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"""
Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
https://github.com/facebookresearch/detr/blob/main/engine.py
by lyuwenyu
"""
import math
import os
import sys
import pathlib
from typing import Iterable
import torch
import torch.amp
from src.data import CocoEvaluator
from src.misc import (MetricLogger, SmoothedValue, reduce_dict)
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, **kwargs):
model.train()
criterion.train()
metric_logger = MetricLogger(delimiter=" ")
metric_logger.add_meter('lr', SmoothedValue(window_size=1, fmt='{value:.6f}'))
# metric_logger.add_meter('class_error', SmoothedValue(window_size=1, fmt='{value:.2f}'))
header = 'Epoch: [{}]'.format(epoch)
print_freq = kwargs.get('print_freq', 10)
ema = kwargs.get('ema', None)
scaler = kwargs.get('scaler', None)
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]
if scaler is not None:
with torch.autocast(device_type=str(device), cache_enabled=True):
outputs = model(samples, targets)
with torch.autocast(device_type=str(device), enabled=False):
loss_dict = criterion(outputs, targets)
loss = sum(loss_dict.values())
scaler.scale(loss).backward()
if max_norm > 0:
scaler.unscale_(optimizer)
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm)
scaler.step(optimizer)
scaler.update()
optimizer.zero_grad()
else:
outputs = model(samples, targets)
loss_dict = criterion(outputs, targets)
loss = sum(loss_dict.values())
optimizer.zero_grad()
loss.backward()
if max_norm > 0:
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm)
optimizer.step()
# ema
if ema is not None:
ema.update(model)
loss_dict_reduced = reduce_dict(loss_dict)
loss_value = sum(loss_dict_reduced.values())
if not math.isfinite(loss_value):
print("Loss is {}, stopping training".format(loss_value))
print(loss_dict_reduced)
sys.exit(1)
metric_logger.update(loss=loss_value, **loss_dict_reduced)
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: torch.nn.Module, criterion: torch.nn.Module, postprocessors, data_loader, base_ds, device, output_dir):
model.eval()
criterion.eval()
metric_logger = MetricLogger(delimiter=" ")
# metric_logger.add_meter('class_error', SmoothedValue(window_size=1, fmt='{value:.2f}'))
header = 'Test:'
# iou_types = tuple(k for k in ('segm', 'bbox') if k in postprocessors.keys())
iou_types = postprocessors.iou_types
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"),
# )
for samples, targets in metric_logger.log_every(data_loader, 10, header):
samples = samples.to(device)
targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
# with torch.autocast(device_type=str(device)):
# outputs = model(samples)
outputs = model(samples)
print(outputs)
# loss_dict = criterion(outputs, targets)
# weight_dict = criterion.weight_dict
# # reduce losses over all GPUs for logging purposes
# loss_dict_reduced = 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(outputs, orig_target_sizes)
# results = postprocessors(outputs, targets)
# 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)}
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 = {}
# stats = {k: meter.global_avg for k, meter in metric_logger.meters.items()}
if coco_evaluator is not None:
if 'bbox' in iou_types:
stats['coco_eval_bbox'] = coco_evaluator.coco_eval['bbox'].stats.tolist()
if 'segm' in iou_types:
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
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