update checkpoint infos to compatible with detectron2

.gitignore

@@ -1,3 +1,8 @@
+# Custome
+.vscode
+datasets/
+out-of-date/
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]

README.md

@@ -26,7 +26,7 @@ This provides a convenient way to initialize backbone in detectron2.
 3. Run: `python train_net_builtin.py --num-gpus <gpu number> --config-file configs/<your config file>`. For example: `sh scripts/train_net_builtin.sh`
 
 
-- Trained with pytorch formal imagenet trainer
+- Trained with pytorch formal imagenet trainer [**Recommend**]
 
 1. Read carefully with some arguments in `train_net.py`
 2. Run: `sh /scripts/train_net.sh`

imgcls/modeling/meta_arch/clsnet.py

@@ -40,7 +40,7 @@ def __init__(self, cfg):
 
         self.num_classes = cfg.MODEL.CLSNET.NUM_CLASSES
         self.in_features = cfg.MODEL.CLSNET.IN_FEATURES
-        self.backbone = build_backbone(cfg)
+        self.bottom_up = build_backbone(cfg)
         self.criterion = nn.CrossEntropyLoss()
 
         self.register_buffer("pixel_mean", torch.Tensor(cfg.MODEL.PIXEL_MEAN).view(-1, 1, 1))
@@ -63,7 +63,7 @@ def forward_d2(self, batched_inputs):
         images = self.preprocess_image(batched_inputs)
         gt_labels = [x['label'] for x in batched_inputs]
         gt_labels = torch.as_tensor(gt_labels, dtype=torch.long).to(self.device)
-        features = self.backbone(images.tensor)
+        features = self.bottom_up(images.tensor)
         features = [features[f] for f in self.in_features]
 
         if self.training:
@@ -79,7 +79,7 @@ def forward_d2(self, batched_inputs):
             return processed_results
 
     def forward(self, images):
-        features = self.backbone(images)
+        features = self.bottom_up(images)
         return features["linear"]
 
 

scripts/train_net.sh

@@ -8,4 +8,4 @@
  ###
 
 
-CUDA_VISIBLE_DEVICES=4,5,6,7 python train_net.py --config-file configs/Base_image_cls.yaml --batch-size 2048 --dist-url 'tcp://127.0.0.1:51151' --dist-backend 'nccl' --multiprocessing-distributed --world-size 1 --rank 0 datasets/ImageNet2012
+CUDA_VISIBLE_DEVICES=4,5,6,7 python train_net.py --config-file configs/Base_image_cls.yaml --batch-size 1024 --dist-url 'tcp://127.0.0.1:51151' --dist-backend 'nccl' --multiprocessing-distributed --world-size 1 --rank 0 datasets/ImageNet2012

train_net.py

@@ -212,7 +212,7 @@ def main_worker(gpu, ngpus_per_node, args, cfg):
             if args.gpu is not None:
                 # best_acc1 may be from a checkpoint from a different GPU
                 best_acc1 = best_acc1.to(args.gpu)
-            model.load_state_dict(checkpoint['state_dict'])
+            model.load_state_dict(checkpoint['model'])
             optimizer.load_state_dict(checkpoint['optimizer'])
             print("=> loaded checkpoint '{}' (epoch {})"
                   .format(args.resume, checkpoint['epoch']))
@@ -278,9 +278,11 @@ def main_worker(gpu, ngpus_per_node, args, cfg):
 
         if not args.multiprocessing_distributed or (args.multiprocessing_distributed
                                                     and args.rank % ngpus_per_node == 0):
+
             save_checkpoint({
                 'epoch': epoch + 1,
-                'state_dict': model.state_dict(),
+                'model': model.state_dict(),
+                'matching_heuristics': True,
                 'best_acc1': best_acc1,
                 'optimizer': optimizer.state_dict(),
             }, is_best)
@@ -381,7 +383,16 @@ def validate(val_loader, model, criterion, args):
 def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
     torch.save(state, filename)
     if is_best:
-        shutil.copyfile(filename, 'model_best.pth.tar')
+        # strip 'module.'
+        from collections import OrderedDict
+        p = OrderedDict()
+        for key, value in state['model'].items():
+            if key.startswith('module.'):
+                key = key[7:]
+            p[key] = value
+        state['model'] = p
+        torch.save(state, "model_best.pth.tar")
+        # shutil.copyfile(filename, 'model_best.pth.tar')
 
 
 class AverageMeter(object):