------------------------- Build Evaluator ---------------------------- self.evaluator = build_evluator(self.args, self.data_cfg, self.val_transform, self.device) # ---------------------------- Build Grad. Scaler ---------------------------- self.scaler = torch.cuda.amp.GradScaler(enabled=self.args.fp16) # ---------------------------- Build Optimizer ---------------------------- accumulate = max(1, round(64 / self.args.batch_size)) self.optimizer_dict['weight_decay'] *= self.args.batch_size * accumulate / 64 self.optimizer, self.start_epoch = build_yolo_optimizer(self.optimizer_dict, model, self.args.resume) # ---------------------------- Build LR Scheduler ---------------------------- self.lr_scheduler, self.lf = build_lr_scheduler(self.lr_schedule_dict, self.optimizer, self.args.max_epoch) self.lr_scheduler.last_epoch = self.start_epoch - 1 # do not move if self.args.resume: self.lr_scheduler.step() # ---------------------------- Build Model-EMA ---------------------------- if self.args.ema and distributed_utils.get_rank() in [-1, 0]: print('Build ModelEMA ...') self.model_ema = ModelEMA(self.ema_dict, model, self.start_epoch * len(self.train_loader)) else: self.model_ema = None def train(self, model): for epoch in range(self.start_epoch, self.args.max_epoch): if self.args.distributed: self.train_loader.batch_sampler.sampler.set_epoch(epoch) # check second stage if epoch >= (self.args.max_epoch - self.no_aug_epoch - 1): # close mosaic augmentation if self.train_loader.dataset.mosaic_prob > 0.: print('close Mosaic Augmentation ...') self.train_loader.dataset.mosaic_prob = 0. self.heavy_eval = True # close mixup augmentation if self.train_loader.dataset.mixup_prob > 0.: print('close Mixup Augmentation ...') self.train_loader.dataset.mixup_prob = 0. self.heavy_eval = True # train one epoch self.epoch = epoch self.train_one_epoch(model) # eval one epoch if self.heavy_eval: model_eval = model.module if self.args.distributed else model self.eval(model_eval) else: model_eval = model.module if self.args.distributed else model if (epoch % self.args.eval_epoch) == 0 or (epoch == self.args.max_epoch - 1): self.eval(model_eval) def eval(self, model): # chech model model_eval = model if self.model_ema is None else self.model_ema.ema # path to save model path_to_save = os.path.join(self.args.save_folder, self.args.dataset, self.args.model) os.makedirs(path_to_save, exist_ok=True) if distributed_utils.is_main_process(): # check evaluator if self.evaluator is None: print('No evaluator ... save model and go on training.') print('Saving state, epoch: {}'.format(self.epoch + 1)) weight_name = '{}_no_eval.pth'.format(self.args.model) checkpoint_path = os.path.join(path_to_save, weight_name) torch.save({'model': model_eval.state_dict(), 'mAP': -1., 'optimizer': self.optimizer.state_dict(), 'epoch': self.epoch, 'args': self.args}, checkpoint_path) else: print('eval ...') # set eval mode model_eval.trainable = False model_eval.eval() # evaluate with torch.no_grad(): self.evaluator.evaluate(model_eval) # save model cur_map = self.evaluator.map if cur_map > self.best_map: # update best-map self.best_map = cur_map # save model print('Saving state, epoch:', self.epoch + 1) weight_name = '{}_best.pth'.format(self.args.model) checkpoint_path = os.path.join(path_to_save, weight_name) torch.save({'model': model_eval.state_dict(), 'mAP': round(self.best_map*100, 1), 'optimizer': self.optimizer.state_dict(), 'epoch': self.epoch, 'args': self.args}, checkpoint_path) # set train mode. model_eval.trainable = True model_eval.train() if self.args.distributed: # wait for all processes to synchronize dist.barrier() def train_one_epoch(self, model): # basic parameters epoch_size = len(self.train_loader) img_size = self.args.img_size t0 = time.time() nw = epoch_size * self.args.wp_epoch accumulate = accumulate = max(1, round(64 / self.args.batch_size)) # train one epoch for iter_i, (images, targets) in enumerate(self.train_loader): ni = iter_i + self.epoch * epoch_size # Warmup if ni <= nw: xi = [0, nw] # x interp accumulate = max(1, np.interp(ni, xi, [1, 64 / self.args.batch_size]).round()) for j, x in enumerate(self.optimizer.param_groups): # bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0 x['lr'] = np.interp( ni, xi, [self.warmup_dict['warmup_bias_lr'] if j == 0 else 0.0, x['initial_lr'] * self.lf(self.epoch)]) if 'momentum' in x: x['momentum'] = np.interp(ni, xi, [self.warmup_dict['warmup_momentum'], self.optimizer_dict['momentum']]) # to device images = images.to(self.device, non_blocking=True).float() / 255. # Multi scale if self.args.multi_scale: images, targets, img_size = self.rescale_image_targets( images, targets, self.model_cfg['stride'], self.args.min_box_size, self.model_cfg['multi_scale']) else: targets = self.refine_targets(targets, self.args.min_box_size) # visualize train targets if self.args.vis_tgt: vis_data(images*255, targets) # inference with torch.cuda.amp.autocast(enabled=self.args.fp16): outputs = model(images) # loss loss_dict = self.criterion(outputs=outputs, targets=targets, epoch=self.epoch) losses = loss_dict['losses'] losses *= images.shape[0] # loss * bs # reduce loss_dict_reduced = distributed_utils.reduce_dict(loss_dict) # gradient averaged between devices in DDP mode losses *= distributed_utils.get_world_size() # backward self.scaler.scale(losses).backward() # Optimize if ni - self.last_opt_step >= accumulate: if self.clip_grad > 0: # unscale gradients self.scaler.unscale_(self.optimizer) # clip gradients torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=self.clip_grad) # optimizer.step self.scaler.step(self.optimizer) self.scaler.update() self.optimizer.zero_grad() # ema if self.model_ema is not None: self.model_ema.update(model) self.last_opt_step = ni # display if distributed_utils.is_main_process() and iter_i % 10 == 0: t1 = time.time() cur_lr = [param_group['lr'] for param_group in self.optimizer.param_groups] # basic infor log = '[Epoch: {}/{}]'.format(self.epoch+1, self.args.max_epoch) log += '[Iter: {}/{}]'.format(iter_i, epoch_size) log += '[lr: {:.6f}]'.format(cur_lr[2]) # loss infor for k in loss_dict_reduced.keys(): log += '[{}: {:.2f}]'.format(k, loss_dict_reduced[k]) # other infor log += '[time: {:.2f}]'.format(t1 - t0) log += '[size: {}]'.format(img_size) # print log infor print(log, flush=True) t0 = time.time() self.lr_scheduler.step() def refine_targets(self, targets, min_box_size): # rescale targets for tgt in targets: boxes = tgt["boxes"].clone() labels = tgt["labels"].clone() # refine tgt tgt_boxes_wh = boxes[..., 2:] - boxes[..., :2] min_tgt_size = torch.min(tgt_boxes_wh, dim=-1)[0] keep = (min_tgt_size >= min_box_size) tgt["boxes"] = boxes[keep] tgt["labels"] = labels[keep] return targets def rescale_image_targets(self, images, targets, stride, min_box_size, multi_scale_range=[0.5, 1.5]): """ Deployed for Multi scale trick. """ if isinstance(stride, int): max_stride = stride elif isinstance(stride, list): max_stride = max(stride) # During training phase, the shape of input image is square. old_img_size = images.shape[-1] new_img_size = random.randrange(old_img_size * multi_scale_range[0], old_img_size * multi_scale_range[1] + max_stride) new_img_size = new_img_size // max_stride * max_stride # size if new_img_size / old_img_size != 1: # interpolate images = torch.nn.functional.interpolate( input=images, size=new_img_size, mode='bilinear', align_corners=False) # rescale targets for tgt in targets: boxes = tgt["boxes"].clone() labels = tgt["labels"].clone() boxes = torch.clamp(boxes, 0, old_img_size) # rescale box boxes[:, [0, 2]] = boxes[:, [0, 2]] / old_img_size * new_img_size boxes[:, [1, 3]] = boxes[:, [1, 3]] / old_img_size * new_img_size # refine tgt tgt_boxes_wh = boxes[..., 2:] - boxes[..., :2] min_tgt_size = torch.min(tgt_boxes_wh, dim=-1)[0] keep = (min_tgt_size >= min_box_size) tgt["boxes"] = boxes[keep] tgt["labels"] = labels[keep] return images, targets, new_img_size # Trainer refered to RTMDet class RTMTrainer(object): def __init__(self, args, data_cfg, model_cfg, trans_cfg, device, model, criterion, world_size): # ------------------- basic parameters ------------------- self.args = args self.epoch = 0 self.best_map = -1. self.device = device self.criterion = criterion self.world_size = world_size self.no_aug_epoch = args.no_aug_epoch self.clip_grad = 35 self.heavy_eval = False self.optimizer_dict = {'optimizer': 'adamw', 'momentum': None, 'weight_decay': 5e-2, 'lr0': 0.001} self.ema_dict = {'ema_decay': 0.9998, 'ema_tau': 2000} self.lr_schedule_dict = {'scheduler': 'linear', 'lrf': 0.01} self.warmup_dict = {'warmup_momentum': 0.8, 'warmup_bias_lr': 0.1} # ---------------------------- Build Dataset & Model & Trans. Config ---------------------------- self.data_cfg = data_cfg self.model_cfg = model_cfg self.trans_cfg = trans_cfg # ---------------------------- Build Transform ---------------------------- self.train_transform, self.trans_cfg = build_transform( args=self.args, trans_config=self.trans_cfg, max_stride=self.model_cfg['max_stride'], is_train=True) self.val_transform, _ = build_transform( args=self.args, trans_config=self.trans_cfg, max_stride=self.model_cfg['max_stride'], is_train=False) # ---------------------------- Build Dataset & Dataloader ---------------------------- self.dataset, self.dataset_info = build_dataset(self.args, self.data_cfg, self.trans_cfg, self.train_transform, is_train=True) self.train_loader = build_dataloader(self.args, self.dataset, self.args.batch_size // self.world_size, CollateFunc()) # ---------------------------- Build Evaluator ---------------------------- self.evaluator = build_evluator(self.args, self.data_cfg, self.val_transform, self.device) # ---------------------------- Build Grad. Scaler ---------------------------- self.scaler = torch.cuda.amp.GradScaler(enabled=self.args.fp16) # ---------------------------- Build Optimizer ---------------------------- self.optimizer_dict['lr0'] *= self.args.batch_size / 64 self.optimizer, self.start_epoch = build_yolo_optimizer(self.optimizer_dict, model, self.args.resume) # ---------------------------- Build LR Scheduler ---------------------------- self.lr_scheduler, self.lf = build_lr_scheduler(self.lr_schedule_dict, self.optimizer, self.args.max_epoch) self.lr_scheduler.last_epoch = self.start_epoch - 1 # do not move if self.args.resume: self.lr_scheduler.step() # ---------------------------- Build Model-EMA ---------------------------- if self.args.ema and distributed_utils.get_rank() in [-1, 0]: print('Build ModelEMA ...') self.model_ema = ModelEMA(self.ema_dict, model, self.start_epoch * len(self.train_loader)) else: self.model_ema = None def train(self, model): for epoch in range(self.start_epoch, self.args.max_epoch): if self.args.distributed: self.train_loader.batch_sampler.sampler.set_epoch(epoch) # check second stage if epoch >= (self.args.max_epoch - self.no_aug_epoch - 1): # close mosaic augmentation if self.train_loader.dataset.mosaic_prob > 0.: print('close Mosaic Augmentation ...') self.train_loader.dataset.mosaic_prob = 0. self.heavy_eval = True # close mixup augmentation if self.train_loader.dataset.mixup_prob > 0.: print('close Mixup Augmentation ...') self.train_loader.dataset.mixup_prob = 0. self.heavy_eval = True # train one epoch self.epoch = epoch self.train_one_epoch(model) # eval one epoch if self.heavy_eval: model_eval = model.module if self.args.distributed else model self.eval(model_eval) else: model_eval = model.module if self.args.distributed else model if (epoch % self.args.eval_epoch) == 0 or (epoch == self.args.max_epoch - 1): self.eval(model_eval) def eval(self, model): # chech model model_eval = model if self.model_ema is None else self.model_ema.ema # path to save model path_to_save = os.path.join(self.args.save_folder, self.args.dataset, self.args.model) os.makedirs(path_to_save, exist_ok=True) if distributed_utils.is_main_process(): # check evaluator if self.evaluator is None: print('No evaluator ... save model and go on training.') print('Saving state, epoch: {}'.format(self.epoch + 1)) weight_name = '{}_no_eval.pth'.format(self.args.model) checkpoint_path = os.path.join(path_to_save, weight_name) torch.save({'model': model_eval.state_dict(), 'mAP': -1., 'optimizer': self.optimizer.state_dict(), 'epoch': self.epoch, 'args': self.args}, checkpoint_path) else: print('eval ...') # set eval mode model_eval.trainable = False model_eval.eval() # evaluate with torch.no_grad(): self.evaluator.evaluate(model_eval) # save model cur_map = self.evaluator.map if cur_map > self.best_map: # update best-map self.best_map = cur_map # save model print('Saving state, epoch:', self.epoch + 1) weight_name = '{}_best.pth'.format(self.args.model) checkpoint_path = os.path.join(path_to_save, weight_name) torch.save({'model': model_eval.state_dict(), 'mAP': round(self.best_map*100, 1), 'optimizer': self.optimizer.state_dict(), 'epoch': self.epoch, 'args': self.args}, checkpoint_path) # set train mode. model_eval.trainable = True model_eval.train() if self.args.distributed: # wait for all processes to synchronize dist.barrier() def train_one_epoch(self, model): # basic parameters epoch_size = len(self.train_loader) img_size = self.args.img_size t0 = time.time() nw = epoch_size * self.args.wp_epoch # Train one epoch for iter_i, (images, targets) in enumerate(self.train_loader): ni = iter_i + self.epoch * epoch_size # Warmup if ni <= nw: xi = [0, nw] # x interp for j, x in enumerate(self.optimizer.param_groups): # bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0 x['lr'] = np.interp( ni, xi, [self.warmup_dict['warmup_bias_lr'] if j == 0 else 0.0, x['initial_lr'] * self.lf(self.epoch)]) if 'momentum' in x: x['momentum'] = np.interp(ni, xi, [self.warmup_dict['warmup_momentum'], self.optimizer_dict['momentum']]) # To device images = images.to(self.device, non_blocking=True).float() / 255. # Multi scale if self.args.multi_scale: images, targets, img_size = self.rescale_image_targets( images, targets, self.model_cfg['stride'], self.args.min_box_size, self.model_cfg['multi_scale']) else: targets = self.refine_targets(targets, self.args.min_box_size) # Visualize train targets if self.args.vis_tgt: vis_data(images*255, targets) # Inference with torch.cuda.amp.autocast(enabled=self.args.fp16): outputs = model(images) # Compute loss loss_dict = self.criterion(outputs=outputs, targets=targets, epoch=self.epoch) losses = loss_dict['losses'] loss_dict_reduced = distributed_utils.reduce_dict(loss_dict) # Backward self.scaler.scale(losses).backward() # Optimize if self.clip_grad > 0: # unscale gradients self.scaler.unscale_(self.optimizer) # clip gradients torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=self.clip_grad) # optimizer.step self.scaler.step(self.optimizer) self.scaler.update() self.optimizer.zero_grad() # ema if self.model_ema is not None: self.model_ema.update(model) # Logs if distributed_utils.is_main_process() and iter_i % 10 == 0: t1 = time.time() cur_lr = [param_group['lr'] for param_group in self.optimizer.param_groups] # basic infor log = '[Epoch: {}/{}]'.format(self.epoch+1, self.args.max_epoch) log += '[Iter: {}/{}]'.format(iter_i, epoch_size) log += '[lr: {:.6f}]'.format(cur_lr[2]) # loss infor for k in loss_dict_reduced.keys(): log += '[{}: {:.2f}]'.format(k, loss_dict_reduced[k]) # other infor log += '[time: {:.2f}]'.format(t1 - t0) log += '[size: {}]'.format(img_size) # print log infor print(log, flush=True) t0 = time.time() # LR Schedule self.lr_scheduler.step() def refine_targets(self, targets, min_box_size): # rescale targets for tgt in targets: boxes = tgt["boxes"].clone() labels = tgt["labels"].clone() # refine tgt tgt_boxes_wh = boxes[..., 2:] - boxes[..., :2] min_tgt_size = torch.min(tgt_boxes_wh, dim=-1)[0] keep = (min_tgt_size >= min_box_size) tgt["boxes"] = boxes[keep] tgt["labels"] = labels[keep] return targets def rescale_image_targets(self, images, targets, stride, min_box_size, multi_scale_range=[0.5, 1.5]): """ Deployed for Multi scale trick. """ if isinstance(stride, int): max_stride = stride elif isinstance(stride, list): max_stride = max(stride) # During training phase, the shape of input image is square. old_img_size = images.shape[-1] new_img_size = random.randrange(old_img_size * multi_scale_range[0], old_img_size * multi_scale_range[1] + max_stride) new_img_size = new_img_size // max_stride * max_stride # size if new_img_size / old_img_size != 1: # interpolate images = torch.nn.functional.interpolate( input=images, size=new_img_size, mode='bilinear', align_corners=False) # rescale targets for tgt in targets: boxes = tgt["boxes"].clone() labels = tgt["labels"].clone() boxes = torch.clamp(boxes, 0, old_img_size) # rescale box boxes[:, [0, 2]] = boxes[:, [0, 2]] / old_img_size * new_img_size boxes[:, [1, 3]] = boxes[:, [1, 3]] / old_img_size * new_img_size # refine tgt tgt_boxes_wh = boxes[..., 2:] - boxes[..., :2] min_tgt_size = torch.min(tgt_boxes_wh, dim=-1)[0] keep = (min_tgt_size >= min_box_size) tgt["boxes"] = boxes[keep] tgt["labels"] = labels[keep] return images, targets, new_img_size # Trainer for DETR class DetrTrainer(object): def __init__(self, args, data_cfg, model_cfg, trans_cfg, device, model, criterion, world_size): # ------------------- basic parameters ------------------- self.args = args self.epoch = 0 self.best_map = -1. self.last_opt_step = 0 self.no_aug_epoch = args.no_aug_epoch self.clip_grad = -1 self.device = device self.criterion = criterion self.world_size = world_size self.heavy_eval = False self.optimizer_dict = {'optimizer': 'adamw', 'momentum': None, 'weight_decay': 1e-4, 'lr0': 0.0001} self.ema_dict = {'ema_decay': 0.9998, 'ema_tau': 2000} self.lr_schedule_dict = {'scheduler': 'linear', 'lrf': 0.1} self.warmup_dict = {'warmup_momentum': 0.8, 'warmup_bias_lr': 0.1} # ---------------------------- Build Dataset & Model & Trans. Config ---------------------------- self.data_cfg = data_cfg self.model_cfg = model_cfg self.trans_cfg = trans_cfg # ---------------------------- Build Transform ---------------------------- self.train_transform, self.trans_cfg = build_transform( args=self.args, trans_config=self.trans_cfg, max_stride=self.model_cfg['max_stride'], is_train=True) self.val_transform, _ = build_transform( args=self.args, trans_config=self.trans_cfg, max_stride=self.model_cfg['max_stride'], is_train=False) # ---------------------------- Build Dataset & Dataloader ---------------------------- self.dataset, self.dataset_info = build_dataset(self.args, self.data_cfg, self.trans_cfg, self.train_transform, is_train=True) self.train_loader = build_dataloader(self.args, self.dataset, self.args.batch_size // self.world_size, CollateFunc()) # ---------------------------- Build Evaluator ---------------------------- self.evaluator = build_evluator(self.args, self.data_cfg, self.val_transform, self.device) # ---------------------------- Build Grad. Scaler ---------------------------- self.scaler = torch.cuda.amp.GradScaler(enabled=self.args.fp16) # ---------------------------- Build Optimizer ---------------------------- self.optimizer_dict['lr0'] *= self.args.batch_size / 16. self.optimizer, self.start_epoch = build_detr_optimizer(self.optimizer_dict, model, self.args.resume) # ---------------------------- Build LR Scheduler ---------------------------- self.lr_scheduler, self.lf = build_lr_scheduler(self.lr_schedule_dict, self.optimizer, self.args.max_epoch) self.lr_scheduler.last_epoch = self.start_epoch - 1 # do not move if self.args.resume: self.lr_scheduler.step() # ---------------------------- Build Model-EMA ---------------------------- if self.args.ema and distributed_utils.get_rank() in [-1, 0]: print('Build ModelEMA ...') self.model_ema = ModelEMA(self.ema_dict, model, self.start_epoch * len(self.train_loader)) else: self.model_ema = None def train(self, model): for epoch in range(self.start_epoch, self.args.max_epoch): if self.args.distributed: self.train_loader.batch_sampler.sampler.set_epoch(epoch) # check second stage if epoch >= (self.args.max_epoch - self.no_aug_epoch - 1): # close mosaic augmentation if self.train_loader.dataset.mosaic_prob > 0.: print('close Mosaic Augmentation ...') self.train_loader.dataset.mosaic_prob = 0. self.heavy_eval = True # close mixup augmentation if self.train_loader.dataset.mixup_prob > 0.: print('close Mixup Augmentation ...') self.train_loader.dataset.mixup_prob = 0. self.heavy_eval = True # train one epoch self.epoch = epoch self.train_one_epoch(model) # eval one epoch if self.heavy_eval: model_eval = model.module if self.args.distributed else model self.eval(model_eval) else: model_eval = model.module if self.args.distributed else model if (epoch % self.args.eval_epoch) == 0 or (epoch == self.args.max_epoch - 1): self.eval(model_eval) def eval(self, model): # chech model model_eval = model if self.model_ema is None else self.model_ema.ema # path to save model path_to_save = os.path.join(self.args.save_folder, self.args.dataset, self.args.model) os.makedirs(path_to_save, exist_ok=True) if distributed_utils.is_main_process(): # check evaluator if self.evaluator is None: print('No evaluator ... save model and go on training.') print('Saving state, epoch: {}'.format(self.epoch + 1)) weight_name = '{}_no_eval.pth'.format(self.args.model) checkpoint_path = os.path.join(path_to_save, weight_name) torch.save({'model': model_eval.state_dict(), 'mAP': -1., 'optimizer': self.optimizer.state_dict(), 'epoch': self.epoch, 'args': self.args}, checkpoint_path) else: print('eval ...') # set eval mode model_eval.trainable = False model_eval.eval() # evaluate with torch.no_grad(): self.evaluator.evaluate(model_eval) # save model cur_map = self.evaluator.map if cur_map > self.best_map: # update best-map self.best_map = cur_map # save model print('Saving state, epoch:', self.epoch + 1) weight_name = '{}_best.pth'.format(self.args.model) checkpoint_path = os.path.join(path_to_save, weight_name) torch.save({'model': model_eval.state_dict(), 'mAP': round(self.best_map*100, 1), 'optimizer': self.optimizer.state_dict(), 'epoch': self.epoch, 'args': self.args}, checkpoint_path) # set train mode. model_eval.trainable = True model_eval.train() if self.args.distributed: # wait for all processes to synchronize dist.barrier() def train_one_epoch(self, model): # basic parameters epoch_size = len(self.train_loader) img_size = self.args.img_size t0 = time.time() nw = epoch_size * self.args.wp_epoch # train one epoch for iter_i, (images, targets) in enumerate(self.train_loader): ni = iter_i + self.epoch * epoch_size # Warmup if ni <= nw: xi = [0, nw] # x interp for j, x in enumerate(self.optimizer.param_groups): # bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0 x['lr'] = np.interp( ni, xi, [0.0, x['initial_lr'] * self.lf(self.epoch)]) if 'momentum' in x: x['momentum'] = np.interp(ni, xi, [self.model_cfg['warmup_momentum'], self.model_cfg['momentum']]) # To device images = images.to(self.device, non_blocking=True).float() / 255. # Multi scale if self.args.multi_scale: images, targets, img_size = self.rescale_image_targets( images, targets, self.model_cfg['stride'], self.args.min_box_size, self.model_cfg['multi_scale']) else: targets = self.refine_targets(targets, self.args.min_box_size, img_size) # Visualize targets if self.args.vis_tgt: vis_data(images*255, targets) # Inference with torch.cuda.amp.autocast(enabled=self.args.fp16): outputs = model(images) # Compute loss loss_dict = self.criterion(outputs=outputs, targets=targets, epoch=self.epoch) losses = loss_dict['losses'] loss_dict_reduced = distributed_utils.reduce_dict(loss_dict) # Backward self.scaler.scale(losses).backward() # Optimize if self.clip_grad > 0: # unscale gradients self.scaler.unscale_(self.optimizer) # clip gradients torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=self.clip_grad) self.scaler.step(self.optimizer) self.scaler.update() self.optimizer.zero_grad() # Model EMA if self.model_ema is not None: self.model_ema.update(model) self.last_opt_step = ni # Log if distributed_utils.is_main_process() and iter_i % 10 == 0: t1 = time.time() cur_lr = [param_group['lr'] for param_group in self.optimizer.param_groups] # basic infor log = '[Epoch: {}/{}]'.format(self.epoch+1, self.args.max_epoch) log += '[Iter: {}/{}]'.format(iter_i, epoch_size) log += '[lr: {:.6f}]'.format(cur_lr[0]) # loss infor for k in loss_dict_reduced.keys(): if self.args.vis_aux_loss: log += '[{}: {:.2f}]'.format(k, loss_dict_reduced[k]) else: if k in ['loss_cls', 'loss_bbox', 'loss_giou', 'losses']: log += '[{}: {:.2f}]'.format(k, loss_dict_reduced[k]) # other infor log += '[time: {:.2f}]'.format(t1 - t0) log += '[size: {}]'.format(img_size) # print log infor print(log, flush=True) t0 = time.time() # LR Scheduler self.lr_scheduler.step() def refine_targets(self, targets, min_box_size, img_size): # rescale targets for tgt in targets: boxes = tgt["boxes"] labels = tgt["labels"] # refine tgt tgt_boxes_wh = boxes[..., 2:] - boxes[..., :2] min_tgt_size = torch.min(tgt_boxes_wh, dim=-1)[0] keep = (min_tgt_size >= min_box_size) # xyxy -> cxcywh new_boxes = torch.zeros_like(boxes) new_boxes[..., :2] = (boxes[..., 2:] + boxes[..., :2]) * 0.5 new_boxes[..., 2:] = (boxes[..., 2:] - boxes[..., :2]) # normalize new_boxes /= img_size del boxes tgt["boxes"] = new_boxes[keep] tgt["labels"] = labels[keep] return targets def rescale_image_targets(self, images, targets, stride, min_box_size, multi_scale_range=[0.5, 1.5]): """ Deployed for Multi scale trick. """ if isinstance(stride, int): max_stride = stride elif isinstance(stride, list): max_stride = max(stride) # During training phase, the shape of input image is square. old_img_size = images.shape[-1] new_img_size = random.randrange(old_img_size * multi_scale_range[0], old_img_size * multi_scale_range[1] + max_stride) new_img_size = new_img_size // max_stride * max_stride # size if new_img_size / old_img_size != 1: # interpolate images = torch.nn.functional.interpolate( input=images, size=new_img_size, mode='bilinear', align_corners=False) # rescale targets for tgt in targets: boxes = tgt["boxes"].clone() labels = tgt["labels"].clone() boxes = torch.clamp(boxes, 0, old_img_size) # rescale box boxes[:, [0, 2]] = boxes[:, [0, 2]] / old_img_size * new_img_size boxes[:, [1, 3]] = boxes[:, [1, 3]] / old_img_size * new_img_size # refine tgt tgt_boxes_wh = boxes[..., 2:] - boxes[..., :2] min_tgt_size = torch.min(tgt_boxes_wh, dim=-1)[0] keep = (min_tgt_size >= min_box_size) # xyxy -> cxcywh new_boxes = torch.zeros_like(boxes) new_boxes[..., :2] = (boxes[..., 2:] + boxes[..., :2]) * 0.5 new_boxes[..., 2:] = (boxes[..., 2:] - boxes[..., :2]) # normalize new_boxes /= new_img_size del boxes tgt["boxes"] = new_boxes[keep] tgt["labels"] = labels[keep] return images, targets, new_img_size # Build Trainer def build_trainer(args, data_cfg, model_cfg, trans_cfg, device, model, criterion, world_size): if model_cfg['trainer_type'] == 'yolo': return YoloTrainer(args, data_cfg, model_cfg, trans_cfg, device, model, criterion, world_size) elif model_cfg['trainer_type'] == 'rtmdet': return RTMTrainer(args, data_cfg, model_cfg, trans_cfg, device, model, criterion, world_size) elif model_cfg['trainer_type'] == 'detr': return DetrTrainer(args, data_cfg, model_cfg, trans_cfg, device, model, criterion, world_size) else: raise NotImplementedError