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engine.py
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engine.py
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# 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
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
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
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
evaluate_rel_batch(outputs, targets, evaluator, evaluator_list)
# 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'])
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)
evaluator['sgdet'].print_stats()
if args.eval:
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)