engine.py

# 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)