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engine.py
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engine.py
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# ------------------------------------------------------------------------
# Train and eval functions used in main.py
# ------------------------------------------------------------------------
# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# ------------------------------------------------------------------------
import math
import os
import sys
from typing import Iterable
import cv2
import numpy as np
import json
import copy
import torch
import util.misc as utils
from util.misc import NestedTensor
from datasets.coco_eval import CocoEvaluator
from datasets.panoptic_eval import PanopticEvaluator
from datasets.data_prefetcher import data_prefetcher
from PIL import Image, ImageDraw
from scipy.optimize import linear_sum_assignment
cv2.setNumThreads(0)
cv2.ocl.setUseOpenCL(False)
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('grad_norm', utils.SmoothedValue(window_size=1, fmt='{value:.2f}'))
header = 'Epoch: [{}]'.format(epoch)
print_freq = 4000
prefetcher = data_prefetcher(data_loader, device, prefetch=True)
samples, targets = prefetcher.next()
for _ in metric_logger.log_every(range(len(data_loader)), print_freq, header):
outputs, loss_dict = model(samples, targets, criterion, train=True)
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(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()
torch.cuda.empty_cache()
# 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):
num_frames = args.num_frames
eval_types = args.eval_types
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:'
coco_iou_types = [k for k in ['bbox', 'segm'] if k in postprocessors.keys()]
coco_evaluator = None
if 'coco' in eval_types:
coco_evaluator = CocoEvaluator(base_ds['coco'], coco_iou_types)
# coco_evaluator.coco_eval[iou_types[0]].params.iouThrs = [0, 0.1, 0.5, 0.75]
for samples, targets in metric_logger.log_every(data_loader, 1000, header):
samples = samples.to(device)
all_outputs, loss_dict = model(samples, targets, criterion, train=False)
#### reduce losses over all GPUs for logging purposes ####
targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
weight_dict = criterion.weight_dict
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'])
#### reduce losses over all GPUs for logging purposes ####
##### single clip input ######
if all_outputs['pred_boxes'].dim() == 3:
all_outputs['pred_boxes'] = all_outputs['pred_boxes'].unsqueeze(2)
orig_target_sizes = torch.stack([t["orig_size"] for t in targets], dim=0)
results = [{} for i in range(len(targets))]
if 'bbox' in postprocessors.keys():
results = postprocessors['bbox'](all_outputs, orig_target_sizes, num_frames=num_frames)
# scores: [num_ins]
# labels: [num_ins]
# boxes: [num_ins, num_frames, 4]
if 'segm' in postprocessors.keys():
target_sizes = torch.stack([t["size"] for t in targets], dim=0)
results = postprocessors['segm'](results, all_outputs, orig_target_sizes, target_sizes)
res_img = {}
# evaluate results
if 'coco' in eval_types:
for target, output in zip(targets, results):
for fid in range(num_frames):
res_img[target['image_id'][fid].item()] = {}
for k, v in output.items():
if k == 'masks':
res_img[target['image_id'][fid].item()][k] = v[:,fid].unsqueeze(1)
elif k == 'boxes':
res_img[target['image_id'][fid].item()][k] = v[:,fid]
else:
res_img[target['image_id'][fid].item()][k] = v
if coco_evaluator is not None:
coco_evaluator.update(res_img)
# 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()
if 'segm' in postprocessors.keys():
stats['coco_eval_masks'] = coco_evaluator.coco_eval['segm'].stats.tolist()
return stats, coco_evaluator