engine_track.py

# 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


def train_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module,
                    data_loader: Iterable, optimizer: torch.optim.Optimizer,
                    device: torch.device, scaler: torch.cuda.amp.GradScaler,
                    epoch: int, max_norm: float = 0, fp16=False):
    model.train()
    criterion.train()
    tensor_type = torch.cuda.HalfTensor if fp16 else torch.cuda.FloatTensor
    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):
        samples.tensors = samples.tensors.type(tensor_type)
        samples.mask = samples.mask.type(tensor_type)

        with torch.cuda.amp.autocast(enabled=fp16):
            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()
        scaler.scale(losses).backward()
        scaler.unscale_(optimizer)
        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)
        scaler.step(optimizer)
        scaler.update()
        
        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, fp16=False):
    tensor_type = torch.cuda.HalfTensor if fp16 else torch.cuda.FloatTensor
    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)
        samples.tensors = samples.tensors.type(tensor_type)
        samples.mask = samples.mask.type(tensor_type)

        targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
        
        with torch.cuda.amp.autocast(enabled=fp16):
            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