# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved # Copyright (c) Institute of Information Processing, Leibniz University Hannover. """ Train and eval functions used in main.py """ import math import sys from typing import Iterable import numpy as np import torch from datasets.coco_eval import CocoEvaluator import util.misc as utils from util.box_ops import rescale_bboxes from lib.evaluation.sg_eval import BasicSceneGraphEvaluator, calculate_mR_from_evaluator_list from lib.openimages_evaluation import task_evaluation_sg 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('sub_error', utils.SmoothedValue(window_size=1, fmt='{value:.2f}')) metric_logger.add_meter('obj_error', utils.SmoothedValue(window_size=1, fmt='{value:.2f}')) metric_logger.add_meter('rel_error', utils.SmoothedValue(window_size=1, fmt='{value:.2f}')) header = 'Epoch: [{}]'.format(epoch) print_freq = 500 for samples, targets in metric_logger.log_every(data_loader, print_freq, header): samples = samples.to(device) targets = [{k: v.to(device) for k, v in t.items()} for t in targets] outputs = model(samples) loss_dict = criterion(outputs, 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: torch.nn.utils.clip_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(sub_error=loss_dict_reduced['sub_error']) metric_logger.update(obj_error=loss_dict_reduced['obj_error']) metric_logger.update(rel_error=loss_dict_reduced['rel_error']) metric_logger.update(lr=optimizer.param_groups[0]["lr"]) # 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, args): model.eval() criterion.eval() metric_logger = utils.MetricLogger(delimiter=" ") metric_logger.add_meter('class_error', utils.SmoothedValue(window_size=1, fmt='{value:.2f}')) metric_logger.add_meter('sub_error', utils.SmoothedValue(window_size=1, fmt='{value:.2f}')) metric_logger.add_meter('obj_error', utils.SmoothedValue(window_size=1, fmt='{value:.2f}')) metric_logger.add_meter('rel_error', utils.SmoothedValue(window_size=1, fmt='{value:.2f}')) header = 'Test:' # initilize evaluator # TODO merge evaluation programs if args.dataset == 'vg': evaluator = BasicSceneGraphEvaluator.all_modes(multiple_preds=False) if args.eval: evaluator_list = [] for index, name in enumerate(data_loader.dataset.rel_categories): if index == 0: continue evaluator_list.append((index, name, BasicSceneGraphEvaluator.all_modes())) else: evaluator_list = None else: all_results = [] iou_types = tuple(k for k in ('segm', 'bbox') if k in postprocessors.keys()) coco_evaluator = CocoEvaluator(base_ds, iou_types) for samples, targets in metric_logger.log_every(data_loader, 100, header): samples = samples.to(device) targets = [{k: v.to(device) for k, v in t.items()} for t in targets] outputs = model(samples) 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']) metric_logger.update(sub_error=loss_dict_reduced['sub_error']) metric_logger.update(obj_error=loss_dict_reduced['obj_error']) metric_logger.update(rel_error=loss_dict_reduced['rel_error']) if args.dataset == 'vg': evaluate_rel_batch(outputs, targets, evaluator, evaluator_list) else: evaluate_rel_batch_oi(outputs, targets, all_results) orig_target_sizes = torch.stack([t["orig_size"] for t in targets], dim=0) results = postprocessors['bbox'](outputs, orig_target_sizes) res = {target['image_id'].item(): output for target, output in zip(targets, results)} if coco_evaluator is not None: coco_evaluator.update(res) if args.dataset == 'vg': evaluator['sgdet'].print_stats() else: task_evaluation_sg.eval_rel_results(all_results, 100, do_val=True, do_vis=False) if args.eval and args.dataset == 'vg': calculate_mR_from_evaluator_list(evaluator_list, 'sgdet') # 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() return stats, coco_evaluator def evaluate_rel_batch(outputs, targets, evaluator, evaluator_list): for batch, target in enumerate(targets): target_bboxes_scaled = rescale_bboxes(target['boxes'].cpu(), torch.flip(target['orig_size'],dims=[0]).cpu()).clone().numpy() # recovered boxes with original size gt_entry = {'gt_classes': target['labels'].cpu().clone().numpy(), 'gt_relations': target['rel_annotations'].cpu().clone().numpy(), 'gt_boxes': target_bboxes_scaled} sub_bboxes_scaled = rescale_bboxes(outputs['sub_boxes'][batch].cpu(), torch.flip(target['orig_size'],dims=[0]).cpu()).clone().numpy() obj_bboxes_scaled = rescale_bboxes(outputs['obj_boxes'][batch].cpu(), torch.flip(target['orig_size'],dims=[0]).cpu()).clone().numpy() pred_sub_scores, pred_sub_classes = torch.max(outputs['sub_logits'][batch].softmax(-1)[:, :-1], dim=1) pred_obj_scores, pred_obj_classes = torch.max(outputs['obj_logits'][batch].softmax(-1)[:, :-1], dim=1) rel_scores = outputs['rel_logits'][batch][:,1:-1].softmax(-1) pred_entry = {'sub_boxes': sub_bboxes_scaled, 'sub_classes': pred_sub_classes.cpu().clone().numpy(), 'sub_scores': pred_sub_scores.cpu().clone().numpy(), 'obj_boxes': obj_bboxes_scaled, 'obj_classes': pred_obj_classes.cpu().clone().numpy(), 'obj_scores': pred_obj_scores.cpu().clone().numpy(), 'rel_scores': rel_scores.cpu().clone().numpy()} evaluator['sgdet'].evaluate_scene_graph_entry(gt_entry, pred_entry) if evaluator_list is not None: for pred_id, _, evaluator_rel in evaluator_list: gt_entry_rel = gt_entry.copy() mask = np.in1d(gt_entry_rel['gt_relations'][:, -1], pred_id) gt_entry_rel['gt_relations'] = gt_entry_rel['gt_relations'][mask, :] if gt_entry_rel['gt_relations'].shape[0] == 0: continue evaluator_rel['sgdet'].evaluate_scene_graph_entry(gt_entry_rel, pred_entry) def evaluate_rel_batch_oi(outputs, targets, all_results): for batch, target in enumerate(targets): target_bboxes_scaled = rescale_bboxes(target['boxes'].cpu(), torch.flip(target['orig_size'],dims=[0]).cpu()).clone().numpy() # recovered boxes with original size sub_bboxes_scaled = rescale_bboxes(outputs['sub_boxes'][batch].cpu(), torch.flip(target['orig_size'],dims=[0]).cpu()).clone().numpy() obj_bboxes_scaled = rescale_bboxes(outputs['obj_boxes'][batch].cpu(), torch.flip(target['orig_size'],dims=[0]).cpu()).clone().numpy() pred_sub_scores, pred_sub_classes = torch.max(outputs['sub_logits'][batch].softmax(-1)[:, :-1], dim=1) pred_obj_scores, pred_obj_classes = torch.max(outputs['obj_logits'][batch].softmax(-1)[:, :-1], dim=1) rel_scores = outputs['rel_logits'][batch][:, :-1].softmax(-1) relation_idx = target['rel_annotations'].cpu().numpy() gt_sub_boxes = target_bboxes_scaled[relation_idx[:, 0]] gt_sub_labels = target['labels'][relation_idx[:, 0]].cpu().clone().numpy() gt_obj_boxes = target_bboxes_scaled[relation_idx[:, 1]] gt_obj_labels = target['labels'][relation_idx[:, 1]].cpu().clone().numpy() img_result_dict = {'sbj_boxes': sub_bboxes_scaled, 'sbj_labels': pred_sub_classes.cpu().clone().numpy(), 'sbj_scores': pred_sub_scores.cpu().clone().numpy(), 'obj_boxes': obj_bboxes_scaled, 'obj_labels': pred_obj_classes.cpu().clone().numpy(), 'obj_scores': pred_obj_scores.cpu().clone().numpy(), 'prd_scores': rel_scores.cpu().clone().numpy(), 'image': str(target['image_id'].item())+'.jpg', 'gt_sbj_boxes': gt_sub_boxes, 'gt_sbj_labels': gt_sub_labels, 'gt_obj_boxes': gt_obj_boxes, 'gt_obj_labels': gt_obj_labels, 'gt_prd_labels': relation_idx[:, 2] } all_results.append(img_result_dict)