# Modified by Peize Sun, Rufeng Zhang # ------------------------------------------------------------------------ # Deformable DETR # Copyright (c) 2020 SenseTime. All Rights Reserved. # Licensed under the Apache License, Version 2.0 [see LICENSE for details] # ------------------------------------------------------------------------ # Modified from DETR (https://github.com/facebookresearch/detr) # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved # ------------------------------------------------------------------------ """ Train and eval functions used in main.py """ import math import os import sys from typing import Iterable import torch import util.misc as utils from datasets.coco_eval import CocoEvaluator from datasets.panoptic_eval import PanopticEvaluator from datasets.data_prefetcher import data_prefetcher from mot_online.byte_tracker import BYTETracker def train_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module, data_loader: Iterable, optimizer: torch.optim.Optimizer, device: torch.device, epoch: int, max_norm: float = 0): model.train() criterion.train() metric_logger = utils.MetricLogger(delimiter=" ") metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}')) metric_logger.add_meter('class_error', utils.SmoothedValue(window_size=1, fmt='{value:.2f}')) metric_logger.add_meter('grad_norm', utils.SmoothedValue(window_size=1, fmt='{value:.2f}')) header = 'Epoch: [{}]'.format(epoch) print_freq = 10 prefetcher = data_prefetcher(data_loader, device, prefetch=True) samples, targets = prefetcher.next() # for samples, targets in metric_logger.log_every(data_loader, print_freq, header): for _ in metric_logger.log_every(range(len(data_loader)), print_freq, header): outputs, pre_outputs, pre_targets = model([samples, targets]) loss_dict = criterion(outputs, targets, pre_outputs, pre_targets) weight_dict = criterion.weight_dict losses = sum(loss_dict[k] * weight_dict[k] for k in loss_dict.keys() if k in weight_dict) # reduce losses over all GPUs for logging purposes loss_dict_reduced = utils.reduce_dict(loss_dict) loss_dict_reduced_unscaled = {f'{k}_unscaled': v for k, v in loss_dict_reduced.items()} loss_dict_reduced_scaled = {k: v * weight_dict[k] for k, v in loss_dict_reduced.items() if k in weight_dict} losses_reduced_scaled = sum(loss_dict_reduced_scaled.values()) loss_value = losses_reduced_scaled.item() if not math.isfinite(loss_value): print("Loss is {}, stopping training".format(loss_value)) print(loss_dict_reduced) sys.exit(1) optimizer.zero_grad() losses.backward() if max_norm > 0: grad_total_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm) else: grad_total_norm = utils.get_total_grad_norm(model.parameters(), max_norm) optimizer.step() metric_logger.update(loss=loss_value, **loss_dict_reduced_scaled, **loss_dict_reduced_unscaled) metric_logger.update(class_error=loss_dict_reduced['class_error']) metric_logger.update(lr=optimizer.param_groups[0]["lr"]) metric_logger.update(grad_norm=grad_total_norm) samples, targets = prefetcher.next() # gather the stats from all processes metric_logger.synchronize_between_processes() print("Averaged stats:", metric_logger) return {k: meter.global_avg for k, meter in metric_logger.meters.items()} @torch.no_grad() def evaluate(model, criterion, postprocessors, data_loader, base_ds, device, output_dir, tracker=None, phase='train', det_val=False): model.eval() criterion.eval() metric_logger = utils.MetricLogger(delimiter=" ") metric_logger.add_meter('class_error', utils.SmoothedValue(window_size=1, fmt='{value:.2f}')) header = 'Test:' iou_types = tuple(k for k in ('segm', 'bbox') if k in postprocessors.keys()) coco_evaluator = CocoEvaluator(base_ds, iou_types) # coco_evaluator.coco_eval[iou_types[0]].params.iouThrs = [0, 0.1, 0.5, 0.75] panoptic_evaluator = None if 'panoptic' in postprocessors.keys(): panoptic_evaluator = PanopticEvaluator( data_loader.dataset.ann_file, data_loader.dataset.ann_folder, output_dir=os.path.join(output_dir, "panoptic_eval"), ) res_tracks = dict() pre_embed = None for samples, targets in metric_logger.log_every(data_loader, 10, header): # pre process for track. if tracker is not None: if phase != 'train': assert samples.tensors.shape[0] == 1, "Now only support inference of batchsize 1." frame_id = targets[0].get("frame_id", None) assert frame_id is not None frame_id = frame_id.item() if frame_id == 1: tracker.reset_all() pre_embed = None samples = samples.to(device) targets = [{k: v.to(device) for k, v in t.items()} for t in targets] if det_val: outputs = model(samples) else: outputs, pre_embed = model(samples, pre_embed) loss_dict = criterion(outputs, targets) weight_dict = criterion.weight_dict # reduce losses over all GPUs for logging purposes loss_dict_reduced = utils.reduce_dict(loss_dict) loss_dict_reduced_scaled = {k: v * weight_dict[k] for k, v in loss_dict_reduced.items() if k in weight_dict} loss_dict_reduced_unscaled = {f'{k}_unscaled': v for k, v in loss_dict_reduced.items()} metric_logger.update(loss=sum(loss_dict_reduced_scaled.values()), **loss_dict_reduced_scaled, **loss_dict_reduced_unscaled) metric_logger.update(class_error=loss_dict_reduced['class_error']) orig_target_sizes = torch.stack([t["orig_size"] for t in targets], dim=0) results = postprocessors['bbox'](outputs, orig_target_sizes) if 'segm' in postprocessors.keys(): target_sizes = torch.stack([t["size"] for t in targets], dim=0) results = postprocessors['segm'](results, outputs, orig_target_sizes, target_sizes) res = {target['image_id'].item(): output for target, output in zip(targets, results)} # post process for track. if tracker is not None: if frame_id == 1: res_track = tracker.init_track(results[0]) else: res_track = tracker.step(results[0]) res_tracks[targets[0]['image_id'].item()] = res_track if coco_evaluator is not None: coco_evaluator.update(res) if panoptic_evaluator is not None: res_pano = postprocessors["panoptic"](outputs, target_sizes, orig_target_sizes) for i, target in enumerate(targets): image_id = target["image_id"].item() file_name = f"{image_id:012d}.png" res_pano[i]["image_id"] = image_id res_pano[i]["file_name"] = file_name panoptic_evaluator.update(res_pano) # gather the stats from all processes metric_logger.synchronize_between_processes() print("Averaged stats:", metric_logger) if coco_evaluator is not None: coco_evaluator.synchronize_between_processes() if panoptic_evaluator is not None: panoptic_evaluator.synchronize_between_processes() # accumulate predictions from all images if coco_evaluator is not None: coco_evaluator.accumulate() coco_evaluator.summarize() panoptic_res = None if panoptic_evaluator is not None: panoptic_res = panoptic_evaluator.summarize() stats = {k: meter.global_avg for k, meter in metric_logger.meters.items()} if coco_evaluator is not None: if 'bbox' in postprocessors.keys(): stats['coco_eval_bbox'] = coco_evaluator.coco_eval['bbox'].stats.tolist() if 'segm' in postprocessors.keys(): stats['coco_eval_masks'] = coco_evaluator.coco_eval['segm'].stats.tolist() if panoptic_res is not None: stats['PQ_all'] = panoptic_res["All"] stats['PQ_th'] = panoptic_res["Things"] stats['PQ_st'] = panoptic_res["Stuff"] return stats, coco_evaluator, res_tracks @torch.no_grad() def evaluate_track(args, model, criterion, postprocessors, data_loader, base_ds, device, output_dir, tracker=None, phase='train', det_val=False): model.eval() criterion.eval() metric_logger = utils.MetricLogger(delimiter=" ") metric_logger.add_meter('class_error', utils.SmoothedValue(window_size=1, fmt='{value:.2f}')) header = 'Test:' iou_types = tuple(k for k in ('segm', 'bbox') if k in postprocessors.keys()) coco_evaluator = CocoEvaluator(base_ds, iou_types) # coco_evaluator.coco_eval[iou_types[0]].params.iouThrs = [0, 0.1, 0.5, 0.75] res_tracks = dict() pre_embed = None for samples, targets in metric_logger.log_every(data_loader, 50, header): # pre process for track. if tracker is not None: frame_id = targets[0].get("frame_id", None) assert frame_id is not None frame_id = frame_id.item() if frame_id == 1: tracker = BYTETracker(args) pre_embed = None samples = samples.to(device) targets = [{k: v.to(device) for k, v in t.items()} for t in targets] if det_val: outputs = model(samples) else: outputs, pre_embed = model(samples, pre_embed) loss_dict = criterion(outputs, targets) weight_dict = criterion.weight_dict # reduce losses over all GPUs for logging purposes loss_dict_reduced = utils.reduce_dict(loss_dict) loss_dict_reduced_scaled = {k: v * weight_dict[k] for k, v in loss_dict_reduced.items() if k in weight_dict} loss_dict_reduced_unscaled = {f'{k}_unscaled': v for k, v in loss_dict_reduced.items()} metric_logger.update(loss=sum(loss_dict_reduced_scaled.values()), **loss_dict_reduced_scaled, **loss_dict_reduced_unscaled) metric_logger.update(class_error=loss_dict_reduced['class_error']) orig_target_sizes = torch.stack([t["orig_size"] for t in targets], dim=0) results = postprocessors['bbox'](outputs, orig_target_sizes) if 'segm' in postprocessors.keys(): target_sizes = torch.stack([t["size"] for t in targets], dim=0) results = postprocessors['segm'](results, outputs, orig_target_sizes, target_sizes) res = {target['image_id'].item(): output for target, output in zip(targets, results)} # post process for track. if tracker is not None: res_track = tracker.update(results[0]) res_tracks[targets[0]['image_id'].item()] = res_track if coco_evaluator is not None: coco_evaluator.update(res) # gather the stats from all processes metric_logger.synchronize_between_processes() print("Averaged stats:", metric_logger) if coco_evaluator is not None: coco_evaluator.synchronize_between_processes() # accumulate predictions from all images if coco_evaluator is not None: coco_evaluator.accumulate() coco_evaluator.summarize() stats = {k: meter.global_avg for k, meter in metric_logger.meters.items()} if coco_evaluator is not None: if 'bbox' in postprocessors.keys(): stats['coco_eval_bbox'] = coco_evaluator.coco_eval['bbox'].stats.tolist() if 'segm' in postprocessors.keys(): stats['coco_eval_masks'] = coco_evaluator.coco_eval['segm'].stats.tolist() return stats, coco_evaluator, res_tracks