FEAT：copy all DETR detection code

DETR.py

@@ -1,13 +1,17 @@
 import argparse
-import imp
+from fileinput import filename
+from functools import total_ordering
+import json
 from tabnanny import check
-import typing
+import time
 import torch
+import datetime
 from pathlib import Path
 from torch.utils.data import DataLoader
+from TAU.engine import train_one_epoch
 from TAU.model.detr import build
 from TAU.dataset.coco import build_coco, get_coco_api_from_dataset
-from TAU.utils.TAUUtils import collate_fn
+from TAU.utils.TAUUtils import collate_fn, evaluate, is_main_process, save_on_master
 
 def get_args_parser():
     parser = argparse.ArgumentParser("set transformer detector", add_help=False)
@@ -70,6 +74,65 @@ def main(args):
         args.start_epoch = checkpoint["epoch"] + 1
 
     if args.eval:
-        test_stats, coco_evaluator = evaluate 
-
+        test_stats, coco_evaluator = evaluate(model, criterion, postprocessors, data_loader_train, base_ds, device, args.output_dir)
+        if args.output_dir:
+            save_on_master(coco_evaluator.coco_eval["bbox"].eval, output_dir / "eval.pth")
+        return
+
+    print("Start training")
+    start_time = time.time()
+    for epoch in range(args.start_epoch, args.epochs):
+        if args.distributed:
+            sampler_train.set_epoch(epoch)
+        train_stats = train_one_epoch(
+            model, criterion, data_loader_train, optimizer, device, epoch, args.clip_max_norm
+        )
+        lr_scheduler.step()
+        if args.output_dir:
+            checkpoint_paths = [output_dir / "checkpoint.pth"]
+
+            if (epoch + 1) % args.lr_drop == 0 or (epoch + 1) % 100 == 0:
+                checkpoint_paths.append(output_dir / f"checkpoint{epoch:04}.pth")
+
+            for checkpoint_path in checkpoint_paths:
+                save_on_master({
+                    "model": model_without_ddp.state_dict(),
+                    "optimizer": optimizer.state_dict(),
+                    "lr_scheduler": lr_scheduler.state_dict(),
+                    "epoch": epoch,
+                    "args": args
+                }, checkpoint_path)
+
+        test_stats, coco_evaluator = evaluate(
+            model, criterion, postprocessors, data_loader_val, base_ds, device, args.output_dir
+        )
+        log_stats = {
+            **{f"train_{k}": v for k, v in train_stats.items()},
+            **{f"test_{k}": v for k, v in test_stats.items()},
+            "epoch": epoch,
+            "n_parameters": n_parameters
+        }
 
+        if args.output_dir and is_main_process():
+            with (output_dir / "log.txt").open("a") as f:
+                f.write(json.dump(log_stats) + "\n")
+
+            if coco_evaluator is not None:
+                (output_dir / "eval").mkdir(exist_ok=True)
+                if "bbox" in coco_evaluator.coco_eval:
+                    filenames = ["latest.pth"]
+                    if epoch % 50 == 0:
+                        filenames.append(f"{epoch:03}.pth")
+                    for name in filenames:
+                        torch.save(coco_evaluator.coco_eval["bbox"].eval, output_dir / "eval" / name)
+
+    total_time = time.time() - start_time
+    total_time_str = str(datetime.timedelta(seconds=int(total_time)))
+    print("Training time {}".format(total_time_str))
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser("DETR training and evaluation script", parents=[get_args_parser()])
+    args = parser.parse_args()
+    if args.output_dir:
+        Path(args.output_dir).mkdir(parents=True, exist_ok=True)
+    main(args)

TAU/engine.py

@@ -1,5 +1,6 @@
 import math
 import sys
+from unittest import result
 from cv2 import reduce
 from numpy import flatnonzero
 import torch
@@ -82,7 +83,7 @@ def evalute(model, criterion, postprocessors, data_loader, base_ds, device, outp
 
         orig_target_sizes = torch.stack([t["orig_size"] for t in targets], dim=0)
         results = postprocessors["bbox"](outputs, orig_target_sizes)
-        res = {targets["image_id"].item(): output for target, output in zip(targets, outputs)}
+        res = {targets["image_id"].item(): output for target, output in zip(targets, results)}
         if coco_evaluator is not None:
             coco_evaluator.update(res)
 

TAU/utils/TAUUtils.py

@@ -569,3 +569,6 @@ def convert_to_xywh(boxes):
     xmin, ymin, xmax, ymax = boxes.unbind(1)
     return torch.stack((xmin, ymin, xmax - xmin, ymax - ymin), dim=1)
 
+def save_on_master(*args, **kwargs):
+    if is_main_process():
+        torch.save(*args, **kwargs)