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"""
Train and eval functions used in main.py
Modified from DETR (https://github.com/facebookresearch/detr)
"""
import math
from models import postprocessors
import os
import sys
from typing import Iterable
import torch
import torch.distributed as dist
import util.misc as utils
from datasets.coco_eval import CocoEvaluator
from datasets.refexp_eval import RefExpEvaluator
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
from datasets.a2d_eval import calculate_precision_at_k_and_iou_metrics, calculate_bbox_precision_at_k_and_iou_metrics
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}'))
header = 'Epoch: [{}]'.format(epoch)
print_freq = 10
for samples, targets in metric_logger.log_every(data_loader, print_freq, header):
samples = samples.to(device)
captions = [t["caption"] for t in targets]
targets = utils.targets_to(targets, device)
outputs = model(samples, captions, targets)
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:
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(lr=optimizer.param_groups[0]["lr"])
metric_logger.update(grad_norm=grad_total_norm)
# 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, evaluator_list, device, args):
model.eval()
criterion.eval()
metric_logger = utils.MetricLogger(delimiter=" ")
header = 'Test:'
predictions = []
for samples, targets in metric_logger.log_every(data_loader, 10, header):
dataset_name = targets[0]["dataset_name"]
samples = samples.to(device)
captions = [t["caption"] for t in targets]
targets = utils.targets_to(targets, device)
outputs = model(samples, captions, targets)
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)
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)}
for evaluator in evaluator_list:
evaluator.update(res)
# REC & RES predictions
for p, target in zip(results, targets):
for s, b, m in zip(p['scores'], p['boxes'], p['rle_masks']):
predictions.append({'image_id': target['image_id'].item(),
'category_id': 1, # dummy label, as categories are not predicted in ref-vos
'bbox': b.tolist(),
'segmentation': m,
'score': s.item()})
# gather the stats from all processes
metric_logger.synchronize_between_processes()
print("Averaged stats:", metric_logger)
for evaluator in evaluator_list:
evaluator.synchronize_between_processes()
# accumulate predictions from all images
refexp_res = None
for evaluator in evaluator_list:
if isinstance(evaluator, CocoEvaluator):
evaluator.accumulate()
evaluator.summarize()
elif isinstance(evaluator, RefExpEvaluator):
refexp_res = evaluator.summarize()
stats = {k: meter.global_avg for k, meter in metric_logger.meters.items()}
# update stats
for evaluator in evaluator_list:
if isinstance(evaluator, CocoEvaluator):
if "bbox" in postprocessors.keys():
stats["coco_eval_bbox"] = evaluator.coco_eval["bbox"].stats.tolist()
if "segm" in postprocessors.keys():
stats["coco_eval_masks"] = evaluator.coco_eval["segm"].stats.tolist()
if refexp_res is not None:
stats.update(refexp_res)
# evaluate RES
# gather and merge predictions from all gpus
gathered_pred_lists = utils.all_gather(predictions)
predictions = [p for p_list in gathered_pred_lists for p in p_list]
eval_metrics = {}
if utils.is_main_process():
if dataset_name == 'refcoco':
coco_gt = COCO(os.path.join(args.coco_path, 'refcoco/instances_refcoco_val.json'))
elif dataset_name == 'refcoco+':
coco_gt = COCO(os.path.join(args.coco_path, 'refcoco+/instances_refcoco+_val.json'))
elif dataset_name == 'refcocog':
coco_gt = COCO(os.path.join(args.coco_path, 'refcocog/instances_refcocog_val.json'))
else:
raise NotImplementedError
coco_pred = coco_gt.loadRes(predictions)
coco_eval = COCOeval(coco_gt, coco_pred, iouType='segm')
coco_eval.params.useCats = 0 # ignore categories as they are not predicted in ref-vos task
coco_eval.evaluate()
coco_eval.accumulate()
coco_eval.summarize()
# ap_labels = ['mAP 0.5:0.95', 'AP 0.5', 'AP 0.75', 'AP 0.5:0.95 S', 'AP 0.5:0.95 M', 'AP 0.5:0.95 L']
# ap_metrics = coco_eval.stats[:6]
# eval_metrics = {l: m for l, m in zip(ap_labels, ap_metrics)}
# Precision and IOU
# bbox
precision_at_k, overall_iou, mean_iou = calculate_bbox_precision_at_k_and_iou_metrics(coco_gt, coco_pred)
eval_metrics.update({f'bbox P@{k}': m for k, m in zip([0.5, 0.6, 0.7, 0.8, 0.9], precision_at_k)})
eval_metrics.update({'bbox overall_iou': overall_iou, 'bbox mean_iou': mean_iou})
# mask
precision_at_k, overall_iou, mean_iou = calculate_precision_at_k_and_iou_metrics(coco_gt, coco_pred)
eval_metrics.update({f'segm P@{k}': m for k, m in zip([0.5, 0.6, 0.7, 0.8, 0.9], precision_at_k)})
eval_metrics.update({'segm overall_iou': overall_iou, 'segm mean_iou': mean_iou})
print(eval_metrics)
stats.update(eval_metrics)
return stats
@torch.no_grad()
def evaluate_a2d(model, data_loader, postprocessor, device, args):
model.eval()
predictions = []
metric_logger = utils.MetricLogger(delimiter=" ")
header = 'Test:'
for samples, targets in metric_logger.log_every(data_loader, 10, header):
image_ids = [t['image_id'] for t in targets]
samples = samples.to(device)
captions = [t["caption"] for t in targets]
targets = utils.targets_to(targets, device)
outputs = model(samples, captions, targets)
orig_target_sizes = torch.stack([t["orig_size"] for t in targets], dim=0)
target_sizes = torch.stack([t["size"] for t in targets], dim=0)
processed_outputs = postprocessor(outputs, orig_target_sizes, target_sizes)
for p, image_id in zip(processed_outputs, image_ids):
for s, m in zip(p['scores'], p['rle_masks']):
predictions.append({'image_id': image_id,
'category_id': 1, # dummy label, as categories are not predicted in ref-vos
'segmentation': m,
'score': s.item()})
# gather and merge predictions from all gpus
gathered_pred_lists = utils.all_gather(predictions)
predictions = [p for p_list in gathered_pred_lists for p in p_list]
# evaluation
eval_metrics = {}
if utils.is_main_process():
if args.dataset_file == 'a2d':
coco_gt = COCO(os.path.join(args.a2d_path, 'a2d_sentences_test_annotations_in_coco_format.json'))
elif args.dataset_file == 'jhmdb':
coco_gt = COCO(os.path.join(args.jhmdb_path, 'jhmdb_sentences_gt_annotations_in_coco_format.json'))
else:
raise NotImplementedError
coco_pred = coco_gt.loadRes(predictions)
coco_eval = COCOeval(coco_gt, coco_pred, iouType='segm')
coco_eval.params.useCats = 0 # ignore categories as they are not predicted in ref-vos task
coco_eval.evaluate()
coco_eval.accumulate()
coco_eval.summarize()
ap_labels = ['mAP 0.5:0.95', 'AP 0.5', 'AP 0.75', 'AP 0.5:0.95 S', 'AP 0.5:0.95 M', 'AP 0.5:0.95 L']
ap_metrics = coco_eval.stats[:6]
eval_metrics = {l: m for l, m in zip(ap_labels, ap_metrics)}
# Precision and IOU
precision_at_k, overall_iou, mean_iou = calculate_precision_at_k_and_iou_metrics(coco_gt, coco_pred)
eval_metrics.update({f'P@{k}': m for k, m in zip([0.5, 0.6, 0.7, 0.8, 0.9], precision_at_k)})
eval_metrics.update({'overall_iou': overall_iou, 'mean_iou': mean_iou})
print(eval_metrics)
# sync all processes before starting a new epoch or exiting
dist.barrier()
return eval_metrics
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