Some model parameters or buffers are not found in the checkpoint

## Issue
When I try to load the model, it says that
```
Some model parameters or buffers are not found in the checkpoint:
backbone.fpn_lateral2.{bias, weight}
backbone.fpn_lateral3.{bias, weight}
.
.
.
roi_heads.mask_head.mask_fcn4.{bias, weight}
roi_heads.mask_head.predictor.{bias, weight}
The checkpoint state_dict contains keys that are not used by the model:
fc1000.{bias, weight}
stem.conv1.bias
```

## Instructions To Reproduce the Issue:
### Installation
I created conda env with torch v1.10.0 and CUDA 11.3
`conda install pytorch==1.10.0 torchvision==0.11.0 torchaudio==0.10.0 cudatoolkit=11.3 -c pytorch -c conda-forge`

I installed detectronv0.6 from the instruction [here](https://detectron2.readthedocs.io/en/latest/tutorials/install.html)
`python -m pip install detectron2 -f \
https://dl.fbaipublicfiles.com/detectron2/wheels/cu113/torch1.10/index.html`

### Code for training
#### Dataset and config
This part works fine
```
TRAIN_IMAGES = "../../train/images"
TRAIN_JSON = "../../train/annotations/train.json"
VAL_IMAGES = "../../val/images"
VAL_JSON = "../../val/annotations/val.json"

register_coco_instances("train", {}, TRAIN_JSON, TRAIN_IMAGES)
register_coco_instances("val", {}, VAL_JSON, VAL_IMAGES)

cfg = get_cfg()
cfg.merge_from_file(r"detectron2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml")
cfg.DATASETS.TRAIN = ("train",)
cfg.DATASETS.TEST = ("val",)
cfg.OUTPUT_DIR = "./output1"
os.makedirs(cfg.OUTPUT_DIR, exist_ok=True)
```
#### Training
```
trainer = DefaultTrainer(cfg)
trainer.resume_or_load(resume=False)
```
The above part throws warning like this
```
Some model parameters or buffers are not found in the checkpoint:
backbone.fpn_lateral2.{bias, weight}
backbone.fpn_lateral3.{bias, weight}
backbone.fpn_lateral4.{bias, weight}
backbone.fpn_lateral5.{bias, weight}
backbone.fpn_output2.{bias, weight}
backbone.fpn_output3.{bias, weight}
backbone.fpn_output4.{bias, weight}
backbone.fpn_output5.{bias, weight}
proposal_generator.rpn_head.anchor_deltas.{bias, weight}
proposal_generator.rpn_head.conv.{bias, weight}
proposal_generator.rpn_head.objectness_logits.{bias, weight}
roi_heads.box_head.fc1.{bias, weight}
roi_heads.box_head.fc2.{bias, weight}
roi_heads.box_predictor.bbox_pred.{bias, weight}
roi_heads.box_predictor.cls_score.{bias, weight}
roi_heads.mask_head.deconv.{bias, weight}
roi_heads.mask_head.mask_fcn1.{bias, weight}
roi_heads.mask_head.mask_fcn2.{bias, weight}
roi_heads.mask_head.mask_fcn3.{bias, weight}
roi_heads.mask_head.mask_fcn4.{bias, weight}
roi_heads.mask_head.predictor.{bias, weight}
The checkpoint state_dict contains keys that are not used by the model:
fc1000.{bias, weight}
stem.conv1.bias
```

## Full log
```
True
[12/20 11:21:47 d2.engine.defaults]: Model:
GeneralizedRCNN(
(backbone): FPN(
(fpn_lateral2): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
(fpn_output2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(fpn_lateral3): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
(fpn_output3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(fpn_lateral4): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))
(fpn_output4): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(fpn_lateral5): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1))
(fpn_output5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(top_block): LastLevelMaxPool()
(bottom_up): ResNet(
(stem): BasicStem(
(conv1): Conv2d(
3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False
(norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
)
)
(res2): Sequential(
(0): BottleneckBlock(
(shortcut): Conv2d(
64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
)
(conv1): Conv2d(
64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
)
(conv2): Conv2d(
64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
)
(conv3): Conv2d(
64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
)
)
(1): BottleneckBlock(
(conv1): Conv2d(
256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
)
(conv2): Conv2d(
64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
)
(conv3): Conv2d(
64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
)
)
(2): BottleneckBlock(
(conv1): Conv2d(
256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
)
(conv2): Conv2d(
64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
)
(conv3): Conv2d(
64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
)
)
)
(res3): Sequential(
(0): BottleneckBlock(
(shortcut): Conv2d(
256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False
(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
)
(conv1): Conv2d(
256, 128, kernel_size=(1, 1), stride=(2, 2), bias=False
(norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
)
(conv2): Conv2d(
128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
)
(conv3): Conv2d(
128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
)
)
(1): BottleneckBlock(
(conv1): Conv2d(
512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
)
(conv2): Conv2d(
128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
)
(conv3): Conv2d(
128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
)
)
(2): BottleneckBlock(
(conv1): Conv2d(
512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
)
(conv2): Conv2d(
128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
)
(conv3): Conv2d(
128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
)
)
(3): BottleneckBlock(
(conv1): Conv2d(
512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
)
(conv2): Conv2d(
128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
)
(conv3): Conv2d(
128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
)
)
)
(res4): Sequential(
(0): BottleneckBlock(
(shortcut): Conv2d(
512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False
(norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
)
(conv1): Conv2d(
512, 256, kernel_size=(1, 1), stride=(2, 2), bias=False
(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
)
(conv2): Conv2d(
256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
)
(conv3): Conv2d(
256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
)
)
(1): BottleneckBlock(
(conv1): Conv2d(
1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
)
(conv2): Conv2d(
256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
)
(conv3): Conv2d(
256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
)
)
(2): BottleneckBlock(
(conv1): Conv2d(
1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
)
(conv2): Conv2d(
256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
)
(conv3): Conv2d(
256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
)
)
(3): BottleneckBlock(
(conv1): Conv2d(
1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
)
(conv2): Conv2d(
256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
)
(conv3): Conv2d(
256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
)
)
(4): BottleneckBlock(
(conv1): Conv2d(
1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
)
(conv2): Conv2d(
256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
)
(conv3): Conv2d(
256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
)
)
(5): BottleneckBlock(
(conv1): Conv2d(
1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
)
(conv2): Conv2d(
256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
)
(conv3): Conv2d(
256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
)
)
)
(res5): Sequential(
(0): BottleneckBlock(
(shortcut): Conv2d(
1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False
(norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)
)
(conv1): Conv2d(
1024, 512, kernel_size=(1, 1), stride=(2, 2), bias=False
(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
)
(conv2): Conv2d(
512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
)
(conv3): Conv2d(
512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)
)
)
(1): BottleneckBlock(
(conv1): Conv2d(
2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
)
(conv2): Conv2d(
512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
)
(conv3): Conv2d(
512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)
)
)
(2): BottleneckBlock(
(conv1): Conv2d(
2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
)
(conv2): Conv2d(
512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
)
(conv3): Conv2d(
512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False
(norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)
)
)
)
)
)
(proposal_generator): RPN(
(rpn_head): StandardRPNHead(
(conv): Conv2d(
256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
(activation): ReLU()
)
(objectness_logits): Conv2d(256, 3, kernel_size=(1, 1), stride=(1, 1))
(anchor_deltas): Conv2d(256, 12, kernel_size=(1, 1), stride=(1, 1))
)
(anchor_generator): DefaultAnchorGenerator(
(cell_anchors): BufferList()
)
)
(roi_heads): StandardROIHeads(
(box_pooler): ROIPooler(
(level_poolers): ModuleList(
(0): ROIAlign(output_size=(7, 7), spatial_scale=0.25, sampling_ratio=0, aligned=True)
(1): ROIAlign(output_size=(7, 7), spatial_scale=0.125, sampling_ratio=0, aligned=True)
(2): ROIAlign(output_size=(7, 7), spatial_scale=0.0625, sampling_ratio=0, aligned=True)
(3): ROIAlign(output_size=(7, 7), spatial_scale=0.03125, sampling_ratio=0, aligned=True)
)
)
(box_head): FastRCNNConvFCHead(
(flatten): Flatten(start_dim=1, end_dim=-1)
(fc1): Linear(in_features=12544, out_features=1024, bias=True)
(fc_relu1): ReLU()
(fc2): Linear(in_features=1024, out_features=1024, bias=True)
(fc_relu2): ReLU()
)
(box_predictor): FastRCNNOutputLayers(
(cls_score): Linear(in_features=1024, out_features=42, bias=True)
(bbox_pred): Linear(in_features=1024, out_features=164, bias=True)
)
(mask_pooler): ROIPooler(
(level_poolers): ModuleList(
(0): ROIAlign(output_size=(14, 14), spatial_scale=0.25, sampling_ratio=0, aligned=True)
(1): ROIAlign(output_size=(14, 14), spatial_scale=0.125, sampling_ratio=0, aligned=True)
(2): ROIAlign(output_size=(14, 14), spatial_scale=0.0625, sampling_ratio=0, aligned=True)
(3): ROIAlign(output_size=(14, 14), spatial_scale=0.03125, sampling_ratio=0, aligned=True)
)
)
(mask_head): MaskRCNNConvUpsampleHead(
(mask_fcn1): Conv2d(
256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
(activation): ReLU()
)
(mask_fcn2): Conv2d(
256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
(activation): ReLU()
)
(mask_fcn3): Conv2d(
256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
(activation): ReLU()
)
(mask_fcn4): Conv2d(
256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
(activation): ReLU()
)
(deconv): ConvTranspose2d(256, 256, kernel_size=(2, 2), stride=(2, 2))
(deconv_relu): ReLU()
(predictor): Conv2d(256, 41, kernel_size=(1, 1), stride=(1, 1))
)
)
)
[12/20 11:21:47 d2.data.datasets.coco]: Loaded 906 images in COCO format from ../../rcshv1.1/train/annotations/train.json
[12/20 11:21:47 d2.data.build]: Removed 0 images with no usable annotations. 906 images left.
[12/20 11:21:47 d2.data.build]: Distribution of instances among all 41 categories:
| category | #instances | category | #instances | category | #instances |
|:-------------:|:-------------|:-------------:|:-------------|:-------------:|:-------------|
| ligne_mixte | 117 | dents_de_re.. | 37 | cedez_passage | 26 |
| passage_pie.. | 443 | zebra | 61 | zigzag | 108 |
| fleche_droit | 160 | fleche_droi.. | 62 | fleche_droi.. | 36 |
| fleche_droite | 21 | fleche_raba.. | 0 | fleche_raba.. | 7 |
| damier | 29 | ligne_feux | 210 | ligne_sens_.. | 87 |
| panneau | 69 | pic_pmr | 45 | pic_velo | 620 |
| txt_autre | 36 | txt_bus | 32 | txt_ecole | 11 |
| txt_livraison | 51 | txt_payant | 186 | fleche_gauche | 93 |
| ligne_stop | 21 | txt_velo | 5 | ligne_autre | 151 |
| sas_velo | 0 | piste_cycla.. | 0 | ligne_conti.. | 244 |
| ligne_conti.. | 106 | ligne_disco.. | 3072 | limite_30 | 61 |
| limite_50 | 0 | pic_voiture.. | 18 | txt_name_of.. | 0 |
| txt_zone_30 | 19 | velo_chevron | 147 | autre | 172 |
| fleche_autre | 0 | limite_stat.. | 2207 | | |
| total | 8770 | | | | |
[12/20 11:21:47 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in training: [ResizeShortestEdge(short_edge_length=(640, 672, 704, 736, 768, 800), max_size=1333, sample_style='choice'), RandomFlip()]
[12/20 11:21:47 d2.data.build]: Using training sampler TrainingSampler
[12/20 11:21:47 d2.data.common]: Serializing 906 elements to byte tensors and concatenating them all ...
[12/20 11:21:47 d2.data.common]: Serialized dataset takes 1.71 MiB
[12/20 11:21:47 d2.checkpoint.c2_model_loading]: Renaming Caffe2 weights ......
[12/20 11:21:47 d2.checkpoint.c2_model_loading]: Following weights matched with submodule backbone.bottom_up:
| Names in Model | Names in Checkpoint | Shapes |
|:------------------|:-------------------------|:------------------------------------------------|
| res2.0.conv1.* | res2_0_branch2a_{bn_*,w} | (64,) (64,) (64,) (64,) (64,64,1,1) |
| res2.0.conv2.* | res2_0_branch2b_{bn_*,w} | (64,) (64,) (64,) (64,) (64,64,3,3) |
| res2.0.conv3.* | res2_0_branch2c_{bn_*,w} | (256,) (256,) (256,) (256,) (256,64,1,1) |
| res2.0.shortcut.* | res2_0_branch1_{bn_*,w} | (256,) (256,) (256,) (256,) (256,64,1,1) |
| res2.1.conv1.* | res2_1_branch2a_{bn_*,w} | (64,) (64,) (64,) (64,) (64,256,1,1) |
| res2.1.conv2.* | res2_1_branch2b_{bn_*,w} | (64,) (64,) (64,) (64,) (64,64,3,3) |
| res2.1.conv3.* | res2_1_branch2c_{bn_*,w} | (256,) (256,) (256,) (256,) (256,64,1,1) |
| res2.2.conv1.* | res2_2_branch2a_{bn_*,w} | (64,) (64,) (64,) (64,) (64,256,1,1) |
| res2.2.conv2.* | res2_2_branch2b_{bn_*,w} | (64,) (64,) (64,) (64,) (64,64,3,3) |
| res2.2.conv3.* | res2_2_branch2c_{bn_*,w} | (256,) (256,) (256,) (256,) (256,64,1,1) |
| res3.0.conv1.* | res3_0_branch2a_{bn_*,w} | (128,) (128,) (128,) (128,) (128,256,1,1) |
| res3.0.conv2.* | res3_0_branch2b_{bn_*,w} | (128,) (128,) (128,) (128,) (128,128,3,3) |
| res3.0.conv3.* | res3_0_branch2c_{bn_*,w} | (512,) (512,) (512,) (512,) (512,128,1,1) |
| res3.0.shortcut.* | res3_0_branch1_{bn_*,w} | (512,) (512,) (512,) (512,) (512,256,1,1) |
| res3.1.conv1.* | res3_1_branch2a_{bn_*,w} | (128,) (128,) (128,) (128,) (128,512,1,1) |
| res3.1.conv2.* | res3_1_branch2b_{bn_*,w} | (128,) (128,) (128,) (128,) (128,128,3,3) |
| res3.1.conv3.* | res3_1_branch2c_{bn_*,w} | (512,) (512,) (512,) (512,) (512,128,1,1) |
| res3.2.conv1.* | res3_2_branch2a_{bn_*,w} | (128,) (128,) (128,) (128,) (128,512,1,1) |
| res3.2.conv2.* | res3_2_branch2b_{bn_*,w} | (128,) (128,) (128,) (128,) (128,128,3,3) |
| res3.2.conv3.* | res3_2_branch2c_{bn_*,w} | (512,) (512,) (512,) (512,) (512,128,1,1) |
| res3.3.conv1.* | res3_3_branch2a_{bn_*,w} | (128,) (128,) (128,) (128,) (128,512,1,1) |
| res3.3.conv2.* | res3_3_branch2b_{bn_*,w} | (128,) (128,) (128,) (128,) (128,128,3,3) |
| res3.3.conv3.* | res3_3_branch2c_{bn_*,w} | (512,) (512,) (512,) (512,) (512,128,1,1) |
| res4.0.conv1.* | res4_0_branch2a_{bn_*,w} | (256,) (256,) (256,) (256,) (256,512,1,1) |
| res4.0.conv2.* | res4_0_branch2b_{bn_*,w} | (256,) (256,) (256,) (256,) (256,256,3,3) |
| res4.0.conv3.* | res4_0_branch2c_{bn_*,w} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |
| res4.0.shortcut.* | res4_0_branch1_{bn_*,w} | (1024,) (1024,) (1024,) (1024,) (1024,512,1,1) |
| res4.1.conv1.* | res4_1_branch2a_{bn_*,w} | (256,) (256,) (256,) (256,) (256,1024,1,1) |
| res4.1.conv2.* | res4_1_branch2b_{bn_*,w} | (256,) (256,) (256,) (256,) (256,256,3,3) |
| res4.1.conv3.* | res4_1_branch2c_{bn_*,w} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |
| res4.2.conv1.* | res4_2_branch2a_{bn_*,w} | (256,) (256,) (256,) (256,) (256,1024,1,1) |
| res4.2.conv2.* | res4_2_branch2b_{bn_*,w} | (256,) (256,) (256,) (256,) (256,256,3,3) |
| res4.2.conv3.* | res4_2_branch2c_{bn_*,w} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |
| res4.3.conv1.* | res4_3_branch2a_{bn_*,w} | (256,) (256,) (256,) (256,) (256,1024,1,1) |
| res4.3.conv2.* | res4_3_branch2b_{bn_*,w} | (256,) (256,) (256,) (256,) (256,256,3,3) |
| res4.3.conv3.* | res4_3_branch2c_{bn_*,w} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |
| res4.4.conv1.* | res4_4_branch2a_{bn_*,w} | (256,) (256,) (256,) (256,) (256,1024,1,1) |
| res4.4.conv2.* | res4_4_branch2b_{bn_*,w} | (256,) (256,) (256,) (256,) (256,256,3,3) |
| res4.4.conv3.* | res4_4_branch2c_{bn_*,w} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |
| res4.5.conv1.* | res4_5_branch2a_{bn_*,w} | (256,) (256,) (256,) (256,) (256,1024,1,1) |
| res4.5.conv2.* | res4_5_branch2b_{bn_*,w} | (256,) (256,) (256,) (256,) (256,256,3,3) |
| res4.5.conv3.* | res4_5_branch2c_{bn_*,w} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |
| res5.0.conv1.* | res5_0_branch2a_{bn_*,w} | (512,) (512,) (512,) (512,) (512,1024,1,1) |
| res5.0.conv2.* | res5_0_branch2b_{bn_*,w} | (512,) (512,) (512,) (512,) (512,512,3,3) |
| res5.0.conv3.* | res5_0_branch2c_{bn_*,w} | (2048,) (2048,) (2048,) (2048,) (2048,512,1,1) |
| res5.0.shortcut.* | res5_0_branch1_{bn_*,w} | (2048,) (2048,) (2048,) (2048,) (2048,1024,1,1) |
| res5.1.conv1.* | res5_1_branch2a_{bn_*,w} | (512,) (512,) (512,) (512,) (512,2048,1,1) |
| res5.1.conv2.* | res5_1_branch2b_{bn_*,w} | (512,) (512,) (512,) (512,) (512,512,3,3) |
| res5.1.conv3.* | res5_1_branch2c_{bn_*,w} | (2048,) (2048,) (2048,) (2048,) (2048,512,1,1) |
| res5.2.conv1.* | res5_2_branch2a_{bn_*,w} | (512,) (512,) (512,) (512,) (512,2048,1,1) |
| res5.2.conv2.* | res5_2_branch2b_{bn_*,w} | (512,) (512,) (512,) (512,) (512,512,3,3) |
| res5.2.conv3.* | res5_2_branch2c_{bn_*,w} | (2048,) (2048,) (2048,) (2048,) (2048,512,1,1) |
| stem.conv1.norm.* | res_conv1_bn_* | (64,) (64,) (64,) (64,) |
| stem.conv1.weight | conv1_w | (64, 3, 7, 7) |
Some model parameters or buffers are not found in the checkpoint:
backbone.fpn_lateral2.{bias, weight}
backbone.fpn_lateral3.{bias, weight}
backbone.fpn_lateral4.{bias, weight}
backbone.fpn_lateral5.{bias, weight}
backbone.fpn_output2.{bias, weight}
backbone.fpn_output3.{bias, weight}
backbone.fpn_output4.{bias, weight}
backbone.fpn_output5.{bias, weight}
proposal_generator.rpn_head.anchor_deltas.{bias, weight}
proposal_generator.rpn_head.conv.{bias, weight}
proposal_generator.rpn_head.objectness_logits.{bias, weight}
roi_heads.box_head.fc1.{bias, weight}
roi_heads.box_head.fc2.{bias, weight}
roi_heads.box_predictor.bbox_pred.{bias, weight}
roi_heads.box_predictor.cls_score.{bias, weight}
roi_heads.mask_head.deconv.{bias, weight}
roi_heads.mask_head.mask_fcn1.{bias, weight}
roi_heads.mask_head.mask_fcn2.{bias, weight}
roi_heads.mask_head.mask_fcn3.{bias, weight}
roi_heads.mask_head.mask_fcn4.{bias, weight}
roi_heads.mask_head.predictor.{bias, weight}
The checkpoint state_dict contains keys that are not used by the model:
fc1000.{bias, weight}
stem.conv1.bias
```

## Your Environment
absl-py==2.1.0
antlr4-python3-runtime==4.9.3
appdirs==1.4.4
asttokens @ file:///home/conda/feedstock_root/build_artifacts/asttokens_1733175639022/work
attrs==24.3.0
backcall @ file:///home/conda/feedstock_root/build_artifacts/backcall_1592338393461/work
beautifulsoup4==4.12.3
black==21.4b2
bleach==6.1.0
cachetools==5.5.0
certifi==2024.12.14
charset-normalizer==3.4.0
click==8.1.7
cloudpickle==3.1.0
comm @ file:///home/conda/feedstock_root/build_artifacts/comm_1710320294760/work
contourpy==1.1.1
cycler==0.12.1
debugpy @ file:///home/conda/feedstock_root/build_artifacts/debugpy_1722923746907/work
decorator @ file:///home/conda/feedstock_root/build_artifacts/decorator_1641555617451/work
defusedxml==0.7.1
detectron2==0.6+cu113
docopt==0.6.2
executing @ file:///home/conda/feedstock_root/build_artifacts/executing_1725214404607/work
fastjsonschema==2.21.1
fonttools==4.55.3
future==1.0.0
fvcore==0.1.5.post20221221
google-auth==2.37.0
google-auth-oauthlib==1.0.0
grpcio==1.68.1
hydra-core==1.3.2
idna==3.10
importlib_metadata @ file:///home/conda/feedstock_root/build_artifacts/importlib-metadata_1726082825846/work
importlib_resources==6.4.5
iopath==0.1.9
ipykernel @ file:///home/conda/feedstock_root/build_artifacts/ipykernel_1719845459717/work
ipython==8.12.3
jedi @ file:///home/conda/feedstock_root/build_artifacts/jedi_1696326070614/work
Jinja2==3.1.4
jsonschema==4.23.0
jsonschema-specifications==2023.12.1
jupyter_client @ file:///home/conda/feedstock_root/build_artifacts/jupyter_client_1726610684920/work
jupyter_core @ file:///home/conda/feedstock_root/build_artifacts/jupyter_core_1727163409502/work
jupyterlab_pygments==0.3.0
kiwisolver==1.4.7
Markdown==3.7
MarkupSafe==2.1.5
matplotlib==3.7.5
matplotlib-inline @ file:///home/conda/feedstock_root/build_artifacts/matplotlib-inline_1713250518406/work
mistune==3.0.2
mypy-extensions==1.0.0
nbclient==0.10.1
nbconvert==7.16.4
nbformat==5.10.4
nest_asyncio @ file:///home/conda/feedstock_root/build_artifacts/nest-asyncio_1705850609492/work
numpy==1.23.1
oauthlib==3.2.2
omegaconf==2.3.0
opencv-python==4.10.0.84
packaging @ file:///home/conda/feedstock_root/build_artifacts/packaging_1733203243479/work
pandocfilters==1.5.1
parso @ file:///home/conda/feedstock_root/build_artifacts/parso_1712320355065/work
pathspec==0.12.1
pexpect @ file:///home/conda/feedstock_root/build_artifacts/pexpect_1706113125309/work
pickleshare @ file:///home/conda/feedstock_root/build_artifacts/pickleshare_1602536217715/work
Pillow @ file:///home/conda/feedstock_root/build_artifacts/pillow_1675487152289/work
pipreqs==0.5.0
pkgutil_resolve_name==1.3.10
platformdirs @ file:///home/conda/feedstock_root/build_artifacts/platformdirs_1726613481435/work
portalocker==3.0.0
prompt_toolkit @ file:///home/conda/feedstock_root/build_artifacts/prompt-toolkit_1727341649933/work
protobuf==5.29.2
psutil @ file:///home/conda/feedstock_root/build_artifacts/psutil_1719274595110/work
ptyprocess @ file:///home/conda/feedstock_root/build_artifacts/ptyprocess_1609419310487/work/dist/ptyprocess-0.7.0-py2.py3-none-any.whl
pure_eval @ file:///home/conda/feedstock_root/build_artifacts/pure_eval_1721585709575/work
pyasn1==0.6.1
pyasn1_modules==0.4.1
pycocotools==2.0.7
pydot==3.0.3
Pygments @ file:///home/conda/feedstock_root/build_artifacts/pygments_1714846767233/work
pyparsing==3.1.4
python-dateutil @ file:///home/conda/feedstock_root/build_artifacts/python-dateutil_1709299778482/work
PyYAML==6.0.2
pyzmq @ file:///home/conda/feedstock_root/build_artifacts/pyzmq_1724399083222/work
referencing==0.35.1
regex==2024.11.6
requests==2.32.3
requests-oauthlib==2.0.0
rpds-py==0.20.1
rsa==4.9
six @ file:///home/conda/feedstock_root/build_artifacts/six_1620240208055/work
soupsieve==2.6
stack-data @ file:///home/conda/feedstock_root/build_artifacts/stack_data_1669632077133/work
tabulate==0.9.0
tensorboard==2.14.0
tensorboard-data-server==0.7.2
termcolor==2.4.0
tinycss2==1.4.0
toml==0.10.2
torch==1.10.0
torchaudio==0.10.0
torchvision==0.11.0
tornado @ file:///home/conda/feedstock_root/build_artifacts/tornado_1717722826518/work
tqdm==4.67.1
traitlets @ file:///home/conda/feedstock_root/build_artifacts/traitlets_1713535121073/work
typing_extensions @ file:///home/conda/feedstock_root/build_artifacts/typing_extensions_1717802530399/work
urllib3==2.2.3
wcwidth @ file:///home/conda/feedstock_root/build_artifacts/wcwidth_1704731205417/work
webencodings==0.5.1
Werkzeug==3.0.6
yacs==0.1.8
yarg==0.1.9
zipp==3.20.2

Start with DefaultTrainer.resume_or_load(resume=False) and the mask_rcnn_R_50_FPN_1x.yaml configuration used in the reproduction. Trace how the checkpoint state_dict is matched to the printed GeneralizedRCNN model, then verify whether the reported missing and unused keys indicate an actual loading failure; done means the warning's cause and expected resolution are established.

Written by the indexing model from the issue text.